Multi-rater Prompting for Ambiguous Medical Image Segmentation
College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；Institute of Wenzhou, Zhejiang University（浙江大学温州研究院）；State Key Laboratory of Transvascular Implantation Devices of The Second Affiliated Hospital, Zhejiang University School of Medicine（浙江大学医学院第二附属医院血管植入装置国家重点实验室）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of Notre Dame（圣母大学）；School of Public Health Zhejiang University（浙江大学公共卫生学院）
Jinhong Wang, Yi Cheng, Jintai Chen, Hongxia Xu, Danny Chen, Jian Wu
Multi-rater annotations commonly occur when medical images are independently annotated by multiple experts (raters). In this paper, we tackle two challenges arisen in multi-rater annotations for medical image segmentation (called ambiguous medical image segmentation): (1) How to train a deep learning model when a group of raters produces a set of diverse but plausible annotations, and (2) how to fine-tune the model efficiently when computation resources are not available for re-training the entire model on a different dataset domain. We propose a multi-rater prompt-based approach to address these two challenges altogether. Specifically, we introduce a series of rater-aware prompts that can be plugged into the U-Net model for uncertainty estimation to handle multi-annotation cases. During the prompt-based fine-tuning process, only 0.3% of learnable parameters are required to be updated comparing to training the entire model. Further, in order to integrate expert consensus and disagreement, we explore different multi-rater incorporation strategies and design a mix-training strategy for comprehensive insight learning. Extensive experiments verify the effectiveness of our new approach for ambiguous medical image segmentation on two public datasets while alleviating the heavy burden of model re-training.
Harmonized Feature Conditioning and Frequency-Prompt Personalization for Multi-Rater Medical Segmentation
Faculty of Informatics and Data Science, University of Regensburg（信息学院与数据科学学院，莱比锡大学）；Sharif University of Technology（谢赫·伊斯兰大学）；Iran University of Science and Technology（伊朗科学技术大学）；ARTORG Center for Biomedical Research, University of Bern（伯尔尼大学生物医学研究中心）；Department of Radiation Oncology, University Hospital Bern, University of Bern（伯尔尼大学放射肿瘤科，伯尔尼大学）；Fraunhofer Institute for Digital Medicine MEVIS, Bremen（弗劳恩霍夫数字医学研究所MEVIS，不莱梅）
The task of image segmentation is inherently noisy due to ambiguities regarding the exact location of boundaries between anatomical structures. We argue that this information can be extracted from the expert annotations at no extra cost, and when integrated into state-of-the-art neural networks, it can lead to improved calibration between soft probabilistic predictions and the underlying uncertainty. We built upon label smoothing (LS) where a network is trained on 'blurred' versions of the ground truth labels which has been shown to be effective for calibrating output predictions. However, LS …
DiffOSeg: Omni Medical Image Segmentation via Multi-Expert Collaboration Diffusion Model
West China Biomedical Big Data Center（西昌生物医学大数据中心）；West China Hospital（西昌医院）；Sichuan University（四川大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
Medical image segmentation is inherently an ambiguous task due to factors such as partial volumes and variations in anatomical definitions. While in most cases the segmentation uncertainty is around the border of structures of interest, there can also be considerable inter-rater differences. The class of conditional variational autoencoders (cVAE) offers a principled approach to inferring distributions over plausible segmentations that are conditioned on input images. Segmentation uncertainty estimated from samples of such distributions can be more informative than using pixel level probabilit…
Weakly Supervised Medical Image Segmentation With Soft Labels and Noise Robust Loss
University of Calgary（卡尔加里大学）；McCaig Institute for Bone and Joint Health（麦凯格骨骼与关节健康研究所）；University of Alberta（阿尔伯塔大学）；Alberta Machine Intelligence Institute (AMII)（阿尔伯塔机器智能研究所）
Pseudo-D: Informing Multi-View Uncertainty Estimation with Calibrated Neural Training Dynamics
Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, Canada（电气与计算机工程系，不列颠哥伦比亚大学，温哥华，加拿大）；Division of Cardiology, Vancouver General Hospital, Vancouver, Canada（心血管科，温哥华总医院，温哥华，加拿大）
Zero-shot large vision-language model prompting for automated bone identification in paleoradiology x-ray archives
Vanderbilt University（范德堡大学）；Vanderbilt University School of Medicine（范德堡大学医学院）；Vanderbilt University Medical Center（范德堡大学医学中心）；Museum of London（伦敦博物馆）；Ossafreelance
University of Strasbourg, CNRS, INSERM, ICube, UMR7357, Strasbourg, France（斯特拉斯堡大学、法国国家科学研究中心、法国国家医学研究院、ICube、UMR7357、法国斯特拉斯堡）；Department of General Surgery, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung, Taiwan（高雄长庚纪念医院外科部、长庚大学医学院）
Baidu Inc.（百度公司）；University of Electronic Science and Technology of China（电子科技大学）；South China University of Technology（华南理工大学）；Shenzhen Eye Hospital, Jinan University（暨南大学附属深圳眼科医院）；Institute for Infocomm Research, A*STAR（新加坡科技研究局资讯通信研究院）
On the medical images, many of the tissues/lesions may be ambiguous. That is why the medical segmentation is typically annotated by a group of clinical experts to mitigate the personal bias. However, this clinical routine also brings new challenges to the application of machine learning algorithms. Without a definite ground-truth, it will be difficult to train and evaluate the deep learning models. When the annotations are collected from different graders, a common choice is majority vote. However such a strategy ignores the difference between the grader expertness. In this paper, we consider …
