Search Results for author: Mobarakol Islam

Found 47 papers, 35 papers with code

LLM-Assisted Multi-Teacher Continual Learning for Visual Question Answering in Robotic Surgery

no code implementations26 Feb 2024 Kexin Chen, Yuyang Du, Tao You, Mobarakol Islam, Ziyu Guo, Yueming Jin, Guangyong Chen, Pheng-Ann Heng

We further design an adaptive weight assignment approach that balances the generalization ability of the LLM and the domain expertise of the old CL model.

EndoOOD: Uncertainty-aware Out-of-distribution Detection in Capsule Endoscopy Diagnosis

no code implementations18 Feb 2024 Qiaozhi Tan, Long Bai, Guankun Wang, Mobarakol Islam, Hongliang Ren

Wireless capsule endoscopy (WCE) is a non-invasive diagnostic procedure that enables visualization of the gastrointestinal (GI) tract.

Decision Making Out-of-Distribution Detection

OSSAR: Towards Open-Set Surgical Activity Recognition in Robot-assisted Surgery

1 code implementation10 Feb 2024 Long Bai, Guankun Wang, Jie Wang, Xiaoxiao Yang, Huxin Gao, Xin Liang, An Wang, Mobarakol Islam, Hongliang Ren

Existing algorithms dedicated to surgical activity recognition predominantly cater to pre-defined closed-set paradigms, ignoring the challenges of real-world open-set scenarios.

Activity Recognition

Privacy-Preserving Synthetic Continual Semantic Segmentation for Robotic Surgery

1 code implementation8 Feb 2024 Mengya Xu, Mobarakol Islam, Long Bai, Hongliang Ren

The problem becomes worse when it limits releasing the dataset of the old instruments for the old model due to privacy concerns and the unavailability of the data for the new or updated version of the instruments for the continual learning model.

Continual Learning Continual Semantic Segmentation +3

Surgical-DINO: Adapter Learning of Foundation Models for Depth Estimation in Endoscopic Surgery

1 code implementation11 Jan 2024 Beilei Cui, Mobarakol Islam, Long Bai, Hongliang Ren

There is clear evidence in the results that zero-shot prediction on pre-trained weights in computer vision datasets or naive fine-tuning is not sufficient to use the foundation model in the surgical domain directly.

3D Reconstruction Depth Estimation

SAM Meets Robotic Surgery: An Empirical Study on Generalization, Robustness and Adaptation

no code implementations14 Aug 2023 An Wang, Mobarakol Islam, Mengya Xu, Yang Zhang, Hongliang Ren

Our extensive evaluation results reveal that although SAM shows remarkable zero-shot generalization ability with bounding box prompts, it struggles to segment the whole instrument with point-based prompts and unprompted settings.

Semantic Segmentation Zero-shot Generalization

Robustness Stress Testing in Medical Image Classification

1 code implementation14 Aug 2023 Mobarakol Islam, Zeju Li, Ben Glocker

We conclude that progressive stress testing is a viable and important tool and should become standard practice in the clinical validation of image-based disease detection models.

Image Classification Medical Image Classification +1

Landmark Detection using Transformer Toward Robot-assisted Nasal Airway Intubation

1 code implementation5 Aug 2023 Tianhang Liu, Hechen Li, Long Bai, Yanan Wu, An Wang, Mobarakol Islam, Hongliang Ren

This paper proposes a transformer-based landmark detection solution with deformable DeTR and the semantic-aligned-matching module for detecting landmarks in robot-assisted intubation.

Revisiting Distillation for Continual Learning on Visual Question Localized-Answering in Robotic Surgery

1 code implementation22 Jul 2023 Long Bai, Mobarakol Islam, Hongliang Ren

We further establish a CL framework on three public surgical datasets in the context of surgical settings that consist of overlapping classes between old and new surgical VQLA tasks.

Continual Learning Scene Understanding

CAT-ViL: Co-Attention Gated Vision-Language Embedding for Visual Question Localized-Answering in Robotic Surgery

1 code implementation11 Jul 2023 Long Bai, Mobarakol Islam, Hongliang Ren

Therefore, a surgical Visual Question Localized-Answering (VQLA) system would be helpful for medical students and junior surgeons to learn and understand from recorded surgical videos.

Question Answering Scene Understanding +1

LLCaps: Learning to Illuminate Low-Light Capsule Endoscopy with Curved Wavelet Attention and Reverse Diffusion

1 code implementation5 Jul 2023 Long Bai, Tong Chen, Yanan Wu, An Wang, Mobarakol Islam, Hongliang Ren

Given the exuberant development of the denoising diffusion probabilistic model (DDPM) in computer vision, we introduce a WCE LLIE framework based on the multi-scale convolutional neural network (CNN) and reverse diffusion process.

Denoising Low-Light Image Enhancement

Generalizing Surgical Instruments Segmentation to Unseen Domains with One-to-Many Synthesis

1 code implementation28 Jun 2023 An Wang, Mobarakol Islam, Mengya Xu, Hongliang Ren

In this work, we mitigate data-related issues by efficiently leveraging minimal source images to generate synthetic surgical instrument segmentation datasets and achieve outstanding generalization performance on unseen real domains.

Scene Understanding

Curriculum-Based Augmented Fourier Domain Adaptation for Robust Medical Image Segmentation

1 code implementation6 Jun 2023 An Wang, Mobarakol Islam, Mengya Xu, Hongliang Ren

Accurate and robust medical image segmentation is fundamental and crucial for enhancing the autonomy of computer-aided diagnosis and intervention systems.

Domain Adaptation Image Segmentation +4

S$^2$ME: Spatial-Spectral Mutual Teaching and Ensemble Learning for Scribble-supervised Polyp Segmentation

1 code implementation1 Jun 2023 An Wang, Mengya Xu, Yang Zhang, Mobarakol Islam, Hongliang Ren

Furthermore, to produce reliable mixed pseudo labels, which enhance the effectiveness of ensemble learning, we introduce a novel adaptive pixel-wise fusion technique based on the entropy guidance from the spatial and spectral branches.

Ensemble Learning Image Segmentation +4

Surgical-VQLA: Transformer with Gated Vision-Language Embedding for Visual Question Localized-Answering in Robotic Surgery

1 code implementation19 May 2023 Long Bai, Mobarakol Islam, Lalithkumar Seenivasan, Hongliang Ren

In this paper, we propose Visual Question Localized-Answering in Robotic Surgery (Surgical-VQLA) to localize the specific surgical area during the answer prediction.

Answer Generation object-detection +3

SAM Meets Robotic Surgery: An Empirical Study in Robustness Perspective

no code implementations28 Apr 2023 An Wang, Mobarakol Islam, Mengya Xu, Yang Zhang, Hongliang Ren

In this empirical study, we investigate the robustness and zero-shot generalizability of the SAM in the domain of robotic surgery in various settings of (i) prompted vs. unprompted; (ii) bounding box vs. points-based prompt; (iii) generalization under corruptions and perturbations with five severity levels; and (iv) state-of-the-art supervised model vs. SAM.

Semantic Segmentation Zero-shot Generalization

SurgicalGPT: End-to-End Language-Vision GPT for Visual Question Answering in Surgery

1 code implementation19 Apr 2023 Lalithkumar Seenivasan, Mobarakol Islam, Gokul Kannan, Hongliang Ren

Given the limitations of unidirectional attention in GPT models and their ability to generate coherent long paragraphs, we carefully sequence the word tokens before vision tokens, mimicking the human thought process of understanding the question to infer an answer from an image.

Question Answering Scene Segmentation +1

Paced-Curriculum Distillation with Prediction and Label Uncertainty for Image Segmentation

1 code implementation2 Feb 2023 Mobarakol Islam, Lalithkumar Seenivasan, S. P. Sharan, V. K. Viekash, Bhavesh Gupta, Ben Glocker, Hongliang Ren

Purpose: In curriculum learning, the idea is to train on easier samples first and gradually increase the difficulty, while in self-paced learning, a pacing function defines the speed to adapt the training progress.

Image Segmentation Medical Image Segmentation +3

Confidence-Aware Paced-Curriculum Learning by Label Smoothing for Surgical Scene Understanding

1 code implementation22 Dec 2022 Mengya Xu, Mobarakol Islam, Ben Glocker, Hongliang Ren

In this work, we design a paced curriculum by label smoothing (P-CBLS) using paced learning with uniform label smoothing (ULS) for classification tasks and fuse uniform and spatially varying label smoothing (SVLS) for semantic segmentation tasks in a curriculum manner.

Multi-Label Classification Scene Understanding +1

Task-Aware Asynchronous Multi-Task Model with Class Incremental Contrastive Learning for Surgical Scene Understanding

1 code implementation28 Nov 2022 Lalithkumar Seenivasan, Mobarakol Islam, Mengya Xu, Chwee Ming Lim, Hongliang Ren

Conclusion: The proposed multi-task model was able to adapt to domain shifts, incorporate novel instruments in the target domain, and perform tool-tissue interaction detection and report generation on par with single-task models.

Contrastive Learning Decision Making +4

Frequency Dropout: Feature-Level Regularization via Randomized Filtering

no code implementations20 Sep 2022 Mobarakol Islam, Ben Glocker

Both high and low frequencies can be characteristic of the underlying noise distribution caused by the image acquisition rather than in relation to the task-relevant information about the image content.

Domain Adaptation Image Classification +1

Estimating Model Performance under Domain Shifts with Class-Specific Confidence Scores

1 code implementation20 Jul 2022 Zeju Li, Konstantinos Kamnitsas, Mobarakol Islam, Chen Chen, Ben Glocker

If we could estimate the performance that a pre-trained model would achieve on data from a specific deployment setting, for example a certain clinic, we could judge whether the model could safely be deployed or if its performance degrades unacceptably on the specific data.

Image Segmentation Semantic Segmentation

Angular Gap: Reducing the Uncertainty of Image Difficulty through Model Calibration

1 code implementation18 Jul 2022 Bohua Peng, Mobarakol Islam, Mei Tu

In this work, we propose Angular Gap, a measure of difficulty based on the difference in angular distance between feature embeddings and class-weight embeddings built by hyperspherical learning.

Unsupervised Domain Adaptation

Rethinking Surgical Captioning: End-to-End Window-Based MLP Transformer Using Patches

1 code implementation30 Jun 2022 Mengya Xu, Mobarakol Islam, Hongliang Ren

Surgical captioning plays an important role in surgical instruction prediction and report generation.

Video Captioning

Rethinking Surgical Instrument Segmentation: A Background Image Can Be All You Need

2 code implementations23 Jun 2022 An Wang, Mobarakol Islam, Mengya Xu, Hongliang Ren

Our empirical analysis suggests that without the high cost of data collection and annotation, we can achieve decent surgical instrument segmentation performance.

Domain Adaptation Incremental Learning +2

Surgical-VQA: Visual Question Answering in Surgical Scenes using Transformer

2 code implementations22 Jun 2022 Lalithkumar Seenivasan, Mobarakol Islam, Adithya K Krishna, Hongliang Ren

This overload often limits their time answering questionnaires from patients, medical students or junior residents related to surgical procedures.

Question Answering Sentence +1

Class Balanced PixelNet for Neurological Image Segmentation

no code implementations23 Apr 2022 Mobarakol Islam, Hongliang Ren

We deal with this problem by selecting an equal number of pixels for all the classes in sampling time.

Brain Tumor Segmentation Image Segmentation +4

Ischemic Stroke Lesion Segmentation Using Adversarial Learning

no code implementations11 Apr 2022 Mobarakol Islam, N Rajiv Vaidyanathan, V Jeya Maria Jose, Hongliang Ren

Training a segmentation network along with an adversarial network can detect and correct higher order inconsistencies between the segmentation maps produced by ground-truth and the Segmentor.

Brain Segmentation Computed Tomography (CT) +3

Global-Reasoned Multi-Task Learning Model for Surgical Scene Understanding

2 code implementations28 Jan 2022 Lalithkumar Seenivasan, Sai Mitheran, Mobarakol Islam, Hongliang Ren

Global and local relational reasoning enable scene understanding models to perform human-like scene analysis and understanding.

Graph Attention Knowledge Distillation +5

ST-MTL: Spatio-Temporal Multitask Learning Model to Predict Scanpath While Tracking Instruments in Robotic Surgery

1 code implementation10 Dec 2021 Mobarakol Islam, Vibashan VS, Chwee Ming Lim, Hongliang Ren

We generate the task-aware saliency maps and scanpath of the instruments on the dataset of the MICCAI 2017 robotic instrument segmentation challenge.

Computational Efficiency Multi-Task Learning +3

Class-Distribution-Aware Calibration for Long-Tailed Visual Recognition

1 code implementation11 Sep 2021 Mobarakol Islam, Lalithkumar Seenivasan, Hongliang Ren, Ben Glocker

In CDA-TS, the scalar temperature value is replaced with the CDA temperature vector encoded with class frequency to compensate for the over-confidence.

Class-Incremental Domain Adaptation with Smoothing and Calibration for Surgical Report Generation

1 code implementation23 Jul 2021 Mengya Xu, Mobarakol Islam, Chwee Ming Lim, Hongliang Ren

To adapt incremental classes and extract domain invariant features, a class-incremental (CI) learning method with supervised contrastive (SupCon) loss is incorporated with a feature extractor.

Domain Adaptation Few-Shot Learning +1

Spatially Varying Label Smoothing: Capturing Uncertainty from Expert Annotations

1 code implementation12 Apr 2021 Mobarakol Islam, Ben Glocker

The task of image segmentation is inherently noisy due to ambiguities regarding the exact location of boundaries between anatomical structures.

Image Segmentation Segmentation +1

Glioma Prognosis: Segmentation of the Tumor and Survival Prediction using Shape, Geometric and Clinical Information

no code implementations2 Apr 2021 Mobarakol Islam, V Jeya Maria Jose, Hongliang Ren

In this paper, we exploit a convolutional neural network (CNN) with hypercolumn technique to segment tumor from healthy brain tissue.

Segmentation Survival Prediction

Brain Tumor Segmentation and Survival Prediction using 3D Attention UNet

1 code implementation2 Apr 2021 Mobarakol Islam, Vibashan VS, V Jeya Maria Jose, Navodini Wijethilake, Uppal Utkarsh, Hongliang Ren

For survival prediction, we extract some novel radiomic features based on geometry, location, the shape of the segmented tumor and combine them with clinical information to estimate the survival duration for each patient.

Brain Tumor Segmentation Survival Prediction +1

Glioblastoma Multiforme Prognosis: MRI Missing Modality Generation, Segmentation and Radiogenomic Survival Prediction

1 code implementation17 Mar 2021 Mobarakol Islam, Navodini Wijethilake, Hongliang Ren

The proposed approaches are evaluated by comparative experiments with state-of-the-art models in synthesis, segmentation, and overall survival (OS) prediction.

Generative Adversarial Network Segmentation +2

Radiogenomics of Glioblastoma: Identification of Radiomics associated with Molecular Subtypes

no code implementations27 Oct 2020 Navodini Wijethilake, Mobarakol Islam, Dulani Meedeniya, Charith Chitraranjan, Indika Perera, Hongliang Ren

Glioblastoma is the most malignant type of central nervous system tumor with GBM subtypes cleaved based on molecular level gene alterations.

Learning and Reasoning with the Graph Structure Representation in Robotic Surgery

2 code implementations7 Jul 2020 Mobarakol Islam, Lalithkumar Seenivasan, Lim Chwee Ming, Hongliang Ren

Learning to infer graph representations and performing spatial reasoning in a complex surgical environment can play a vital role in surgical scene understanding in robotic surgery.

Edge Classification Graph Generation +3

AP-MTL: Attention Pruned Multi-task Learning Model for Real-time Instrument Detection and Segmentation in Robot-assisted Surgery

1 code implementation10 Mar 2020 Mobarakol Islam, Vibashan VS, Hongliang Ren

Training a real-time robotic system for the detection and segmentation of high-resolution images provides a challenging problem with the limited computational resource.

Multi-Task Learning Scene Understanding +1

Learning Where to Look While Tracking Instruments in Robot-assisted Surgery

1 code implementation29 Jun 2019 Mobarakol Islam, Yueyuan Li, Hongliang Ren

For this purpose, we propose an end-to-end trainable multitask learning (MTL) model for real-time surgical instrument segmentation and attention prediction.


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

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