Recent advances in deep learning have resulted in great successes in various applications. Although semi-supervised or unsupervised learning methods have been widely investigated, the performance of deep neural networks highly depends on the annotated data. The problem is that the budget for annotation is usually limited due to the annotation time and expensive annotation cost in medical data. Active learning is one of the solutions to this problem where an active learner is designed to indicate which samples need to be annotated to effectively train a target model. In this paper, we propose a novel active learning method, confident coreset, which considers both uncertainty and distribution for effectively selecting informative samples. By comparative experiments on two medical image analysis tasks, we show that our method outperforms other active learning methods.
Selective experience replay compression using coresets for lifelong deep reinforcement learning in medical imaging
Rice University（莱斯大学）；McGovern Medical School, UTHealth Houston（德克萨斯大学健康科学中心休斯顿麦戈文医学院）；The Johns Hopkins University School of Medicine（约翰斯·霍普金斯大学医学院）；University of Maryland School of Medicine（马里兰大学医学院）
Evolution-aware VAriance (EVA) Coreset Selection for Medical Image Classification
South-Central Minzu University（中南民族大学）；College of Computer Science（计算机学院）；XiDian University（西安电子科技大学）；Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局）；Centre for Frontier AI Research (CFAR)（前沿人工智能研究中心）；Institute of High Performance Computing (IHPC)（高性能计算研究所）
WaveFuse-AL: Cyclical and Performance-Adaptive Multi-Strategy Active Learning for Medical Images
Department of Computer Science \& Engineering, Indian Institute of Technology Ropar, India Department of Biomedical Engineering, Indian Institute of Technology Ropar, India
I Detect What I Don't Know: Incremental Anomaly Learning with Stochastic Weight Averaging-Gaussian for Oracle-Free Medical Imaging
AI Research Lab, Department of Computer Science（人工智能研究实验室、计算机科学系）；Biomedical & Translational Sciences, Sanford School of Medicine University of South Dakota（生物医学与转化科学、桑福大学医学院）
McGill University（麦吉尔大学）；MILA Quebec AI Institute（魁北克人工智能研究所MILA）
Large, annotated datasets are not widely available in medical image analysis due to the prohibitive time, costs, and challenges associated with labelling large datasets. Unlabelled datasets are easier to obtain, and in many contexts, it would be feasible for an expert to provide labels for a small subset of images. This work presents an information-theoretic active learning framework that guides the optimal selection of images from the unlabelled pool to be labeled based on maximizing the expected information gain (EIG) on an evaluation dataset. Experiments are performed on two different medic…
Johns Hopkins University（约翰斯·霍普金斯大学）；Huazhong University of Science and Technology（华中科技大学）；Shanghai University（上海大学）；The Johns Hopkins University School of Medicine（约翰斯·霍普金斯大学医学院）
Deep convolutional neural networks (CNNs), especially fully convolutional networks, have been widely applied to automatic medical image segmentation problems, e.g., multi-organ segmentation. Existing CNN-based segmentation methods mainly focus on looking for increasingly powerful network architectures, but pay less attention to data sampling strategies for training networks more effectively. In this paper, we present a simple but effective sample selection method for training multi-organ segmentation networks. Sample selection exhibits an exploitation-exploration strategy, i.e., exploiting har…
Transmute AI Lab（Transmute AI实验室）；UiT The Arctic University of Norway（挪威北极大学UiT）；Immersal（Immersal公司）
Coreset selection is among the most effective ways to reduce the training time of CNNs, however, only limited is known on how the resultant models will behave under variations of the coreset size, and choice of datasets and models. Moreover, given the recent paradigm shift towards transformer-based models, it is still an open question how coreset selection would impact their performance. There are several similar intriguing questions that need to be answered for a wide acceptance of coreset selection methods, and this paper attempts to answer some of these. We present a systematic benchmarking…
Early detection of esophagitis is important because this condition can progress to cancer if left untreated. However, the accuracies of different deep learning models in detecting esophagitis have yet to be compared. Thus, this study aimed to compare the accuracies of convolutional neural network models (GoogLeNet, ResNet-50, MobileNet V2, and MobileNet V3) in detecting esophagitis from the open Kvasir dataset of endoscopic images. Results showed that among the models, GoogLeNet achieved the highest F1-scores. Based on the average of true positive rate, MobileNet V3 predicted esophagitis more …
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）；Universität Ulm（乌尔姆大学）
Effective semi-supervised learning (SSL) in medical image analysis (MIA) must address two challenges: 1) work effectively on both multi-class (e.g., lesion classification) and multi-label (e.g., multiple-disease diagnosis) problems, and 2) handle imbalanced learning (because of the high variance in disease prevalence). One strategy to explore in SSL MIA is based on the pseudo labelling strategy, but it has a few shortcomings. Pseudo-labelling has in general lower accuracy than consistency learning, it is not specifically designed for both multi-class and multi-label problems, and it can be cha…
Vanderbilt University（范德堡大学）；Vanderbilt University Medical Center（范德堡大学医学中心）；Siemens Healthineers（西门子医疗）
Although deep learning prediction models have been successful in the discrimination of different classes, they can often suffer from poor calibration across challenging domains including healthcare. Moreover, the long-tail distribution poses great challenges in deep learning classification problems including clinical disease prediction. There are approaches proposed recently to calibrate deep prediction in computer vision, but there are no studies found to demonstrate how the representative models work in different challenging contexts. In this paper, we bridge the confidence calibration from …
Technical University of Munich（慕尼黑工业大学）；Johns Hopkins University（约翰斯·霍普金斯大学）；ETH Zurich（苏黎世联邦理工学院）
Medical image segmentation is one of the major challenges addressed by machine learning methods. Yet, deep learning methods profoundly depend on a large amount of annotated data, which is time-consuming and costly. Though, semi-supervised learning methods approach this problem by leveraging an abundant amount of unlabeled data along with a small amount of labeled data in the training process. Recently, MixUp regularizer has been successfully introduced to semi-supervised learning methods showing superior performance. MixUp augments the model with new data points through linear interpolation of…
Deep neural network-based medical image classifications often use "hard" labels for training, where the probability of the correct category is 1 and those of others are 0. However, these hard targets can drive the networks over-confident about their predictions and prone to overfit the training data, affecting model generalization and adaption. Studies have shown that label smoothing and softening can improve classification performance. Nevertheless, existing approaches are either non-data-driven or limited in applicability. In this paper, we present the Class-Correlation Learning Network (CCL…
Scientific Computing and Imaging Institute（科学计算与成像研究所）；University of Utah（犹他大学）
Statistical shape modeling (SSM) directly from 3D medical images is an underutilized tool for detecting pathology, diagnosing disease, and conducting population-level morphology analysis. Deep learning frameworks have increased the feasibility of adopting SSM in medical practice by reducing the expert-driven manual and computational overhead in traditional SSM workflows. However, translating such frameworks to clinical practice requires calibrated uncertainty measures as neural networks can produce over-confident predictions that cannot be trusted in sensitive clinical decision-making. Existin…
Manipal Institute of Technology（马尼帕尔理工学院）；Manipal Academy of Higher Education（马尼帕尔高等教育学院）
Deep Ensemble Convolutional Neural Networks has become a methodology of choice for analyzing medical images with a diagnostic performance comparable to a physician, including the diagnosis of Diabetic Retinopathy. However, commonly used techniques are deterministic and are therefore unable to provide any estimate of predictive uncertainty. Quantifying model uncertainty is crucial for reducing the risk of misdiagnosis. A reliable architecture should be well-calibrated to avoid over-confident predictions. To address this, we propose a UATTA-ENS: Uncertainty-Aware Test-Time Augmented Ensemble Tec…
Federated Learning for Large Models in Medical Imaging: A Comprehensive Review
School of Cyber Science and Engineering, Sichuan University（四川大学网络科学与工程学院）；Sichuan Institute of Computer Sciences（四川省计算机科学研究院）；College of Computer Science, Sichuan University（四川大学计算机学院）；Department of Mathematics, The Chinese University of Hong Kong（香港中文大学数学系）；School of Electrical and Electronic Engineering, College of Engineering, Yonsei University（延世大学工程学院电子与电气工程系）
Information Sciences Institute（信息科学研究所）；University of Southern California（南加州大学）
Convolutional neural networks (CNNs) have led to significant improvements in the semantic segmentation of images. When source and target datasets come from different modalities, CNN performance suffers due to domain shift. In such cases data annotation in the target domain becomes necessary to maintain model performance. To circumvent the re-annotation process, unsupervised domain adaptation (UDA) is proposed to adapt a model to new modalities using solely unlabeled target data. Common UDA algorithms require access to source domain data during adaptation, which may not be feasible in medical i…
Medical Image De-Identification Resources: Synthetic DICOM Data and Tools for Validation
University of Arkansas for Medical Sciences（亚拉巴马医学科学大学）；Frederick National Laboratory for Cancer Research（癌症研究国家实验室）；Ellumen, Inc.（Ellumen公司）；Deloitte Consulting LLP（德勤咨询有限公司）；National Cancer Institute（国家癌症研究所）；Pixelmed Publishing（Pixelmed出版公司）；National Heart, Lung, and Blood Institute（国家心脏、肺部和血液研究所）
SecondOpinion: Anatomy-Aware Gated Reasoning for Efficient Medical Image Analysis
Center for Computational & Data Sciences, Independent University, Bangladesh（孟加拉国独立大学计算与数据科学中心）；Department of Computer Science and Engineering, Independent University, Bangladesh（孟加拉国独立大学计算机科学与工程系）；Graduate School of Engineering, University of Hyogo（兵库大学工程研究生院）
Iterative Misclassification Error Training (IMET): An Optimized Neural Network Training Technique for Image Classification
National Institute of Standards；Department of Physics, Colorado State University, Fort Collins, CO 80523 USA (e-mail: )；Electrical Engineering Department, University of Colorado, Boulder, CO 80309 USA；Farragut High School, Knoxville, TN 37934 USA (e-mail: )；Department of Mathematics；Statistics, Texas A\&M University, Corpus Christi, TX 78412 USA (e-mail: )
Beyond scalar losses: calibrating segmentation models via gradient vector field surgery
School of Computation, Information and Technology, TUM（慕尼黑工业大学计算、信息与技术学院）；Munich Center for Machine Learning（慕尼黑机器学习中心）；Department of Radiology, Weill Cornell Medicine（威尔康乃尔医学院放射科）；School of Medicine and Health, TUM University Hospital（慕尼黑工业大学医院医学与健康学院）；Cornell Tech（康奈尔科技学院）；Department of Computing, Imperial College London（伦敦帝国理工学院计算系）
UPL-SFDA: Uncertainty-aware Pseudo Label Guided Source-Free Domain Adaptation for Medical Image Segmentation
School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China（电子科技大学机械与电气工程学院）；Shanghai AI laboratory（上海人工智能实验室）；Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China（电子科技大学附属四川省人民医院）；SenseTime Research（商汤科技研究院）；Zhejiang Laboratory（浙江实验室）；School of Biomedical Engineering & Imaging Sciences, King’s College London（伦敦国王学院生物医学工程与影像科学学院）
Intersectional Fairness in Vision-Language Models for Medical Image Disease Classification
Biomedical Data Analysis and Visualisation (BDAV) Lab, School of Computer Science（生物医学数据分析与可视化实验室，计算机科学学院）；Sydney School of Public Health（悉尼公共卫生学院）；School of Computing（计算学院）
A Contrastive Learning-Guided Confident Meta-learning for Zero Shot Anomaly Detection
Dept. of Engineering for Innovation Medicine, University of Verona（创新医学工程系，威尼斯大学）；Faculty of Computer and Information Science, University of Ljubljana（计算机与信息科学系，卢布尔雅那大学）；Qualyco S.r.l.（Qualyco公司）
Translation Consistent Semi-supervised Segmentation for 3D Medical Images
Australian Institute for Machine Learning, University of Adelaide（澳大利亚机器学习研究所，阿德莱德大学）；Harvard Ophtalmology AI Lab of Schepens Eye Research Institute, Harvard Medical School（哈佛施佩尔眼科研究所眼科人工智能实验室，哈佛医学院）；College of Electronic and Information Engineering, Tongji University（同济大学电子与信息学院）；School of Electrical and Computer Engineering, Cornell University and Cornell Tech（康奈尔大学电气与计算机工程学院及康奈尔科技）；Faculty of Engineering of the Friedrich-Alexander-Universität Erlangen-Nürnberg（埃朗根-纽伦堡弗里德里希-亚历山大大学工程学院）；Centre for Vision, Speech and Signal Processing (CVSSP), University of Surrey（视觉、语音和信号处理中心（CVSSP）， Surrey大学）
East China Normal University（华东师范大学）；Huashan Hospital, Fudan University（复旦大学附属华山医院）
Medical image segmentation is one of the fundamental problems for artificial intelligence-based clinical decision systems. Current automatic medical image segmentation methods are often failed to meet clinical requirements. As such, a series of interactive segmentation algorithms are proposed to utilize expert correction information. However, existing methods suffer from some segmentation refining failure problems after long-term interactions and some cost problems from expert annotation, which hinder clinical applications. This paper proposes an interactive segmentation framework, called inte…
MetaSSL: A General Heterogeneous Loss for Semi-Supervised Medical Image Segmentation
School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China（电子科技大学机械与电子工程学院）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Department of Radiation Oncology, Sichuan Cancer Hospital and Institute, University of Electronic Science and Technology of China（电子科技大学四川肿瘤医院放射肿瘤科）；Department of Pathology, West China Second University Hospital, Sichuan University（四川大学华西第二医院病理科）
Deep convolutional neural networks (DCNNs) have contributed many breakthroughs in segmentation tasks, especially in the field of medical imaging. However, \textit{domain shift} and \textit{corrupted annotations}, which are two common problems in medical imaging, dramatically degrade the performance of DCNNs in practice. In this paper, we propose a novel robust cross-denoising framework using two peer networks to address domain shift and corrupted label problems with a peer-review strategy. Specifically, each network performs as a mentor, mutually supervised to learn from reliable samples selec…
Technical University of Munich（慕尼黑工业大学）；OpenMined（OpenMined（开放隐私计算社区））；Imperial College London（帝国理工学院）
Collaborative machine learning techniques such as federated learning (FL) enable the training of models on effectively larger datasets without data transfer. Recent initiatives have demonstrated that segmentation models trained with FL can achieve performance similar to locally trained models. However, FL is not a fully privacy-preserving technique and privacy-centred attacks can disclose confidential patient data. Thus, supplementing FL with privacy-enhancing technologies (PTs) such as differential privacy (DP) is a requirement for clinical applications in a multi-institutional setting. The a…
Kyoto University（京都大学）；Guangdong Provincial People’s Hospital（广东省人民医院）；University of Notre Dame（圣母大学）
The strict security requirements placed on medical records by various privacy regulations become major obstacles in the age of big data. To ensure efficient machine learning as a service schemes while protecting data confidentiality, in this work, we propose blind UNET (BUNET), a secure protocol that implements privacy-preserving medical image segmentation based on the UNET architecture. In BUNET, we efficiently utilize cryptographic primitives such as homomorphic encryption and garbled circuits (GC) to design a complete secure protocol for the UNET neural architecture. In addition, we perform…
Boston Children’s Hospital（波士顿儿童医院）；Harvard Medical School（哈佛医学院）
Convolutional Neural Networks (CNNs) have shown to be powerful medical image segmentation models. In this study, we address some of the main unresolved issues regarding these models. Specifically, training of these models on small medical image datasets is still challenging, with many studies promoting techniques such as transfer learning. Moreover, these models are infamous for producing over-confident predictions and for failing silently when presented with out-of-distribution (OOD) data at test time. In this paper, we advocate for multi-task learning, i.e., training a single model on severa…
The Hong Kong University of Science and Technology（香港科技大学）；The Chinese University of Hong Kong（香港中文大学）；The Hong Kong University of Science and Technology Shenzhen Research Institute（香港科技大学深圳研究院）
Generalizing the medical image segmentation algorithms to unseen domains is an important research topic for computer-aided diagnosis and surgery. Most existing methods require a fully labeled dataset in each source domain. Although some researchers developed a semi-supervised domain generalized method, it still requires the domain labels. This paper presents a novel confidence-aware cross pseudo supervision algorithm for semi-supervised domain generalized medical image segmentation. The main goal is to enhance the pseudo label quality for unlabeled images from unknown distributions. To achieve…
Similarity-as-Evidence: Calibrating Overconfident VLMs for Interpretable and Label-Efficient Medical Active Learning
School of Electronic Technology and Engineering, Xiamen University, Xiamen, China（厦门大学电子技术与工程学院，厦门，中国）；School of Informatics, Xiamen University, Xiamen, China（厦门大学信息学院，厦门，中国）；School of Engineering, Case Western Reserve University, Cleveland, USA（凯斯西储大学工程学院，克利夫兰，美国）
Measuring Prediction Uncertainty in Neural Cellular Automata
Computational Health Center, Helmholtz Munich, Neuherberg, Germany（赫尔姆霍茨慕尼黑计算健康中心）；Helmholtz AI, Helmholtz Munich, Neuherberg, Germany（赫尔姆霍茨慕尼黑人工智能研究所）；Computer Aided Medical Procedures, Technical University of Munich, Munich, Germany（慕尼黑技术大学计算机辅助医疗程序研究所）；Munich Center for Machine Learning, Munich, Germany（慕尼黑机器学习中心）；Department of Medicine III, Ludwig-Maximilian-University Hospital, Munich, Germany（慕尼黑路德维希-马克西米利安大学医院第三医学部）；Department of Physics, University of Munich, Munich, Germany（慕尼黑大学物理系）；German Cancer Consortium (DKTK), partner site Munich, Germany（德国癌症研究中心（DKTK）慕尼黑分部）
Leveraging Task-Specific Knowledge from LLM for Semi-Supervised 3D Medical Image Segmentation
Indian Institute of Technology Roorkee（鲁尔基印度理工学院）；Vellore Institute of Technology（韦洛尔理工大学）；Alipurduar Government Engineering and Management College（阿里布尔杜阿尔政府工程与管理学院）；Indian Institute of Technology Mandi（曼迪印度理工学院）
本文提出 LLM-SegNet，通过 LLM 将任务特定知识注入协同训练框架，并设计统一分割损失，提升半监督 3D 医学图像分割性能。
