Search Results for author: Kai Ma

Found 69 papers, 19 papers with code

Dual Adversarial Network for Deep Active Learning

no code implementations ECCV 2020 Shuo Wang, Yuexiang Li, Kai Ma, Ruhui Ma, Haibing Guan, Yefeng Zheng

In this paper, we investigate the overlapping problem of recent uncertainty-based approaches and propose to alleviate the issue by taking representativeness into consideration.

Active Learning

Self-Supervised CycleGAN for Object-Preserving Image-to-Image Domain Adaptation

no code implementations ECCV 2020 Xinpeng Xie, Jia-Wei Chen, Yuexiang Li, Linlin Shen, Kai Ma, Yefeng Zheng

Recent generative adversarial network (GAN) based methods (e. g., CycleGAN) are prone to fail at preserving image-objects in image-to-image translation, which reduces their practicality on tasks such as domain adaptation.

Domain Adaptation Image-to-Image Translation +2

Learning Shape Priors by Pairwise Comparison for Robust Semantic Segmentation

no code implementations23 Apr 2022 Cong Xie, Hualuo Liu, Shilei Cao, Dong Wei, Kai Ma, Liansheng Wang, Yefeng Zheng

A cosine similarity based attention module is proposed to fuse the information from both encoders, to utilize both types of prior information encoded by the template-encoder and model the inter-subject similarity for each foreground class.

Semantic Segmentation

DFTR: Depth-supervised Fusion Transformer for Salient Object Detection

no code implementations12 Mar 2022 Heqin Zhu, Xu sun, Yuexiang Li, Kai Ma, S. Kevin Zhou, Yefeng Zheng

This paper, for the first time, seeks to expand the applicability of depth supervision to the Transformer architecture.

object-detection Object Detection +1

Conquering Data Variations in Resolution: A Slice-Aware Multi-Branch Decoder Network

no code implementations7 Mar 2022 Shuxin Wang, Shilei Cao, Zhizhong Chai, Dong Wei, Kai Ma, Liansheng Wang, Yefeng Zheng

Based on the aforementioned innovations, we achieve state-of-the-art results on the MICCAI 2017 Liver Tumor Segmentation (LiTS) dataset.

Tumor Segmentation

Simultaneous Alignment and Surface Regression Using Hybrid 2D-3D Networks for 3D Coherent Layer Segmentation of Retina OCT Images

1 code implementation4 Mar 2022 Hong Liu, Dong Wei, Donghuan Lu, Yuexiang Li, Kai Ma, Liansheng Wang, Yefeng Zheng

To the best of our knowledge, this is the first study that attempts 3D retinal layer segmentation in volumetric OCT images based on CNNs.

Label Propagation for Annotation-Efficient Nuclei Segmentation from Pathology Images

no code implementations16 Feb 2022 Yi Lin, Zhiyong Qu, Hao Chen, Zhongke Gao, Yuexiang Li, Lili Xia, Kai Ma, Yefeng Zheng, Kwang-Ting Cheng

Third, a self-supervised visual representation learning method is tailored for nuclei segmentation of pathology images that transforms the hematoxylin component images into the H\&E stained images to gain better understanding of the relationship between the nuclei and cytoplasm.

Representation Learning

Energy Management Based on Multi-Agent Deep Reinforcement Learning for A Multi-Energy Industrial Park

no code implementations8 Feb 2022 Dafeng Zhu, Bo Yang, Yuxiang Liu, Zhaojian Wang, Kai Ma, Xinping Guan

Owing to large industrial energy consumption, industrial production has brought a huge burden to the grid in terms of renewable energy access and power supply.

reinforcement-learning

Revisiting Experience Replay: Continual Learning by Adaptively Tuning Task-wise Relationship

no code implementations31 Dec 2021 Quanziang Wang, Yuexiang Li, Dong Wei, Renzhen Wang, Kai Ma, Yefeng Zheng, Deyu Meng

These approaches save a small part of the data of the past tasks as a memory buffer to prevent models from forgetting previously learned knowledge.

Continual Learning Meta-Learning

Fast Distributed Stochastic Scheduling for A Multi-Energy Industrial Park

no code implementations27 Oct 2021 Dafeng Zhu, Bo Yang, Zhaojian Wang, Chengbin Ma, Kai Ma, Shanying Zhu

To solve the issue, the energy management problem is constructed as a stochastic optimization problem.

Stochastic Optimization

A Unified Framework for Generalized Low-Shot Medical Image Segmentation with Scarce Data

no code implementations18 Oct 2021 Hengji Cui, Dong Wei, Kai Ma, Shi Gu, Yefeng Zheng

In this work, we propose a unified framework for generalized low-shot (one- and few-shot) medical image segmentation based on distance metric learning (DML).

Medical Image Segmentation Metric Learning +1

Alleviating Noisy-label Effects in Image Classification via Probability Transition Matrix

no code implementations17 Oct 2021 Ziqi Zhang, Yuexiang Li, Hongxin Wei, Kai Ma, Tao Xu, Yefeng Zheng

The hard samples, which are beneficial for classifier learning, are often mistakenly treated as noises in such a setting since both the hard samples and ones with noisy labels lead to a relatively larger loss value than the easy cases.

Image Classification

Unsupervised Representation Learning Meets Pseudo-Label Supervised Self-Distillation: A New Approach to Rare Disease Classification

1 code implementation9 Oct 2021 Jinghan Sun, Dong Wei, Kai Ma, Liansheng Wang, Yefeng Zheng

Second, we integrate the URL with pseudo-label supervised classification for effective self-distillation of the knowledge about the rare diseases, composing a hybrid approach taking advantages of both unsupervised and (pseudo-) supervised learning on the base dataset.

Classification Few-Shot Learning +1

Training Automatic View Planner for Cardiac MR Imaging via Self-Supervision by Spatial Relationship between Views

1 code implementation24 Sep 2021 Dong Wei, Kai Ma, Yefeng Zheng

Then, a multi-view planning strategy is proposed to aggregate information from the predicted heatmaps for all the source views of a target view, for a globally optimal prescription.

InDuDoNet: An Interpretable Dual Domain Network for CT Metal Artifact Reduction

1 code implementation11 Sep 2021 Hong Wang, Yuexiang Li, Haimiao Zhang, Jiawei Chen, Kai Ma, Deyu Meng, Yefeng Zheng

For the task of metal artifact reduction (MAR), although deep learning (DL)-based methods have achieved promising performances, most of them suffer from two problems: 1) the CT imaging geometry constraint is not fully embedded into the network during training, leaving room for further performance improvement; 2) the model interpretability is lack of sufficient consideration.

Metal Artifact Reduction

Multi-Anchor Active Domain Adaptation for Semantic Segmentation

1 code implementation ICCV 2021 Munan Ning, Donghuan Lu, Dong Wei, Cheng Bian, Chenglang Yuan, Shuang Yu, Kai Ma, Yefeng Zheng

Unsupervised domain adaption has proven to be an effective approach for alleviating the intensive workload of manual annotation by aligning the synthetic source-domain data and the real-world target-domain samples.

Active Learning Domain Adaptation +1

A New Bidirectional Unsupervised Domain Adaptation Segmentation Framework

no code implementations18 Aug 2021 Munan Ning, Cheng Bian, Dong Wei, Chenglang Yuan, Yaohua Wang, Yang Guo, Kai Ma, Yefeng Zheng

Domain shift happens in cross-domain scenarios commonly because of the wide gaps between different domains: when applying a deep learning model well-trained in one domain to another target domain, the model usually performs poorly.

Representation Learning Unsupervised Domain Adaptation

RECIST-Net: Lesion detection via grouping keypoints on RECIST-based annotation

no code implementations19 Jul 2021 Cong Xie, Shilei Cao, Dong Wei, HongYu Zhou, Kai Ma, Xianli Zhang, Buyue Qian, Liansheng Wang, Yefeng Zheng

Universal lesion detection in computed tomography (CT) images is an important yet challenging task due to the large variations in lesion type, size, shape, and appearance.

Computed Tomography (CT) Lesion Detection +1

Mutual-GAN: Towards Unsupervised Cross-Weather Adaptation with Mutual Information Constraint

no code implementations30 Jun 2021 Jiawei Chen, Yuexiang Li, Kai Ma, Yefeng Zheng

In practical applications, the outdoor weather and illumination are changeable, e. g., cloudy and nighttime, which results in a significant drop of semantic segmentation accuracy of CNN only trained with daytime data.

Autonomous Driving Semantic Segmentation +2

Residual Moment Loss for Medical Image Segmentation

no code implementations27 Jun 2021 Quanziang Wang, Renzhen Wang, Yuexiang Li, Kai Ma, Yefeng Zheng, Deyu Meng

Location information is proven to benefit the deep learning models on capturing the manifold structure of target objects, and accordingly boosts the accuracy of medical image segmentation.

Medical Image Segmentation Semantic Segmentation

Calibrated RGB-D Salient Object Detection

1 code implementation CVPR 2021 Wei Ji, Jingjing Li, Shuang Yu, Miao Zhang, Yongri Piao, Shunyu Yao, Qi Bi, Kai Ma, Yefeng Zheng, Huchuan Lu, Li Cheng

Complex backgrounds and similar appearances between objects and their surroundings are generally recognized as challenging scenarios in Salient Object Detection (SOD).

object-detection RGB-D Salient Object Detection +1

LENAS: Learning-based Neural Architecture Search and Ensemble for 3D Radiotherapy Dose Prediction

no code implementations12 Jun 2021 Yi Lin, Yanfei Liu, Hao Chen, Xin Yang, Kai Ma, Yefeng Zheng, Kwang-Ting Cheng

To the best of our knowledge, this is the first attempt to investigate NAS and knowledge distillation in ensemble learning, especially in the field of medical image analysis.

Ensemble Learning Knowledge Distillation +1

Noisy Labels are Treasure: Mean-Teacher-Assisted Confident Learning for Hepatic Vessel Segmentation

no code implementations3 Jun 2021 Zhe Xu, Donghuan Lu, Yixin Wang, Jie Luo, Jayender Jagadeesan, Kai Ma, Yefeng Zheng, Xiu Li

Manually segmenting the hepatic vessels from Computer Tomography (CT) is far more expertise-demanding and laborious than other structures due to the low-contrast and complex morphology of vessels, resulting in the extreme lack of high-quality labeled data.

Generalized Organ Segmentation by Imitating One-shot Reasoning using Anatomical Correlation

no code implementations30 Mar 2021 Hong-Yu Zhou, Hualuo Liu, Shilei Cao, Dong Wei, Chixiang Lu, Yizhou Yu, Kai Ma, Yefeng Zheng

In this paper, we show that such process can be integrated into the one-shot segmentation task which is a very challenging but meaningful topic.

One-Shot Segmentation

Stabilized Medical Image Attacks

1 code implementation9 Mar 2021 Gege Qi, Lijun Gong, Yibing Song, Kai Ma, Yefeng Zheng

However, a threat to these systems arises that adversarial attacks make CNNs vulnerable.

Adversarial Attack Medical Diagnosis

MixSearch: Searching for Domain Generalized Medical Image Segmentation Architectures

1 code implementation26 Feb 2021 Luyan Liu, Zhiwei Wen, Songwei Liu, Hong-Yu Zhou, Hongwei Zhu, Weicheng Xie, Linlin Shen, Kai Ma, Yefeng Zheng

Considering the scarcity of medical data, most datasets in medical image analysis are an order of magnitude smaller than those of natural images.

Medical Image Segmentation Semantic Segmentation

Stabilized Medical Attacks

no code implementations ICLR 2021 Gege Qi, Lijun Gong, Yibing Song, Kai Ma, Yefeng Zheng

We further analyze the KL-divergence of the proposed loss function and find that the loss stabilization term makes the perturbations updated towards a fixed objective spot while deviating from the ground truth.

Adversarial Attack Medical Diagnosis

Ensembled ResUnet for Anatomical Brain Barriers Segmentation

no code implementations29 Dec 2020 Munan Ning, Cheng Bian, Chenglang Yuan, Kai Ma, Yefeng Zheng

However, due to the visual and anatomical differences between different modalities, the accurate segmentation of brain structures becomes challenging.

A Hierarchical Feature Constraint to Camouflage Medical Adversarial Attacks

1 code implementation17 Dec 2020 Qingsong Yao, Zecheng He, Yi Lin, Kai Ma, Yefeng Zheng, S. Kevin Zhou

Deep neural networks (DNNs) for medical images are extremely vulnerable to adversarial examples (AEs), which poses security concerns on clinical decision making.

Adversarial Attack Decision Making

Kinematical Observables in Semi-Invisible Decays

no code implementations8 Dec 2020 Kai Ma

Invisible particles frequently appear in final state in studying physics at colliders.

High Energy Physics - Phenomenology High Energy Physics - Experiment

Transport based Graph Kernels

no code implementations2 Nov 2020 Kai Ma, Peng Wan, Daoqiang Zhang

In order to effectively utilize graph hierarchical structure information, we propose pyramid graph kernel based on optimal transport (OT).

Ordinal Pattern Kernel for Brain Connectivity Network Classification

no code implementations18 Aug 2020 Kai Ma, Biao Jie, Daoqiang Zhang

Kernel-based method, such as graph kernel (i. e., kernel defined on graphs), has been proposed for measuring the similarity of brain networks, and yields the promising classification performance.

Classification General Classification

TR-GAN: Topology Ranking GAN with Triplet Loss for Retinal Artery/Vein Classification

no code implementations29 Jul 2020 Wenting Chen, Shuang Yu, Junde Wu, Kai Ma, Cheng Bian, Chunyan Chu, Linlin Shen, Yefeng Zheng

A topology ranking discriminator based on ordinal regression is proposed to rank the topological connectivity level of the ground-truth, the generated A/V mask and the intentionally shuffled mask.

Classification General Classification

Difficulty-aware Glaucoma Classification with Multi-Rater Consensus Modeling

no code implementations29 Jul 2020 Shuang Yu, Hong-Yu Zhou, Kai Ma, Cheng Bian, Chunyan Chu, Hanruo Liu, Yefeng Zheng

However, when being used for model training, only the final ground-truth label is utilized, while the critical information contained in the raw multi-rater gradings regarding the image being an easy/hard case is discarded.

Classification General Classification

Learning Crisp Edge Detector Using Logical Refinement Network

no code implementations24 Jul 2020 Luyan Liu, Kai Ma, Yefeng Zheng

Edge detection is a fundamental problem in different computer vision tasks.

Edge Detection

Instance-aware Self-supervised Learning for Nuclei Segmentation

no code implementations22 Jul 2020 Xinpeng Xie, Jia-Wei Chen, Yuexiang Li, Linlin Shen, Kai Ma, Yefeng Zheng

Due to the wide existence and large morphological variances of nuclei, accurate nuclei instance segmentation is still one of the most challenging tasks in computational pathology.

Instance Segmentation Self-Supervised Learning +1

MI^2GAN: Generative Adversarial Network for Medical Image Domain Adaptation using Mutual Information Constraint

no code implementations22 Jul 2020 Xinpeng Xie, Jia-Wei Chen, Yuexiang Li, Linlin Shen, Kai Ma, Yefeng Zheng

Domain shift between medical images from multicentres is still an open question for the community, which degrades the generalization performance of deep learning models.

Domain Adaptation Translation

Leveraging Undiagnosed Data for Glaucoma Classification with Teacher-Student Learning

1 code implementation22 Jul 2020 Junde Wu, Shuang Yu, WenTing Chen, Kai Ma, Rao Fu, Hanruo Liu, Xiaoguang Di, Yefeng Zheng

Recently, deep learning has been adopted to the glaucoma classification task with performance comparable to that of human experts.

Classification General Classification +1

A Macro-Micro Weakly-supervised Framework for AS-OCT Tissue Segmentation

no code implementations20 Jul 2020 Munan Ning, Cheng Bian, Donghuan Lu, Hong-Yu Zhou, Shuang Yu, Chenglang Yuan, Yang Guo, Yaohua Wang, Kai Ma, Yefeng Zheng

Primary angle closure glaucoma (PACG) is the leading cause of irreversible blindness among Asian people.

Self-Loop Uncertainty: A Novel Pseudo-Label for Semi-Supervised Medical Image Segmentation

no code implementations20 Jul 2020 Yuexiang Li, Jia-Wei Chen, Xinpeng Xie, Kai Ma, Yefeng Zheng

A novel pseudo-label (namely self-loop uncertainty), generated by recurrently optimizing the neural network with a self-supervised task, is adopted as the ground-truth for the unlabeled images to augment the training set and boost the segmentation accuracy.

Semantic Segmentation Semi-supervised Medical Image Segmentation

GREEN: a Graph REsidual rE-ranking Network for Grading Diabetic Retinopathy

1 code implementation20 Jul 2020 Shaoteng Liu, Lijun Gong, Kai Ma, Yefeng Zheng

In this paper, we propose a Graph REsidual rE-ranking Network (GREEN) to introduce a class dependency prior into the original image classification network.

Classification General Classification +3

Distractor-Aware Neuron Intrinsic Learning for Generic 2D Medical Image Classifications

no code implementations20 Jul 2020 Lijun Gong, Kai Ma, Yefeng Zheng

We formulate a novel distractor-aware loss that encourages large distance between the original image and its distractor in the feature space.

Classification Diabetic Retinopathy Grading +1

Deep Image Clustering with Category-Style Representation

1 code implementation ECCV 2020 Junjie Zhao, Donghuan Lu, Kai Ma, Yu Zhang, Yefeng Zheng

In this paper, we propose a novel deep image clustering framework to learn a category-style latent representation in which the category information is disentangled from image style and can be directly used as the cluster assignment.

Deep Clustering Image Clustering

Multi-Task Neural Networks with Spatial Activation for Retinal Vessel Segmentation and Artery/Vein Classification

no code implementations18 Jul 2020 Wenao Ma, Shuang Yu, Kai Ma, Jiexiang Wang, Xinghao Ding, Yefeng Zheng

In this paper, we propose a multi-task deep neural network with spatial activation mechanism that is able to segment full retinal vessel, artery and vein simultaneously, without the pre-requirement of vessel segmentation.

Classification General Classification +1

Superpixel-Guided Label Softening for Medical Image Segmentation

no code implementations17 Jul 2020 Hang Li, Dong Wei, Shilei Cao, Kai Ma, Liansheng Wang, Yefeng Zheng

If a superpixel intersects with the annotation boundary, we consider a high probability of uncertain labeling within this area.

Medical Image Segmentation Semantic Segmentation

Comparing to Learn: Surpassing ImageNet Pretraining on Radiographs By Comparing Image Representations

1 code implementation15 Jul 2020 Hong-Yu Zhou, Shuang Yu, Cheng Bian, Yifan Hu, Kai Ma, Yefeng Zheng

In deep learning era, pretrained models play an important role in medical image analysis, in which ImageNet pretraining has been widely adopted as the best way.

Learning and Exploiting Interclass Visual Correlations for Medical Image Classification

no code implementations13 Jul 2020 Dong Wei, Shilei Cao, Kai Ma, Yefeng Zheng

In this paper, we present the Class-Correlation Learning Network (CCL-Net) to learn interclass visual correlations from given training data, and produce soft labels to help with classification tasks.

General Classification Image Classification +2

Cross-denoising Network against Corrupted Labels in Medical Image Segmentation with Domain Shift

no code implementations19 Jun 2020 Qinming Zhang, Luyan Liu, Kai Ma, Cheng Zhuo, Yefeng Zheng

However, \textit{domain shift} and \textit{corrupted annotations}, which are two common problems in medical imaging, dramatically degrade the performance of DCNNs in practice.

Denoising Medical Image Segmentation +1

Generative Adversarial Networks for Video-to-Video Domain Adaptation

no code implementations17 Apr 2020 Jiawei Chen, Yuexiang Li, Kai Ma, Yefeng Zheng

Two colonoscopic datasets from different centres, i. e., CVC-Clinic and ETIS-Larib, are adopted to evaluate the performance of domain adaptation of our VideoGAN.

Domain Adaptation Translation

Quality Control of Neuron Reconstruction Based on Deep Learning

no code implementations19 Mar 2020 Donghuan Lu, Sujun Zhao, Peng Xie, Kai Ma, Li-Juan Liu, Yefeng Zheng

To ensure the quality of reconstructed neurons and provide guidance for annotators to improve their efficiency, we propose a deep learning based quality control method for neuron reconstruction in this paper.

Identification of primary angle-closure on AS-OCT images with Convolutional Neural Networks

no code implementations23 Oct 2019 Chenglang Yuan, Cheng Bian, Hongjian Kang, Shu Liang, Kai Ma, Yefeng Zheng

In this paper, we propose an efficient and accurate end-to-end architecture for angle-closure classification and scleral spur localization.

General Classification

Self-supervised Feature Learning for 3D Medical Images by Playing a Rubik's Cube

no code implementations5 Oct 2019 Xinrui Zhuang, Yuexiang Li, Yifan Hu, Kai Ma, Yujiu Yang, Yefeng Zheng

Witnessed the development of deep learning, increasing number of studies try to build computer aided diagnosis systems for 3D volumetric medical data.

Brain Tumor Segmentation Self-Supervised Learning +1

Uncertainty-Guided Domain Alignment for Layer Segmentation in OCT Images

no code implementations22 Aug 2019 Jiexiang Wang, Cheng Bian, Meng Li, Xin Yang, Kai Ma, Wenao Ma, Jin Yuan, Xinghao Ding, Yefeng Zheng

Automatic and accurate segmentation for retinal and choroidal layers of Optical Coherence Tomography (OCT) is crucial for detection of various ocular diseases.

Attentive CT Lesion Detection Using Deep Pyramid Inference with Multi-Scale Booster

1 code implementation9 Jul 2019 Qingbin Shao, Lijun Gong, Kai Ma, Hualuo Liu, Yefeng Zheng

Accurate lesion detection in computer tomography (CT) slices benefits pathologic organ analysis in the medical diagnosis process.

Lesion Detection Medical Diagnosis +2

X2CT-GAN: Reconstructing CT from Biplanar X-Rays with Generative Adversarial Networks

1 code implementation CVPR 2019 Xingde Ying, Heng Guo, Kai Ma, Jian Wu, Zheng-Xin Weng, Yefeng Zheng

Computed tomography (CT) can provide a 3D view of the patient's internal organs, facilitating disease diagnosis, but it incurs more radiation dose to a patient and a CT scanner is much more cost prohibitive than an X-ray machine too.

Computed Tomography (CT)

TAN: Temporal Affine Network for Real-Time Left Ventricle Anatomical Structure Analysis Based on 2D Ultrasound Videos

no code implementations1 Apr 2019 Sihong Chen, Kai Ma, Yefeng Zheng

Instead of using three networks with one dedicating to each task, we use a multi-task network to perform three tasks simultaneously.

Optical Flow Estimation

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

1 code implementation5 Nov 2018 Spyridon Bakas, Mauricio Reyes, Andras Jakab, Stefan Bauer, Markus Rempfler, Alessandro Crimi, Russell Takeshi Shinohara, Christoph Berger, Sung Min Ha, Martin Rozycki, Marcel Prastawa, Esther Alberts, Jana Lipkova, John Freymann, Justin Kirby, Michel Bilello, Hassan Fathallah-Shaykh, Roland Wiest, Jan Kirschke, Benedikt Wiestler, Rivka Colen, Aikaterini Kotrotsou, Pamela Lamontagne, Daniel Marcus, Mikhail Milchenko, Arash Nazeri, Marc-Andre Weber, Abhishek Mahajan, Ujjwal Baid, Elizabeth Gerstner, Dongjin Kwon, Gagan Acharya, Manu Agarwal, Mahbubul Alam, Alberto Albiol, Antonio Albiol, Francisco J. Albiol, Varghese Alex, Nigel Allinson, Pedro H. A. Amorim, Abhijit Amrutkar, Ganesh Anand, Simon Andermatt, Tal Arbel, Pablo Arbelaez, Aaron Avery, Muneeza Azmat, Pranjal B., W Bai, Subhashis Banerjee, Bill Barth, Thomas Batchelder, Kayhan Batmanghelich, Enzo Battistella, Andrew Beers, Mikhail Belyaev, Martin Bendszus, Eze Benson, Jose Bernal, Halandur Nagaraja Bharath, George Biros, Sotirios Bisdas, James Brown, Mariano Cabezas, Shilei Cao, Jorge M. Cardoso, Eric N Carver, Adrià Casamitjana, Laura Silvana Castillo, Marcel Catà, Philippe Cattin, Albert Cerigues, Vinicius S. Chagas, Siddhartha Chandra, Yi-Ju Chang, Shiyu Chang, Ken Chang, Joseph Chazalon, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Chen, Kun Cheng, Ahana Roy Choudhury, Roger Chylla, Albert Clérigues, Steven Colleman, Ramiro German Rodriguez Colmeiro, Marc Combalia, Anthony Costa, Xiaomeng Cui, Zhenzhen Dai, Lutao Dai, Laura Alexandra Daza, Eric Deutsch, Changxing Ding, Chao Dong, Shidu Dong, Wojciech Dudzik, Zach Eaton-Rosen, Gary Egan, Guilherme Escudero, Théo Estienne, Richard Everson, Jonathan Fabrizio, Yong Fan, Longwei Fang, Xue Feng, Enzo Ferrante, Lucas Fidon, Martin Fischer, Andrew P. French, Naomi Fridman, Huan Fu, David Fuentes, Yaozong Gao, Evan Gates, David Gering, Amir Gholami, Willi Gierke, Ben Glocker, Mingming Gong, Sandra González-Villá, T. Grosges, Yuanfang Guan, Sheng Guo, Sudeep Gupta, Woo-Sup Han, Il Song Han, Konstantin Harmuth, Huiguang He, Aura Hernández-Sabaté, Evelyn Herrmann, Naveen Himthani, Winston Hsu, Cheyu Hsu, Xiaojun Hu, Xiaobin Hu, Yan Hu, Yifan Hu, Rui Hua, Teng-Yi Huang, Weilin Huang, Sabine Van Huffel, Quan Huo, Vivek HV, Khan M. Iftekharuddin, Fabian Isensee, Mobarakol Islam, Aaron S. Jackson, Sachin R. Jambawalikar, Andrew Jesson, Weijian Jian, Peter Jin, V Jeya Maria Jose, Alain Jungo, B Kainz, Konstantinos Kamnitsas, Po-Yu Kao, Ayush Karnawat, Thomas Kellermeier, Adel Kermi, Kurt Keutzer, Mohamed Tarek Khadir, Mahendra Khened, Philipp Kickingereder, Geena Kim, Nik King, Haley Knapp, Urspeter Knecht, Lisa Kohli, Deren Kong, Xiangmao Kong, Simon Koppers, Avinash Kori, Ganapathy Krishnamurthi, Egor Krivov, Piyush Kumar, Kaisar Kushibar, Dmitrii Lachinov, Tryphon Lambrou, Joon Lee, Chengen Lee, Yuehchou Lee, M Lee, Szidonia Lefkovits, Laszlo Lefkovits, James Levitt, Tengfei Li, Hongwei Li, Hongyang Li, Xiaochuan Li, Yuexiang Li, Heng Li, Zhenye Li, Xiaoyu Li, Zeju Li, Xiaogang Li, Wenqi Li, Zheng-Shen Lin, Fengming Lin, Pietro Lio, Chang Liu, Boqiang Liu, Xiang Liu, Mingyuan Liu, Ju Liu, Luyan Liu, Xavier Llado, Marc Moreno Lopez, Pablo Ribalta Lorenzo, Zhentai Lu, Lin Luo, Zhigang Luo, Jun Ma, Kai Ma, Thomas Mackie, Anant Madabushi, Issam Mahmoudi, Klaus H. Maier-Hein, Pradipta Maji, CP Mammen, Andreas Mang, B. S. Manjunath, Michal Marcinkiewicz, S McDonagh, Stephen McKenna, Richard McKinley, Miriam Mehl, Sachin Mehta, Raghav Mehta, Raphael Meier, Christoph Meinel, Dorit Merhof, Craig Meyer, Robert Miller, Sushmita Mitra, Aliasgar Moiyadi, David Molina-Garcia, Miguel A. B. Monteiro, Grzegorz Mrukwa, Andriy Myronenko, Jakub Nalepa, Thuyen Ngo, Dong Nie, Holly Ning, Chen Niu, Nicholas K Nuechterlein, Eric Oermann, Arlindo Oliveira, Diego D. C. Oliveira, Arnau Oliver, Alexander F. I. Osman, Yu-Nian Ou, Sebastien Ourselin, Nikos Paragios, Moo Sung Park, Brad Paschke, J. Gregory Pauloski, Kamlesh Pawar, Nick Pawlowski, Linmin Pei, Suting Peng, Silvio M. Pereira, Julian Perez-Beteta, Victor M. Perez-Garcia, Simon Pezold, Bao Pham, Ashish Phophalia, Gemma Piella, G. N. Pillai, Marie Piraud, Maxim Pisov, Anmol Popli, Michael P. Pound, Reza Pourreza, Prateek Prasanna, Vesna Prkovska, Tony P. Pridmore, Santi Puch, Élodie Puybareau, Buyue Qian, Xu Qiao, Martin Rajchl, Swapnil Rane, Michael Rebsamen, Hongliang Ren, Xuhua Ren, Karthik Revanuru, Mina Rezaei, Oliver Rippel, Luis Carlos Rivera, Charlotte Robert, Bruce Rosen, Daniel Rueckert, Mohammed Safwan, Mostafa Salem, Joaquim Salvi, Irina Sanchez, Irina Sánchez, Heitor M. Santos, Emmett Sartor, Dawid Schellingerhout, Klaudius Scheufele, Matthew R. Scott, Artur A. Scussel, Sara Sedlar, Juan Pablo Serrano-Rubio, N. Jon Shah, Nameetha Shah, Mazhar Shaikh, B. Uma Shankar, Zeina Shboul, Haipeng Shen, Dinggang Shen, Linlin Shen, Haocheng Shen, Varun Shenoy, Feng Shi, Hyung Eun Shin, Hai Shu, Diana Sima, M Sinclair, Orjan Smedby, James M. Snyder, Mohammadreza Soltaninejad, Guidong Song, Mehul Soni, Jean Stawiaski, Shashank Subramanian, Li Sun, Roger Sun, Jiawei Sun, Kay Sun, Yu Sun, Guoxia Sun, Shuang Sun, Yannick R Suter, Laszlo Szilagyi, Sanjay Talbar, DaCheng Tao, Zhongzhao Teng, Siddhesh Thakur, Meenakshi H Thakur, Sameer Tharakan, Pallavi Tiwari, Guillaume Tochon, Tuan Tran, Yuhsiang M. Tsai, Kuan-Lun Tseng, Tran Anh Tuan, Vadim Turlapov, Nicholas Tustison, Maria Vakalopoulou, Sergi Valverde, Rami Vanguri, Evgeny Vasiliev, Jonathan Ventura, Luis Vera, Tom Vercauteren, C. A. Verrastro, Lasitha Vidyaratne, Veronica Vilaplana, Ajeet Vivekanandan, Qian Wang, Chiatse J. Wang, Wei-Chung Wang, Duo Wang, Ruixuan Wang, Yuanyuan Wang, Chunliang Wang, Guotai Wang, Ning Wen, Xin Wen, Leon Weninger, Wolfgang Wick, Shaocheng Wu, Qiang Wu, Yihong Wu, Yong Xia, Yanwu Xu, Xiaowen Xu, Peiyuan Xu, Tsai-Ling Yang, Xiaoping Yang, Hao-Yu Yang, Junlin Yang, Haojin Yang, Guang Yang, Hongdou Yao, Xujiong Ye, Changchang Yin, Brett Young-Moxon, Jinhua Yu, Xiangyu Yue, Songtao Zhang, Angela Zhang, Kun Zhang, Xue-jie Zhang, Lichi Zhang, Xiaoyue Zhang, Yazhuo Zhang, Lei Zhang, Jian-Guo Zhang, Xiang Zhang, Tianhao Zhang, Sicheng Zhao, Yu Zhao, Xiaomei Zhao, Liang Zhao, Yefeng Zheng, Liming Zhong, Chenhong Zhou, Xiaobing Zhou, Fan Zhou, Hongtu Zhu, Jin Zhu, Ying Zhuge, Weiwei Zong, Jayashree Kalpathy-Cramer, Keyvan Farahani, Christos Davatzikos, Koen van Leemput, Bjoern Menze

This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.

Brain Tumor Segmentation Survival Prediction +1

Generating Synthetic X-ray Images of a Person from the Surface Geometry

no code implementations CVPR 2018 Brian Teixeira, Vivek Singh, Terrence Chen, Kai Ma, Birgi Tamersoy, Yifan Wu, Elena Balashova, Dorin Comaniciu

Furthermore, the synthetic X-ray image is parametrized and can be manipulated by adjusting a set of body markers which are also generated during the X-ray image prediction.

Anomaly Detection

BodyPrint: Pose Invariant 3D Shape Matching of Human Bodies

no code implementations ICCV 2015 Jiangping Wang, Kai Ma, Vivek Kumar Singh, Thomas Huang, Terrence Chen

3D human body shape matching has large potential on many real world applications, especially with the recent advances in the 3D range sensing technology.

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