Search Results for author: Qi Dou

Found 117 papers, 62 papers with code

Structured Gradient-based Interpretations via Norm-Regularized Adversarial Training

1 code implementation6 Apr 2024 Shizhan Gong, Qi Dou, Farzan Farnia

We show a duality relation between the regularized norms of the adversarial perturbations and gradient-based maps, based on which we design adversarial training loss functions promoting sparsity and group-sparsity properties in simple gradient maps.

EndoGSLAM: Real-Time Dense Reconstruction and Tracking in Endoscopic Surgeries using Gaussian Splatting

no code implementations22 Mar 2024 Kailing Wang, Chen Yang, Yuehao Wang, Sikuang Li, Yan Wang, Qi Dou, Xiaokang Yang, Wei Shen

Precise camera tracking, high-fidelity 3D tissue reconstruction, and real-time online visualization are critical for intrabody medical imaging devices such as endoscopes and capsule robots.

Simultaneous Localization and Mapping

Efficient Transferability Assessment for Selection of Pre-trained Detectors

no code implementations14 Mar 2024 Zhao Wang, Aoxue Li, Zhenguo Li, Qi Dou

Given this zoo, we adopt 7 target datasets from 5 diverse domains as the downstream target tasks for evaluation.

Open-Vocabulary Object Detection with Meta Prompt Representation and Instance Contrastive Optimization

no code implementations14 Mar 2024 Zhao Wang, Aoxue Li, Fengwei Zhou, Zhenguo Li, Qi Dou

Without using knowledge distillation, ensemble model or extra training data during detector training, our proposed MIC outperforms previous SOTA methods trained with these complex techniques on LVIS.

Contrastive Learning Knowledge Distillation +2

OpenMEDLab: An Open-source Platform for Multi-modality Foundation Models in Medicine

no code implementations28 Feb 2024 Xiaosong Wang, Xiaofan Zhang, Guotai Wang, Junjun He, Zhongyu Li, Wentao Zhu, Yi Guo, Qi Dou, Xiaoxiao Li, Dequan Wang, Liang Hong, Qicheng Lao, Tong Ruan, Yukun Zhou, Yixue Li, Jie Zhao, Kang Li, Xin Sun, Lifeng Zhu, Shaoting Zhang

The emerging trend of advancing generalist artificial intelligence, such as GPTv4 and Gemini, has reshaped the landscape of research (academia and industry) in machine learning and many other research areas.

Transfer Learning

CustomVideo: Customizing Text-to-Video Generation with Multiple Subjects

no code implementations18 Jan 2024 Zhao Wang, Aoxue Li, Enze Xie, Lingting Zhu, Yong Guo, Qi Dou, Zhenguo Li

Customized text-to-video generation aims to generate high-quality videos guided by text prompts and subject references.

Object Text-to-Video Generation +1

Any-point Trajectory Modeling for Policy Learning

no code implementations28 Dec 2023 Chuan Wen, Xingyu Lin, John So, Kai Chen, Qi Dou, Yang Gao, Pieter Abbeel

Learning from demonstration is a powerful method for teaching robots new skills, and having more demonstration data often improves policy learning.

Trajectory Modeling Transfer Learning

Efficient Deformable Tissue Reconstruction via Orthogonal Neural Plane

2 code implementations23 Dec 2023 Chen Yang, Kailing Wang, Yuehao Wang, Qi Dou, Xiaokang Yang, Wei Shen

Intraoperative imaging techniques for reconstructing deformable tissues in vivo are pivotal for advanced surgical systems.

SeamlessNeRF: Stitching Part NeRFs with Gradient Propagation

no code implementations30 Oct 2023 Bingchen Gong, Yuehao Wang, Xiaoguang Han, Qi Dou

To fill this gap, we propose SeamlessNeRF, a novel approach for seamless appearance blending of multiple NeRFs.

Interactive Navigation in Environments with Traversable Obstacles Using Large Language and Vision-Language Models

no code implementations13 Oct 2023 Zhen Zhang, Anran Lin, Chun Wai Wong, Xiangyu Chu, Qi Dou, K. W. Samuel Au

This paper proposes an interactive navigation framework by using large language and vision-language models, allowing robots to navigate in environments with traversable obstacles.

Language Modelling Large Language Model +1

ArSDM: Colonoscopy Images Synthesis with Adaptive Refinement Semantic Diffusion Models

1 code implementation3 Sep 2023 Yuhao Du, Yuncheng Jiang, Shuangyi Tan, Xusheng Wu, Qi Dou, Zhen Li, Guanbin Li, Xiang Wan

Colonoscopy analysis, particularly automatic polyp segmentation and detection, is essential for assisting clinical diagnosis and treatment.

Segmentation

Visual-Kinematics Graph Learning for Procedure-agnostic Instrument Tip Segmentation in Robotic Surgeries

no code implementations2 Sep 2023 Jiaqi Liu, Yonghao Long, Kai Chen, Cheuk Hei Leung, Zerui Wang, Qi Dou

However, this task is very challenging due to the small sizes of surgical instrument tips, and significant variance of surgical scenes across different procedures.

Graph Learning Segmentation

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

no code implementations11 Aug 2023 Karim Lekadir, Aasa Feragen, Abdul Joseph Fofanah, Alejandro F Frangi, Alena Buyx, Anais Emelie, Andrea Lara, Antonio R Porras, An-Wen Chan, Arcadi Navarro, Ben Glocker, Benard O Botwe, Bishesh Khanal, Brigit Beger, Carol C Wu, Celia Cintas, Curtis P Langlotz, Daniel Rueckert, Deogratias Mzurikwao, Dimitrios I Fotiadis, Doszhan Zhussupov, Enzo Ferrante, Erik Meijering, Eva Weicken, Fabio A González, Folkert W Asselbergs, Fred Prior, Gabriel P Krestin, Gary Collins, Geletaw S Tegenaw, Georgios Kaissis, Gianluca Misuraca, Gianna Tsakou, Girish Dwivedi, Haridimos Kondylakis, Harsha Jayakody, Henry C Woodruf, Hugo JWL Aerts, Ian Walsh, Ioanna Chouvarda, Irène Buvat, Islem Rekik, James Duncan, Jayashree Kalpathy-Cramer, Jihad Zahir, Jinah Park, John Mongan, Judy W Gichoya, Julia A Schnabel, Kaisar Kushibar, Katrine Riklund, Kensaku MORI, Kostas Marias, Lameck M Amugongo, Lauren A Fromont, Lena Maier-Hein, Leonor Cerdá Alberich, Leticia Rittner, Lighton Phiri, Linda Marrakchi-Kacem, Lluís Donoso-Bach, Luis Martí-Bonmatí, M Jorge Cardoso, Maciej Bobowicz, Mahsa Shabani, Manolis Tsiknakis, Maria A Zuluaga, Maria Bielikova, Marie-Christine Fritzsche, Marius George Linguraru, Markus Wenzel, Marleen de Bruijne, Martin G Tolsgaard, Marzyeh Ghassemi, Md Ashrafuzzaman, Melanie Goisauf, Mohammad Yaqub, Mohammed Ammar, Mónica Cano Abadía, Mukhtar M E Mahmoud, Mustafa Elattar, Nicola Rieke, Nikolaos Papanikolaou, Noussair Lazrak, Oliver Díaz, Olivier Salvado, Oriol Pujol, Ousmane Sall, Pamela Guevara, Peter Gordebeke, Philippe Lambin, Pieta Brown, Purang Abolmaesumi, Qi Dou, Qinghua Lu, Richard Osuala, Rose Nakasi, S Kevin Zhou, Sandy Napel, Sara Colantonio, Shadi Albarqouni, Smriti Joshi, Stacy Carter, Stefan Klein, Steffen E Petersen, Susanna Aussó, Suyash Awate, Tammy Riklin Raviv, Tessa Cook, Tinashe E M Mutsvangwa, Wendy A Rogers, Wiro J Niessen, Xènia Puig-Bosch, Yi Zeng, Yunusa G Mohammed, Yves Saint James Aquino, Zohaib Salahuddin, Martijn P A Starmans

This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare.

Fairness

Deep Fusion Transformer Network with Weighted Vector-Wise Keypoints Voting for Robust 6D Object Pose Estimation

1 code implementation ICCV 2023 Jun Zhou, Kai Chen, Linlin Xu, Qi Dou, Jing Qin

One critical challenge in 6D object pose estimation from a single RGBD image is efficient integration of two different modalities, i. e., color and depth.

6D Pose Estimation using RGB Semantic Similarity +1

Value-Informed Skill Chaining for Policy Learning of Long-Horizon Tasks with Surgical Robot

1 code implementation31 Jul 2023 Tao Huang, Kai Chen, Wang Wei, Jianan Li, Yonghao Long, Qi Dou

Based on this value function, a chaining policy is learned to instruct subtask policies to terminate at the state with the highest value so that all subsequent policies are more likely to be connected for accomplishing the task.

reinforcement-learning

Client-Level Differential Privacy via Adaptive Intermediary in Federated Medical Imaging

1 code implementation24 Jul 2023 Meirui Jiang, Yuan Zhong, Anjie Le, Xiaoxiao Li, Qi Dou

In this paper, we propose to optimize the trade-off under the context of client-level DP, which focuses on privacy during communications.

Federated Learning

Treatment Outcome Prediction for Intracerebral Hemorrhage via Generative Prognostic Model with Imaging and Tabular Data

1 code implementation24 Jul 2023 Wenao Ma, Cheng Chen, Jill Abrigo, Calvin Hoi-Kwan Mak, Yuqi Gong, Nga Yan Chan, Chu Han, Zaiyi Liu, Qi Dou

Specifically, we propose to employ a variational autoencoder model to generate a low-dimensional prognostic score, which can effectively address the selection bias resulting from the non-randomized controlled trials.

Selection bias

FedSoup: Improving Generalization and Personalization in Federated Learning via Selective Model Interpolation

1 code implementation20 Jul 2023 Minghui Chen, Meirui Jiang, Qi Dou, Zehua Wang, Xiaoxiao Li

In this paper, we propose a novel federated model soup method (i. e., selective interpolation of model parameters) to optimize the trade-off between local and global performance.

Federated Learning Image Classification +1

Foundation Model for Endoscopy Video Analysis via Large-scale Self-supervised Pre-train

1 code implementation29 Jun 2023 Zhao Wang, Chang Liu, Shaoting Zhang, Qi Dou

Foundation models have exhibited remarkable success in various applications, such as disease diagnosis and text report generation.

Segmentation Transfer Learning

3DSAM-adapter: Holistic Adaptation of SAM from 2D to 3D for Promptable Medical Image Segmentation

1 code implementation23 Jun 2023 Shizhan Gong, Yuan Zhong, Wenao Ma, Jinpeng Li, Zhao Wang, Jingyang Zhang, Pheng-Ann Heng, Qi Dou

Notably, the original SAM architecture is designed for 2D natural images, therefore would not be able to extract the 3D spatial information from volumetric medical data effectively.

Image Segmentation Medical Image Segmentation +2

Joint Optimization of Class-Specific Training- and Test-Time Data Augmentation in Segmentation

1 code implementation30 May 2023 Zeju Li, Konstantinos Kamnitsas, Qi Dou, Chen Qin, Ben Glocker

We demonstrate the effectiveness of our method on four medical image segmentation tasks across different scenarios with two state-of-the-art segmentation models, DeepMedic and nnU-Net.

Data Augmentation Image Segmentation +4

Why is the winner the best?

no code implementations CVPR 2023 Matthias Eisenmann, Annika Reinke, Vivienn Weru, Minu Dietlinde Tizabi, Fabian Isensee, Tim J. Adler, Sharib Ali, Vincent Andrearczyk, Marc Aubreville, Ujjwal Baid, Spyridon Bakas, Niranjan Balu, Sophia Bano, Jorge Bernal, Sebastian Bodenstedt, Alessandro Casella, Veronika Cheplygina, Marie Daum, Marleen de Bruijne, Adrien Depeursinge, Reuben Dorent, Jan Egger, David G. Ellis, Sandy Engelhardt, Melanie Ganz, Noha Ghatwary, Gabriel Girard, Patrick Godau, Anubha Gupta, Lasse Hansen, Kanako Harada, Mattias Heinrich, Nicholas Heller, Alessa Hering, Arnaud Huaulmé, Pierre Jannin, Ali Emre Kavur, Oldřich Kodym, Michal Kozubek, Jianning Li, Hongwei Li, Jun Ma, Carlos Martín-Isla, Bjoern Menze, Alison Noble, Valentin Oreiller, Nicolas Padoy, Sarthak Pati, Kelly Payette, Tim Rädsch, Jonathan Rafael-Patiño, Vivek Singh Bawa, Stefanie Speidel, Carole H. Sudre, Kimberlin Van Wijnen, Martin Wagner, Donglai Wei, Amine Yamlahi, Moi Hoon Yap, Chun Yuan, Maximilian Zenk, Aneeq Zia, David Zimmerer, Dogu Baran Aydogan, Binod Bhattarai, Louise Bloch, Raphael Brüngel, Jihoon Cho, Chanyeol Choi, Qi Dou, Ivan Ezhov, Christoph M. Friedrich, Clifton Fuller, Rebati Raman Gaire, Adrian Galdran, Álvaro García Faura, Maria Grammatikopoulou, SeulGi Hong, Mostafa Jahanifar, Ikbeom Jang, Abdolrahim Kadkhodamohammadi, Inha Kang, Florian Kofler, Satoshi Kondo, Hugo Kuijf, Mingxing Li, Minh Huan Luu, Tomaž Martinčič, Pedro Morais, Mohamed A. Naser, Bruno Oliveira, David Owen, Subeen Pang, Jinah Park, Sung-Hong Park, Szymon Płotka, Elodie Puybareau, Nasir Rajpoot, Kanghyun Ryu, Numan Saeed, Adam Shephard, Pengcheng Shi, Dejan Štepec, Ronast Subedi, Guillaume Tochon, Helena R. Torres, Helene Urien, João L. Vilaça, Kareem Abdul Wahid, Haojie Wang, Jiacheng Wang, Liansheng Wang, Xiyue Wang, Benedikt Wiestler, Marek Wodzinski, Fangfang Xia, Juanying Xie, Zhiwei Xiong, Sen yang, Yanwu Yang, Zixuan Zhao, Klaus Maier-Hein, Paul F. Jäger, Annette Kopp-Schneider, Lena Maier-Hein

The "typical" lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning.

Benchmarking Multi-Task Learning

Fair Federated Medical Image Segmentation via Client Contribution Estimation

no code implementations CVPR 2023 Meirui Jiang, Holger R Roth, Wenqi Li, Dong Yang, Can Zhao, Vishwesh Nath, Daguang Xu, Qi Dou, Ziyue Xu

Recent studies have investigated how to reward clients based on their contribution (collaboration fairness), and how to achieve uniformity of performance across clients (performance fairness).

Fairness Federated Learning +3

Adaptive Negative Evidential Deep Learning for Open-set Semi-supervised Learning

no code implementations21 Mar 2023 Yang Yu, Danruo Deng, Furui Liu, Yueming Jin, Qi Dou, Guangyong Chen, Pheng-Ann Heng

Open-set semi-supervised learning (Open-set SSL) considers a more practical scenario, where unlabeled data and test data contain new categories (outliers) not observed in labeled data (inliers).

Outlier Detection

FedDBL: Communication and Data Efficient Federated Deep-Broad Learning for Histopathological Tissue Classification

1 code implementation24 Feb 2023 Tianpeng Deng, Yanqi Huang, Guoqiang Han, Zhenwei Shi, Jiatai Lin, Qi Dou, Zaiyi Liu, Xiao-jing Guo, C. L. Philip Chen, Chu Han

In this paper, we propose a universal and lightweight federated learning framework, named Federated Deep-Broad Learning (FedDBL), to achieve superior classification performance with limited training samples and only one-round communication.

Federated Learning

RecolorNeRF: Layer Decomposed Radiance Fields for Efficient Color Editing of 3D Scenes

no code implementations19 Jan 2023 Bingchen Gong, Yuehao Wang, Xiaoguang Han, Qi Dou

We present RecolorNeRF, a novel user-friendly color editing approach for the neural radiance fields.

Color Manipulation

Human-in-the-loop Embodied Intelligence with Interactive Simulation Environment for Surgical Robot Learning

1 code implementation1 Jan 2023 Yonghao Long, Wang Wei, Tao Huang, Yuehao Wang, Qi Dou

We showcase the improvement of our simulation environment with the designed new features, and validate effectiveness of incorporating human factors in embodied intelligence through the use of human demonstrations and reinforcement learning as a representative example.

Diffusion Model based Semi-supervised Learning on Brain Hemorrhage Images for Efficient Midline Shift Quantification

no code implementations1 Jan 2023 Shizhan Gong, Cheng Chen, Yuqi Gong, Nga Yan Chan, Wenao Ma, Calvin Hoi-Kwan Mak, Jill Abrigo, Qi Dou

Brain midline shift (MLS) is one of the most critical factors to be considered for clinical diagnosis and treatment decision-making for intracranial hemorrhage.

Decision Making Representation Learning

StereoPose: Category-Level 6D Transparent Object Pose Estimation from Stereo Images via Back-View NOCS

no code implementations3 Nov 2022 Kai Chen, Stephen James, Congying Sui, Yun-hui Liu, Pieter Abbeel, Qi Dou

To further improve the performance of the stereo framework, StereoPose is equipped with a parallax attention module for stereo feature fusion and an epipolar loss for improving the stereo-view consistency of network predictions.

Object Pose Estimation +1

Federated Domain Generalization for Image Recognition via Cross-Client Style Transfer

1 code implementation3 Oct 2022 Junming Chen, Meirui Jiang, Qi Dou, Qifeng Chen

Our style representation is exceptionally lightweight and can hardly be used for the reconstruction of the dataset.

Domain Generalization Federated Learning +1

AutoLaparo: A New Dataset of Integrated Multi-tasks for Image-guided Surgical Automation in Laparoscopic Hysterectomy

no code implementations3 Aug 2022 Ziyi Wang, Bo Lu, Yonghao Long, Fangxun Zhong, Tak-Hong Cheung, Qi Dou, Yunhui Liu

In addition, we provide experimental results with state-of-the-art models as reference benchmarks for further model developments and evaluations on this dataset.

Anatomy motion prediction +2

Pseudo-label Guided Cross-video Pixel Contrast for Robotic Surgical Scene Segmentation with Limited Annotations

no code implementations20 Jul 2022 Yang Yu, Zixu Zhao, Yueming Jin, Guangyong Chen, Qi Dou, Pheng-Ann Heng

Concretely, for trusty representation learning, we propose to incorporate pseudo labels to instruct the pair selection, obtaining more reliable representation pairs for pixel contrast.

Pseudo Label Representation Learning +2

Test-time Adaptation with Calibration of Medical Image Classification Nets for Label Distribution Shift

1 code implementation2 Jul 2022 Wenao Ma, Cheng Chen, Shuang Zheng, Jing Qin, Huimao Zhang, Qi Dou

In this paper, we propose the first method to tackle label shift for medical image classification, which effectively adapt the model learned from a single training label distribution to arbitrary unknown test label distribution.

Image Classification Medical Diagnosis +3

Neural Rendering for Stereo 3D Reconstruction of Deformable Tissues in Robotic Surgery

1 code implementation30 Jun 2022 Yuehao Wang, Yonghao Long, Siu Hin Fan, Qi Dou

Reconstruction of the soft tissues in robotic surgery from endoscopic stereo videos is important for many applications such as intra-operative navigation and image-guided robotic surgery automation.

3D Reconstruction Neural Rendering

Dynamic Bank Learning for Semi-supervised Federated Image Diagnosis with Class Imbalance

1 code implementation27 Jun 2022 Meirui Jiang, Hongzheng Yang, Xiaoxiao Li, Quande Liu, Pheng-Ann Heng, Qi Dou

Despite recent progress on semi-supervised federated learning (FL) for medical image diagnosis, the problem of imbalanced class distributions among unlabeled clients is still unsolved for real-world use.

Federated Learning

DLTTA: Dynamic Learning Rate for Test-time Adaptation on Cross-domain Medical Images

1 code implementation27 May 2022 Hongzheng Yang, Cheng Chen, Meirui Jiang, Quande Liu, Jianfeng Cao, Pheng Ann Heng, Qi Dou

Based on this estimated discrepancy, a dynamic learning rate adjustment strategy is then developed to achieve a suitable degree of adaptation for each test sample.

Histopathological Image Classification Image Classification +2

Robust Medical Image Classification from Noisy Labeled Data with Global and Local Representation Guided Co-training

no code implementations10 May 2022 Cheng Xue, Lequan Yu, Pengfei Chen, Qi Dou, Pheng-Ann Heng

In this paper, we propose a novel collaborative training paradigm with global and local representation learning for robust medical image classification from noisy-labeled data to combat the lack of high quality annotated medical data.

Image Classification Medical Image Classification +1

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

1 code implementation22 Apr 2022 Sarthak Pati, Ujjwal Baid, Brandon Edwards, Micah Sheller, Shih-han Wang, G Anthony Reina, Patrick Foley, Alexey Gruzdev, Deepthi Karkada, Christos Davatzikos, Chiharu Sako, Satyam Ghodasara, Michel Bilello, Suyash Mohan, Philipp Vollmuth, Gianluca Brugnara, Chandrakanth J Preetha, Felix Sahm, Klaus Maier-Hein, Maximilian Zenk, Martin Bendszus, Wolfgang Wick, Evan Calabrese, Jeffrey Rudie, Javier Villanueva-Meyer, Soonmee Cha, Madhura Ingalhalikar, Manali Jadhav, Umang Pandey, Jitender Saini, John Garrett, Matthew Larson, Robert Jeraj, Stuart Currie, Russell Frood, Kavi Fatania, Raymond Y Huang, Ken Chang, Carmen Balana, Jaume Capellades, Josep Puig, Johannes Trenkler, Josef Pichler, Georg Necker, Andreas Haunschmidt, Stephan Meckel, Gaurav Shukla, Spencer Liem, Gregory S Alexander, Joseph Lombardo, Joshua D Palmer, Adam E Flanders, Adam P Dicker, Haris I Sair, Craig K Jones, Archana Venkataraman, Meirui Jiang, Tiffany Y So, Cheng Chen, Pheng Ann Heng, Qi Dou, Michal Kozubek, Filip Lux, Jan Michálek, Petr Matula, Miloš Keřkovský, Tereza Kopřivová, Marek Dostál, Václav Vybíhal, Michael A Vogelbaum, J Ross Mitchell, Joaquim Farinhas, Joseph A Maldjian, Chandan Ganesh Bangalore Yogananda, Marco C Pinho, Divya Reddy, James Holcomb, Benjamin C Wagner, Benjamin M Ellingson, Timothy F Cloughesy, Catalina Raymond, Talia Oughourlian, Akifumi Hagiwara, Chencai Wang, Minh-Son To, Sargam Bhardwaj, Chee Chong, Marc Agzarian, Alexandre Xavier Falcão, Samuel B Martins, Bernardo C A Teixeira, Flávia Sprenger, David Menotti, Diego R Lucio, Pamela Lamontagne, Daniel Marcus, Benedikt Wiestler, Florian Kofler, Ivan Ezhov, Marie Metz, Rajan Jain, Matthew Lee, Yvonne W Lui, Richard McKinley, Johannes Slotboom, Piotr Radojewski, Raphael Meier, Roland Wiest, Derrick Murcia, Eric Fu, Rourke Haas, John Thompson, David Ryan Ormond, Chaitra Badve, Andrew E Sloan, Vachan Vadmal, Kristin Waite, Rivka R Colen, Linmin Pei, Murat AK, Ashok Srinivasan, J Rajiv Bapuraj, Arvind Rao, Nicholas Wang, Ota Yoshiaki, Toshio Moritani, Sevcan Turk, Joonsang Lee, Snehal Prabhudesai, Fanny Morón, Jacob Mandel, Konstantinos Kamnitsas, Ben Glocker, Luke V M Dixon, Matthew Williams, Peter Zampakis, Vasileios Panagiotopoulos, Panagiotis Tsiganos, Sotiris Alexiou, Ilias Haliassos, Evangelia I Zacharaki, Konstantinos Moustakas, Christina Kalogeropoulou, Dimitrios M Kardamakis, Yoon Seong Choi, Seung-Koo Lee, Jong Hee Chang, Sung Soo Ahn, Bing Luo, Laila Poisson, Ning Wen, Pallavi Tiwari, Ruchika Verma, Rohan Bareja, Ipsa Yadav, Jonathan Chen, Neeraj Kumar, Marion Smits, Sebastian R van der Voort, Ahmed Alafandi, Fatih Incekara, Maarten MJ Wijnenga, Georgios Kapsas, Renske Gahrmann, Joost W Schouten, Hendrikus J Dubbink, Arnaud JPE Vincent, Martin J van den Bent, Pim J French, Stefan Klein, Yading Yuan, Sonam Sharma, Tzu-Chi Tseng, Saba Adabi, Simone P Niclou, Olivier Keunen, Ann-Christin Hau, Martin Vallières, David Fortin, Martin Lepage, Bennett Landman, Karthik Ramadass, Kaiwen Xu, Silky Chotai, Lola B Chambless, Akshitkumar Mistry, Reid C Thompson, Yuriy Gusev, Krithika Bhuvaneshwar, Anousheh Sayah, Camelia Bencheqroun, Anas Belouali, Subha Madhavan, Thomas C Booth, Alysha Chelliah, Marc Modat, Haris Shuaib, Carmen Dragos, Aly Abayazeed, Kenneth Kolodziej, Michael Hill, Ahmed Abbassy, Shady Gamal, Mahmoud Mekhaimar, Mohamed Qayati, Mauricio Reyes, Ji Eun Park, Jihye Yun, Ho Sung Kim, Abhishek Mahajan, Mark Muzi, Sean Benson, Regina G H Beets-Tan, Jonas Teuwen, Alejandro Herrera-Trujillo, Maria Trujillo, William Escobar, Ana Abello, Jose Bernal, Jhon Gómez, Joseph Choi, Stephen Baek, Yusung Kim, Heba Ismael, Bryan Allen, John M Buatti, Aikaterini Kotrotsou, Hongwei Li, Tobias Weiss, Michael Weller, Andrea Bink, Bertrand Pouymayou, Hassan F Shaykh, Joel Saltz, Prateek Prasanna, Sampurna Shrestha, Kartik M Mani, David Payne, Tahsin Kurc, Enrique Pelaez, Heydy Franco-Maldonado, Francis Loayza, Sebastian Quevedo, Pamela Guevara, Esteban Torche, Cristobal Mendoza, Franco Vera, Elvis Ríos, Eduardo López, Sergio A Velastin, Godwin Ogbole, Dotun Oyekunle, Olubunmi Odafe-Oyibotha, Babatunde Osobu, Mustapha Shu'aibu, Adeleye Dorcas, Mayowa Soneye, Farouk Dako, Amber L Simpson, Mohammad Hamghalam, Jacob J Peoples, Ricky Hu, Anh Tran, Danielle Cutler, Fabio Y Moraes, Michael A Boss, James Gimpel, Deepak Kattil Veettil, Kendall Schmidt, Brian Bialecki, Sailaja Marella, Cynthia Price, Lisa Cimino, Charles Apgar, Prashant Shah, Bjoern Menze, Jill S Barnholtz-Sloan, Jason Martin, Spyridon Bakas

Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data.

Boundary Detection Federated Learning

IOP-FL: Inside-Outside Personalization for Federated Medical Image Segmentation

1 code implementation16 Apr 2022 Meirui Jiang, Hongzheng Yang, Chen Cheng, Qi Dou

Federated learning (FL) allows multiple medical institutions to collaboratively learn a global model without centralizing client data.

Federated Learning Image Segmentation +2

Sim-to-Real 6D Object Pose Estimation via Iterative Self-training for Robotic Bin Picking

no code implementations14 Apr 2022 Kai Chen, Rui Cao, Stephen James, Yichuan Li, Yun-hui Liu, Pieter Abbeel, Qi Dou

To continuously improve the quality of pseudo labels, we iterate the above steps by taking the trained student model as a new teacher and re-label real data using the refined teacher model.

6D Pose Estimation using RGB Robotic Grasping

Rapid model transfer for medical image segmentation via iterative human-in-the-loop update: from labelled public to unlabelled clinical datasets for multi-organ segmentation in CT

no code implementations13 Apr 2022 Wenao Ma, Shuang Zheng, Lei Zhang, Huimao Zhang, Qi Dou

Despite the remarkable success on medical image analysis with deep learning, it is still under exploration regarding how to rapidly transfer AI models from one dataset to another for clinical applications.

Image Segmentation Medical Image Segmentation +2

Robotic Surgery Remote Mentoring via AR with 3D Scene Streaming and Hand Interaction

no code implementations9 Apr 2022 Yonghao Long, Chengkun Li, Qi Dou

In this paper, we propose a novel AR-based robotic surgery remote mentoring system with efficient 3D scene visualization and natural 3D hand interaction.

Federated Learning from Only Unlabeled Data with Class-Conditional-Sharing Clients

1 code implementation7 Apr 2022 Nan Lu, Zhao Wang, Xiaoxiao Li, Gang Niu, Qi Dou, Masashi Sugiyama

We propose federation of unsupervised learning (FedUL), where the unlabeled data are transformed into surrogate labeled data for each of the clients, a modified model is trained by supervised FL, and the wanted model is recovered from the modified model.

Federated Learning

Transformer-empowered Multi-scale Contextual Matching and Aggregation for Multi-contrast MRI Super-resolution

1 code implementation CVPR 2022 Guangyuan Li, Jun Lv, Yapeng Tian, Qi Dou, Chengyan Wang, Chenliang Xu, Jing Qin

However, existing methods still have two shortcomings: (1) they neglect that the multi-contrast features at different scales contain different anatomical details and hence lack effective mechanisms to match and fuse these features for better reconstruction; and (2) they are still deficient in capturing long-range dependencies, which are essential for the regions with complicated anatomical structures.

Super-Resolution

Integrating Artificial Intelligence and Augmented Reality in Robotic Surgery: An Initial dVRK Study Using a Surgical Education Scenario

1 code implementation2 Jan 2022 Yonghao Long, Jianfeng Cao, Anton Deguet, Russell H. Taylor, Qi Dou

In this paper, we develop a novel system by seamlessly merging artificial intelligence module and augmented reality visualization to automatically generate the surgical guidance for robotic surgery education.

HarmoFL: Harmonizing Local and Global Drifts in Federated Learning on Heterogeneous Medical Images

1 code implementation20 Dec 2021 Meirui Jiang, ZiRui Wang, Qi Dou

Multiple medical institutions collaboratively training a model using federated learning (FL) has become a promising solution for maximizing the potential of data-driven models, yet the non-independent and identically distributed (non-iid) data in medical images is still an outstanding challenge in real-world practice.

Federated Learning Image Classification +1

UniFed: A Unified Framework for Federated Learning on Non-IID Image Features

no code implementations19 Oct 2021 Meirui Jiang, Xiaoxiao Li, Xiaofei Zhang, Michael Kamp, Qi Dou

In this work, we propose a unified framework to tackle the non-iid issues for internal and external clients together.

Domain Generalization Federated Learning +1

Stereo Dense Scene Reconstruction and Accurate Localization for Learning-Based Navigation of Laparoscope in Minimally Invasive Surgery

no code implementations8 Oct 2021 Ruofeng Wei, Bin Li, Hangjie Mo, Bo Lu, Yonghao Long, Bohan Yang, Qi Dou, Yunhui Liu, Dong Sun

Then, we develop a dense visual reconstruction algorithm to represent the scene by surfels, estimate the laparoscope poses and fuse the depth maps into a unified reference coordinate for tissue reconstruction.

Anatomy Depth Estimation

Unsupervised Federated Learning is Possible

no code implementations ICLR 2022 Nan Lu, Zhao Wang, Xiaoxiao Li, Gang Niu, Qi Dou, Masashi Sugiyama

We propose federation of unsupervised learning (FedUL), where the unlabeled data are transformed into surrogate labeled data for each of the clients, a modified model is trained by supervised FL, and the wanted model is recovered from the modified model.

Federated Learning

Source-Free Domain Adaptive Fundus Image Segmentation with Denoised Pseudo-Labeling

1 code implementation19 Sep 2021 Cheng Chen, Quande Liu, Yueming Jin, Qi Dou, Pheng-Ann Heng

We present a novel denoised pseudo-labeling method for this problem, which effectively makes use of the source model and unlabeled target data to promote model self-adaptation from pseudo labels.

Denoising Image Segmentation +2

SurRoL: An Open-source Reinforcement Learning Centered and dVRK Compatible Platform for Surgical Robot Learning

1 code implementation30 Aug 2021 Jiaqi Xu, Bin Li, Bo Lu, Yun-hui Liu, Qi Dou, Pheng-Ann Heng

Ten learning-based surgical tasks are built in the platform, which are common in the real autonomous surgical execution.

Reinforcement Learning (RL)

Category-Level 6D Object Pose Estimation via Cascaded Relation and Recurrent Reconstruction Networks

no code implementations19 Aug 2021 Jiaze Wang, Kai Chen, Qi Dou

Furthermore, we design a recurrent reconstruction network for iterative residual refinement to progressively improve the reconstruction and correspondence estimations from coarse to fine.

6D Pose Estimation 6D Pose Estimation using RGB +3

Accurate Grid Keypoint Learning for Efficient Video Prediction

1 code implementation28 Jul 2021 Xiaojie Gao, Yueming Jin, Qi Dou, Chi-Wing Fu, Pheng-Ann Heng

Video prediction methods generally consume substantial computing resources in training and deployment, among which keypoint-based approaches show promising improvement in efficiency by simplifying dense image prediction to light keypoint prediction.

Video Prediction

E-DSSR: Efficient Dynamic Surgical Scene Reconstruction with Transformer-based Stereoscopic Depth Perception

2 code implementations1 Jul 2021 Yonghao Long, Zhaoshuo Li, Chi Hang Yee, Chi Fai Ng, Russell H. Taylor, Mathias Unberath, Qi Dou

After that, a dynamic reconstruction algorithm which can estimate the tissue deformation and camera movement, and aggregate the information over time is proposed for surgical scene reconstruction.

Anatomy Depth Estimation +1

Federated Semi-supervised Medical Image Classification via Inter-client Relation Matching

1 code implementation16 Jun 2021 Quande Liu, Hongzheng Yang, Qi Dou, Pheng-Ann Heng

This paper studies a practical yet challenging FL problem, named \textit{Federated Semi-supervised Learning} (FSSL), which aims to learn a federated model by jointly utilizing the data from both labeled and unlabeled clients (i. e., hospitals).

Federated Learning Image Classification +2

Cascaded Robust Learning at Imperfect Labels for Chest X-ray Segmentation

no code implementations5 Apr 2021 Cheng Xue, Qiao Deng, Xiaomeng Li, Qi Dou, Pheng Ann Heng

To deal with the high inter-rater variability, the study of imperfect label has great significance in medical image segmentation tasks.

Image Segmentation Medical Image Segmentation +2

Temporal Memory Relation Network for Workflow Recognition from Surgical Video

1 code implementation30 Mar 2021 Yueming Jin, Yonghao Long, Cheng Chen, Zixu Zhao, Qi Dou, Pheng-Ann Heng

In this paper, we propose a novel end-to-end temporal memory relation network (TMRNet) for relating long-range and multi-scale temporal patterns to augment the present features.

Relation Relation Network

One to Many: Adaptive Instrument Segmentation via Meta Learning and Dynamic Online Adaptation in Robotic Surgical Video

no code implementations24 Mar 2021 Zixu Zhao, Yueming Jin, Bo Lu, Chi-Fai Ng, Qi Dou, Yun-hui Liu, Pheng-Ann Heng

To greatly increase the label efficiency, we explore a new problem, i. e., adaptive instrument segmentation, which is to effectively adapt one source model to new robotic surgical videos from multiple target domains, only given the annotated instruments in the first frame.

General Knowledge Meta-Learning

Future Frame Prediction for Robot-assisted Surgery

no code implementations18 Mar 2021 Xiaojie Gao, Yueming Jin, Zixu Zhao, Qi Dou, Pheng-Ann Heng

Predicting future frames for robotic surgical video is an interesting, important yet extremely challenging problem, given that the operative tasks may have complex dynamics.

Future prediction Optical Flow Estimation

Trans-SVNet: Accurate Phase Recognition from Surgical Videos via Hybrid Embedding Aggregation Transformer

1 code implementation17 Mar 2021 Xiaojie Gao, Yueming Jin, Yonghao Long, Qi Dou, Pheng-Ann Heng

In this paper, we introduce, for the first time in surgical workflow analysis, Transformer to reconsider the ignored complementary effects of spatial and temporal features for accurate surgical phase recognition.

Surgical phase recognition

Domain Adaptive Robotic Gesture Recognition with Unsupervised Kinematic-Visual Data Alignment

no code implementations6 Mar 2021 Xueying Shi, Yueming Jin, Qi Dou, Jing Qin, Pheng-Ann Heng

In this paper, we propose a novel unsupervised domain adaptation framework which can simultaneously transfer multi-modality knowledge, i. e., both kinematic and visual data, from simulator to real robot.

Gesture Recognition Surgical Gesture Recognition +1

FedBN: Federated Learning on Non-IID Features via Local Batch Normalization

4 code implementations ICLR 2021 Xiaoxiao Li, Meirui Jiang, Xiaofei Zhang, Michael Kamp, Qi Dou

The emerging paradigm of federated learning (FL) strives to enable collaborative training of deep models on the network edge without centrally aggregating raw data and hence improving data privacy.

Autonomous Driving Federated Learning

SGPA: Structure-Guided Prior Adaptation for Category-Level 6D Object Pose Estimation

no code implementations ICCV 2021 Kai Chen, Qi Dou

The prior adaptation intrinsically associates the adopted prior with different objects, from which we can accurately reconstruct the 3D canonical model of the specific object for pose estimation.

6D Pose Estimation using RGB Object

Contrastive Cross-site Learning with Redesigned Net for COVID-19 CT Classification

1 code implementation15 Sep 2020 Zhao Wang, Quande Liu, Qi Dou

The pandemic of coronavirus disease 2019 (COVID-19) has lead to a global public health crisis spreading hundreds of countries.

COVID-19 Diagnosis General Classification

Learning Motion Flows for Semi-supervised Instrument Segmentation from Robotic Surgical Video

1 code implementation6 Jul 2020 Zixu Zhao, Yueming Jin, Xiaojie Gao, Qi Dou, Pheng-Ann Heng

Considering the fast instrument motion, we further introduce a flow compensator to estimate intermediate motion within continuous frames, with a novel cycle learning strategy.

Segmentation

Multi-Site Infant Brain Segmentation Algorithms: The iSeg-2019 Challenge

no code implementations4 Jul 2020 Yue Sun, Kun Gao, Zhengwang Wu, Zhihao Lei, Ying WEI, Jun Ma, Xiaoping Yang, Xue Feng, Li Zhao, Trung Le Phan, Jitae Shin, Tao Zhong, Yu Zhang, Lequan Yu, Caizi Li, Ramesh Basnet, M. Omair Ahmad, M. N. S. Swamy, Wenao Ma, Qi Dou, Toan Duc Bui, Camilo Bermudez Noguera, Bennett Landman, Ian H. Gotlib, Kathryn L. Humphreys, Sarah Shultz, Longchuan Li, Sijie Niu, Weili Lin, Valerie Jewells, Gang Li, Dinggang Shen, Li Wang

Deep learning-based methods have achieved state-of-the-art performance; however, one of major limitations is that the learning-based methods may suffer from the multi-site issue, that is, the models trained on a dataset from one site may not be applicable to the datasets acquired from other sites with different imaging protocols/scanners.

Brain Segmentation

Shape-aware Meta-learning for Generalizing Prostate MRI Segmentation to Unseen Domains

1 code implementation4 Jul 2020 Quande Liu, Qi Dou, Pheng-Ann Heng

We present a novel shape-aware meta-learning scheme to improve the model generalization in prostate MRI segmentation.

Domain Generalization Meta-Learning +1

Semi-supervised Medical Image Classification with Relation-driven Self-ensembling Model

1 code implementation15 May 2020 Quande Liu, Lequan Yu, Luyang Luo, Qi Dou, Pheng Ann Heng

It is a consistency-based method which exploits the unlabeled data by encouraging the prediction consistency of given input under perturbations, and leverages a self-ensembling model to produce high-quality consistency targets for the unlabeled data.

General Classification Multi-Label Image Classification +2

LRTD: Long-Range Temporal Dependency based Active Learning for Surgical Workflow Recognition

1 code implementation21 Apr 2020 Xueying Shi, Yueming Jin, Qi Dou, Pheng-Ann Heng

Specifically, we propose a non-local recurrent convolutional network (NL-RCNet), which introduces non-local block to capture the long-range temporal dependency (LRTD) among continuous frames.

Active Learning

Constrained Multi-shape Evolution for Overlapping Cytoplasm Segmentation

no code implementations8 Apr 2020 Youyi Song, Lei Zhu, Baiying Lei, Bin Sheng, Qi Dou, Jing Qin, Kup-Sze Choi

In the shape evolution, we compensate intensity deficiency for the segmentation by introducing not only the modeled local shape priors but also global shape priors (clump--level) modeled by considering mutual shape constraints of cytoplasms in the clump.

Harmonizing Transferability and Discriminability for Adapting Object Detectors

1 code implementation CVPR 2020 Chaoqi Chen, Zebiao Zheng, Xinghao Ding, Yue Huang, Qi Dou

Recent advances in adaptive object detection have achieved compelling results in virtue of adversarial feature adaptation to mitigate the distributional shifts along the detection pipeline.

Object object-detection +1

Robust Multimodal Brain Tumor Segmentation via Feature Disentanglement and Gated Fusion

1 code implementation22 Feb 2020 Cheng Chen, Qi Dou, Yueming Jin, Hao Chen, Jing Qin, Pheng-Ann Heng

We tackle this challenge and propose a novel multimodal segmentation framework which is robust to the absence of imaging modalities.

Brain Tumor Segmentation Disentanglement +3

Automatic Gesture Recognition in Robot-assisted Surgery with Reinforcement Learning and Tree Search

no code implementations20 Feb 2020 Xiaojie Gao, Yueming Jin, Qi Dou, Pheng-Ann Heng

Automatic surgical gesture recognition is fundamental for improving intelligence in robot-assisted surgery, such as conducting complicated tasks of surgery surveillance and skill evaluation.

Action Segmentation Gesture Recognition +3

MS-Net: Multi-Site Network for Improving Prostate Segmentation with Heterogeneous MRI Data

2 code implementations9 Feb 2020 Quande Liu, Qi Dou, Lequan Yu, Pheng Ann Heng

However, the prostate MRIs from different sites present heterogeneity due to the differences in scanners and imaging protocols, raising challenges for effective ways of aggregating multi-site data for network training.

Transfer Learning

Unsupervised Bidirectional Cross-Modality Adaptation via Deeply Synergistic Image and Feature Alignment for Medical Image Segmentation

1 code implementation6 Feb 2020 Cheng Chen, Qi Dou, Hao Chen, Jing Qin, Pheng Ann Heng

In this work, we present a novel unsupervised domain adaptation framework, named as Synergistic Image and Feature Alignment (SIFA), to effectively adapt a segmentation network to an unlabeled target domain.

Image Segmentation Medical Image Segmentation +4

Unpaired Multi-modal Segmentation via Knowledge Distillation

1 code implementation6 Jan 2020 Qi Dou, Quande Liu, Pheng Ann Heng, Ben Glocker

We propose a novel learning scheme for unpaired cross-modality image segmentation, with a highly compact architecture achieving superior segmentation accuracy.

Image Segmentation Knowledge Distillation +3

Machine Learning with Multi-Site Imaging Data: An Empirical Study on the Impact of Scanner Effects

no code implementations10 Oct 2019 Ben Glocker, Robert Robinson, Daniel C. Castro, Qi Dou, Ender Konukoglu

This is an empirical study to investigate the impact of scanner effects when using machine learning on multi-site neuroimaging data.

BIG-bench Machine Learning

IRNet: Instance Relation Network for Overlapping Cervical Cell Segmentation

1 code implementation19 Aug 2019 Yanning Zhou, Hao Chen, Jiaqi Xu, Qi Dou, Pheng-Ann Heng

In this paper, we propose a novel Instance Relation Network (IRNet) for robust overlapping cell segmentation by exploring instance relation interaction.

Cell Segmentation Instance Segmentation +5

Incorporating Temporal Prior from Motion Flow for Instrument Segmentation in Minimally Invasive Surgery Video

1 code implementation18 Jul 2019 Yueming Jin, Keyun Cheng, Qi Dou, Pheng-Ann Heng

In this paper, we propose a novel framework to leverage instrument motion information, by incorporating a derived temporal prior to an attention pyramid network for accurate segmentation.

Segmentation

Multi-Task Recurrent Convolutional Network with Correlation Loss for Surgical Video Analysis

1 code implementation13 Jul 2019 Yueming Jin, Huaxia Li, Qi Dou, Hao Chen, Jing Qin, Chi-Wing Fu, Pheng-Ann Heng

Mutually leveraging both low-level feature sharing and high-level prediction correlating, our MTRCNet-CL method can encourage the interactions between the two tasks to a large extent, and hence can bring about benefits to each other.

Surgical phase recognition Surgical tool detection

Respiratory Motion Correction in Abdominal MRI using a Densely Connected U-Net with GAN-guided Training

no code implementations24 Jun 2019 Wenhao Jiang, Zhiyu Liu, Kit-Hang Lee, Shihui Chen, Yui-Lun Ng, Qi Dou, Hing-Chiu Chang, Ka-Wai Kwok

Abdominal magnetic resonance imaging (MRI) provides a straightforward way of characterizing tissue and locating lesions of patients as in standard diagnosis.

Generative Adversarial Network

Deep Angular Embedding and Feature Correlation Attention for Breast MRI Cancer Analysis

no code implementations7 Jun 2019 Luyang Luo, Hao Chen, Xi Wang, Qi Dou, Huangjin Lin, Juan Zhou, Gongjie Li, Pheng-Ann Heng

In this paper, we propose to identify breast tumor in MRI by Cosine Margin Sigmoid Loss (CMSL) with deep learning (DL) and localize possible cancer lesion by COrrelation Attention Map (COAM) based on the learned features.

Feature Correlation Specificity

Improving RetinaNet for CT Lesion Detection with Dense Masks from Weak RECIST Labels

3 code implementations5 Jun 2019 Martin Zlocha, Qi Dou, Ben Glocker

We propose a highly accurate and efficient one-stage lesion detector, by re-designing a RetinaNet to meet the particular challenges in medical imaging.

Computed Tomography (CT) Lesion Detection +3

CIA-Net: Robust Nuclei Instance Segmentation with Contour-aware Information Aggregation

no code implementations13 Mar 2019 Yanning Zhou, Omer Fahri Onder, Qi Dou, Efstratios Tsougenis, Hao Chen, Pheng-Ann Heng

Accurate segmenting nuclei instances is a crucial step in computer-aided image analysis to extract rich features for cellular estimation and following diagnosis as well as treatment.

Instance Segmentation Multi-tissue Nucleus Segmentation +2

Robust Learning at Noisy Labeled Medical Images: Applied to Skin Lesion Classification

no code implementations23 Jan 2019 Cheng Xue, Qi Dou, Xueying Shi, Hao Chen, Pheng Ann Heng

In this paper, we propose an effective iterative learning framework for noisy-labeled medical image classification, to combat the lacking of high quality annotated medical data.

General Classification Image Classification +3

The Liver Tumor Segmentation Benchmark (LiTS)

6 code implementations13 Jan 2019 Patrick Bilic, Patrick Christ, Hongwei Bran Li, Eugene Vorontsov, Avi Ben-Cohen, Georgios Kaissis, Adi Szeskin, Colin Jacobs, Gabriel Efrain Humpire Mamani, Gabriel Chartrand, Fabian Lohöfer, Julian Walter Holch, Wieland Sommer, Felix Hofmann, Alexandre Hostettler, Naama Lev-Cohain, Michal Drozdzal, Michal Marianne Amitai, Refael Vivantik, Jacob Sosna, Ivan Ezhov, Anjany Sekuboyina, Fernando Navarro, Florian Kofler, Johannes C. Paetzold, Suprosanna Shit, Xiaobin Hu, Jana Lipková, Markus Rempfler, Marie Piraud, Jan Kirschke, Benedikt Wiestler, Zhiheng Zhang, Christian Hülsemeyer, Marcel Beetz, Florian Ettlinger, Michela Antonelli, Woong Bae, Míriam Bellver, Lei Bi, Hao Chen, Grzegorz Chlebus, Erik B. Dam, Qi Dou, Chi-Wing Fu, Bogdan Georgescu, Xavier Giró-i-Nieto, Felix Gruen, Xu Han, Pheng-Ann Heng, Jürgen Hesser, Jan Hendrik Moltz, Christian Igel, Fabian Isensee, Paul Jäger, Fucang Jia, Krishna Chaitanya Kaluva, Mahendra Khened, Ildoo Kim, Jae-Hun Kim, Sungwoong Kim, Simon Kohl, Tomasz Konopczynski, Avinash Kori, Ganapathy Krishnamurthi, Fan Li, Hongchao Li, Junbo Li, Xiaomeng Li, John Lowengrub, Jun Ma, Klaus Maier-Hein, Kevis-Kokitsi Maninis, Hans Meine, Dorit Merhof, Akshay Pai, Mathias Perslev, Jens Petersen, Jordi Pont-Tuset, Jin Qi, Xiaojuan Qi, Oliver Rippel, Karsten Roth, Ignacio Sarasua, Andrea Schenk, Zengming Shen, Jordi Torres, Christian Wachinger, Chunliang Wang, Leon Weninger, Jianrong Wu, Daguang Xu, Xiaoping Yang, Simon Chun-Ho Yu, Yading Yuan, Miao Yu, Liping Zhang, Jorge Cardoso, Spyridon Bakas, Rickmer Braren, Volker Heinemann, Christopher Pal, An Tang, Samuel Kadoury, Luc Soler, Bram van Ginneken, Hayit Greenspan, Leo Joskowicz, Bjoern Menze

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018.

Benchmarking Computed Tomography (CT) +3

PnP-AdaNet: Plug-and-Play Adversarial Domain Adaptation Network with a Benchmark at Cross-modality Cardiac Segmentation

2 code implementations19 Dec 2018 Qi Dou, Cheng Ouyang, Cheng Chen, Hao Chen, Ben Glocker, Xiahai Zhuang, Pheng-Ann Heng

In this paper, we propose the PnPAdaNet (plug-and-play adversarial domain adaptation network) for adapting segmentation networks between different modalities of medical images, e. g., MRI and CT. We propose to tackle the significant domain shift by aligning the feature spaces of source and target domains in an unsupervised manner.

Cardiac Segmentation Domain Adaptation +2

3D RoI-aware U-Net for Accurate and Efficient Colorectal Tumor Segmentation

2 code implementations27 Jun 2018 Yi-Jie Huang, Qi Dou, Zi-Xian Wang, Li-Zhi Liu, Ying Jin, Chao-Feng Li, Lisheng Wang, Hao Chen, Rui-Hua Xu

With the region proposals from the encoder, we crop multi-level RoI in-region features from the encoder to form a GPU memory-efficient decoder for detailpreserving segmentation and therefore enlarged applicable volume size and effective receptive field.

Image Segmentation Multi-Task Learning +2

MILD-Net: Minimal Information Loss Dilated Network for Gland Instance Segmentation in Colon Histology Images

no code implementations5 Jun 2018 Simon Graham, Hao Chen, Jevgenij Gamper, Qi Dou, Pheng-Ann Heng, David Snead, Yee Wah Tsang, Nasir Rajpoot

However, this task is non-trivial due to the large variability in glandular appearance and the difficulty in differentiating between certain glandular and non-glandular histological structures.

Colorectal Gland Segmentation: Decision Making +4

Semantic-Aware Generative Adversarial Nets for Unsupervised Domain Adaptation in Chest X-ray Segmentation

no code implementations2 Jun 2018 Cheng Chen, Qi Dou, Hao Chen, Pheng-Ann Heng

In spite of the compelling achievements that deep neural networks (DNNs) have made in medical image computing, these deep models often suffer from degraded performance when being applied to new test datasets with domain shift.

Segmentation Transfer Learning +1

Unsupervised Cross-Modality Domain Adaptation of ConvNets for Biomedical Image Segmentations with Adversarial Loss

2 code implementations29 Apr 2018 Qi Dou, Cheng Ouyang, Cheng Chen, Hao Chen, Pheng-Ann Heng

The domain adaptation is more significant while challenging in the field of biomedical image analysis, where cross-modality data have largely different distributions.

Transfer Learning Unsupervised Domain Adaptation

SFCN-OPI: Detection and Fine-grained Classification of Nuclei Using Sibling FCN with Objectness Prior Interaction

no code implementations22 Dec 2017 Yanning Zhou, Qi Dou, Hao Chen, Jing Qin, Pheng-Ann Heng

Cell nuclei detection and fine-grained classification have been fundamental yet challenging problems in histopathology image analysis.

General Classification

H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation from CT Volumes

1 code implementation21 Sep 2017 Xiaomeng Li, Hao Chen, Xiaojuan Qi, Qi Dou, Chi-Wing Fu, Pheng Ann Heng

Our method outperformed other state-of-the-arts on the segmentation results of tumors and achieved very competitive performance for liver segmentation even with a single model.

 Ranked #1 on Liver Segmentation on LiTS2017 (Dice metric)

Automatic Liver And Tumor Segmentation Image Segmentation +4

Automated Pulmonary Nodule Detection via 3D ConvNets with Online Sample Filtering and Hybrid-Loss Residual Learning

no code implementations13 Aug 2017 Qi Dou, Hao Chen, Yueming Jin, Huangjing Lin, Jing Qin, Pheng-Ann Heng

In this paper, we propose a novel framework with 3D convolutional networks (ConvNets) for automated detection of pulmonary nodules from low-dose CT scans, which is a challenging yet crucial task for lung cancer early diagnosis and treatment.

Automatic 3D Cardiovascular MR Segmentation with Densely-Connected Volumetric ConvNets

2 code implementations2 Aug 2017 Lequan Yu, Jie-Zhi Cheng, Qi Dou, Xin Yang, Hao Chen, Jing Qin, Pheng-Ann Heng

Second, it avoids learning redundant feature maps by encouraging feature reuse and hence requires fewer parameters to achieve high performance, which is essential for medical applications with limited training data.

ScanNet: A Fast and Dense Scanning Framework for Metastatic Breast Cancer Detection from Whole-Slide Images

no code implementations30 Jul 2017 Huangjing Lin, Hao Chen, Qi Dou, Liansheng Wang, Jing Qin, Pheng-Ann Heng

Lymph node metastasis is one of the most significant diagnostic indicators in breast cancer, which is traditionally observed under the microscope by pathologists.

Breast Cancer Detection whole slide images

VoxResNet: Deep Voxelwise Residual Networks for Volumetric Brain Segmentation

3 code implementations21 Aug 2016 Hao Chen, Qi Dou, Lequan Yu, Pheng-Ann Heng

Recently deep residual learning with residual units for training very deep neural networks advanced the state-of-the-art performance on 2D image recognition tasks, e. g., object detection and segmentation.

Brain Segmentation Image Segmentation +4

3D Deeply Supervised Network for Automatic Liver Segmentation from CT Volumes

no code implementations3 Jul 2016 Qi Dou, Hao Chen, Yueming Jin, Lequan Yu, Jing Qin, Pheng-Ann Heng

Automatic liver segmentation from CT volumes is a crucial prerequisite yet challenging task for computer-aided hepatic disease diagnosis and treatment.

Liver Segmentation Segmentation

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