Uncertainty Quantification in Medical Image Segmentation with Multi-decoder U-Net
Harbin Institute of Technology at Shenzhen（哈尔滨工业大学（深圳））；Peng Cheng Lab（鹏城实验室）；MindsGo Co.Ltd（MindsGo 有限公司）；Capital Medical University（首都医科大学）；Xuanwu Hospital Capital Medical University（首都医科大学宣武医院）
Accurate medical image segmentation is crucial for diagnosis and analysis. However, the models without calibrated uncertainty estimates might lead to errors in downstream analysis and exhibit low levels of robustness. Estimating the uncertainty in the measurement is vital to making definite, informed conclusions. Especially, it is difficult to make accurate predictions on ambiguous areas and focus boundaries for both models and radiologists, even harder to reach a consensus with multiple annotations. In this work, the uncertainty under these areas is studied, which introduces significant information with anatomical structure and is as important as segmentation performance. We exploit the medical image segmentation uncertainty quantification by measuring segmentation performance with multiple annotations in a supervised learning manner and propose a U-Net based architecture with multiple decoders, where the image representation is encoded with the same encoder, and segmentation referring to each annotation is estimated with multiple decoders. Nevertheless, a cross-loss function is proposed for bridging the gap between different branches. The proposed architecture is trained in an end-to-end manner and able to improve predictive uncertainty estimates. The model achieves comparable performance with fewer parameters to the integrated training model that ranked the runner-up in the MICCAI-QUBIQ 2020 challenge.
Inter-Rater Uncertainty Quantification in Medical Image Segmentation via Rater-Specific Bayesian Neural Networks
Southern University of Science and Technology (SUSTech)（南方科技大学）；University of California, Los Angeles（加州大学洛杉矶分校）；Brown University（布朗大学）；Affiliated Drum Tower Hospital of Nanjing University Medical School（南京大学医学院附属鼓楼医院）；Technical University of Munich（慕尼黑工业大学）；University of Zurich（苏黎世大学）
PMR-Net: Parallel Multi-Resolution Encoder-Decoder Network Framework for Medical Image Segmentation
Shaanxi University of Science and Technology（陕西科技大学）；School of Electronic Information and Artificial Intelligence, Shaanxi University of Science and Technology（陕西科技大学电子信息与人工智能学院）
Double Encoder-Decoder Networks for Gastrointestinal Polyp Segmentation
Bournemouth University（伯恩茅斯大学）；Australian Institute for Machine Learning, University of Adelaide（阿德莱德大学澳大利亚机器学习研究所）；BCN Medtech（巴塞罗那医疗科技公司）；Universitat Pompeu Fabra（庞培法布拉大学）；ICREA（加泰罗尼亚高等研究院）
PottsMGNet: A Mathematical Explanation of Encoder-Decoder Based Neural Networks
NORCE Norwegian Research Centre（NORCE挪威研究中心）；Hong Kong Baptist University（香港浸会大学）；City University of Hong Kong（香港城市大学）；Hong Kong Centre for Cerebro-Cardiovascular Health Engineering（香港心脑血管健康工程中心）
Beyond Uncertainty Quantification: Learning Uncertainty for Trust-Informed Neural Network Decisions - A Case Study in COVID-19 Classification
Faculty of Engineering & IT, University of Technology Sydney（技术悉尼大学工程与信息学院）；University Research and Innovation Center (EKIK), Óbuda University（奥布达大学研究与创新中心）
Guided Deep Decoder: Unsupervised Image Pair Fusion
RIKEN AIP（理化学研究所革新智能综合研究中心）；German Aerospace Center（德国航空航天中心）；Univ. Grenoble Alpes（格勒诺布尔-阿尔卑斯大学）；CNRS（法国国家科学研究中心）；Grenoble INP（格勒诺布尔理工学院）；GIPSA-lab（格勒诺布尔图像语音信号自动控制实验室）；The University of Tokyo（东京大学）
Cycle Consistency-based Uncertainty Quantification of Neural Networks in Inverse Imaging Problems
University of California, Los Angeles（加州大学洛杉矶分校）；California NanoSystems Institute (CNSI), University of California, Los Angeles（加州大学洛杉矶分校加州纳米系统研究所）；David Geffen School of Medicine, University of California Los Angeles（加州大学洛杉矶分校大卫·格芬医学院）
