Tied Prototype Model for Few-Shot Medical Image Segmentation
Department of Physics and Technology, UiT - The Arctic University of Norway（物理与技术系，UiT - 北极大学）；Norwegian Centre for Clinical Artificial Intelligence (SPKI)（挪威临床人工智能中心）；University Hospital of North-Norway（北挪威大学医院）；Norwegian Computing Center（挪威计算中心）
Hyeongji Kim, Stine Hansen, Michael Kampffmeyer
备注：
Submitted version (MICCAI). Accepted at MICCAI 2025. The code repo will be made publicly available soon
Common prototype-based medical image few-shot segmentation (FSS) methods model foreground and background classes using class-specific prototypes. However, given the high variability of the background, a more promising direction is to focus solely on foreground modeling, treating the background as an anomaly -- an approach introduced by ADNet. Yet, ADNet faces three key limitations: dependence on a single prototype per class, a focus on binary classification, and fixed thresholds that fail to adapt to patient and organ variability. To address these shortcomings, we propose the Tied Prototype Model (TPM), a principled reformulation of ADNet with tied prototype locations for foreground and background distributions. Building on its probabilistic foundation, TPM naturally extends to multiple prototypes and multi-class segmentation while effectively separating non-typical background features. Notably, both extensions lead to improved segmentation accuracy. Finally, we leverage naturally occurring class priors to define an ideal target for adaptive thresholds, boosting segmentation performance. Taken together, TPM provides a fresh perspective on prototype-based FSS for medical image segmentation. The code can be found at https://github.com/hjk92g/TPM-FSS.
Shanghai Jiao Tong University（上海交通大学）；VoxelCloud, Inc.（体素云公司）；University of California, Los Angeles（加利福尼亚大学洛杉矶分校）
Despite the tremendous success of deep neural networks in medical image segmentation, they typically require a large amount of costly, expert-level annotated data. Few-shot segmentation approaches address this issue by learning to transfer knowledge from limited quantities of labeled examples. Incorporating appropriate prior knowledge is critical in designing high-performance few-shot segmentation algorithms. Since strong spatial priors exist in many medical imaging modalities, we propose a prototype-based method -- namely, the location-sensitive local prototype network -- that leverages spati…
Prototype Contrastive Consistency Learning for Semi-Supervised Medical Image Segmentation
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Computer Vision Institute, College of Computer Science and Software Engineering, Shenzhen University（深圳大学计算机与软件学院计算机视觉研究所）；Baoan Central Hospital of Shenzhen（深圳市宝安中心医院）
SPENet: Self-guided Prototype Enhancement Network for Few-shot Medical Image Segmentation
School of Computer Science, Beijing University of Technology, Beijing, China（北京理工大学计算机科学学院）；Institute of Automation Chinese Academy of Sciences, Beijing, China（中国科学院自动化研究所）；Multimedia Department Xiaomi Inc, Beijing, China（小米公司多媒体部门）；Capital Medical University Affiliated Beijing Friendship Hospital, Beijing, China（首都医科大学附属北京友谊医院）
ProCNS: Progressive Prototype Calibration and Noise Suppression for Weakly-Supervised Medical Image Segmentation
Southern University of Science and Technology（南方科技大学）；University of Hong Kong（香港大学）；Jiaxing Research Institute, Southern University of Science and Technology（南方科技大学嘉兴研究院）
Semi-supervised learning methods have been explored in medical image segmentation tasks due to the scarcity of pixel-level annotation in the real scenario. Proto-type alignment based consistency constraint is an intuitional and plausible solu-tion to explore the useful information in the unlabeled data. In this paper, we propose a mutual- and self- prototype alignment (MSPA) framework to better utilize the unlabeled data. In specific, mutual-prototype alignment enhances the information interaction between labeled and unlabeled data. The mutual-prototype alignment imposes two consistency constr…
Efficient Prototype Consistency Learning in Medical Image Segmentation via Joint Uncertainty and Data Augmentation
Faculty of Science and Technology（科技学院）；University of Macau（澳门大学）；Key Laboratory of High Confidence Software Technologies (MOE)（高可信软件技术重点实验室）；School of Computer Science（计算机学院）；Peking University（北京大学）
Few-Shot Medical Image Segmentation with High-Fidelity Prototypes
University of Shanghai for Science and Technology（上海理工大学）；Universität Hamburg（汉堡大学）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of Electronic Science and Technology of China（电子科技大学）；University of Surrey（萨里大学）
Prototype Correlation Matching and Class-Relation Reasoning for Few-Shot Medical Image Segmentation
The Hong Kong Polytechnic University（香港理工大学）；Shenyang Institute of Automation, Chinese Academy of Sciences（中国科学院沈阳自动化研究所）；Durham University（杜伦大学）；Jarvis Research Center, Tencent YouTu Lab（腾讯优图实验室贾维斯研究中心）
Beyond Euclidean Prototypes: Spectral Disentanglement and Geodesic Matching for Few-Shot Medical Image Segmentation
School of Microelectronics and Communication Engineering, Chongqing University（重庆大学微电子与通信工程学院）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）
Prompting Segment Anything Model with Domain-Adaptive Prototype for Generalizable Medical Image Segmentation
National Engineering Research Center for Multimedia Software（国家多媒体软件工程技术研究中心）；Medical Artificial Intelligence Research Institute of Renmin Hospital, Wuhan University（武汉大学人民医院医学人工智能研究院）；School of Computer Science, Central China Normal University（华中师范大学计算机学院）
EPL: Evidential Prototype Learning for Semi-supervised Medical Image Segmentation
Key Laboratory of High Confidence Software Technologies (Peking University)（高可信软件技术重点实验室（北京大学））；School of Computer Science, Peking University（北京大学计算机学院）
Style-Aware Blending and Prototype-Based Cross-Contrast Consistency for Semi-Supervised Medical Image Segmentation
School of Information Science and Engineering（信息科学与工程学院）；Yunnan University（云南大学）；Yunnan Key Laboratory of Intelligent Systems and Computing（智能系统与计算云南省重点实验室）
Rethinking Medical Landmark Localization with Prototype Learning-based Progressive Offset Correction
Shenzhen University（深圳大学）；Centre for Artificial Intelligence and Robotics, Hong Kong Institute of Science & Innovation, Chinese Academy of Sciences（中国科学院香港创新研究院人工智能与机器人中心）；Boston Children’s Hospital（波士顿儿童医院）；Harvard Medical School（哈佛医学院）；School of Artificial Intelligence, Shenzhen University（深圳大学人工智能学院）；School of Biomedical Engineering and Informatics, Nanjing Medical University（南京医科大学生物医学工程与信息学院）；National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University（深圳大学大数据系统计算技术国家工程实验室）
Hebei University of Technology（河北工业大学）；University of Macau（澳门大学）；University of Oxford（牛津大学）；Hospital of Joint Logistics Support Force（联勤保障部队第983医院）
Because the expansion path of U-Net may ignore the characteristics of small targets, intermediate supervision mechanism is proposed. The original mask is also entered into the network as a label for intermediate output. However, U-Net is mainly engaged in segmentation, and the extracted features are also targeted at segmentation location information, and the input and output are different. The label we need is that the input and output are both original masks, which is more similar to the refactoring process, so we propose another intermediate supervision mechanism. However, the features extra…
Double Banking on Knowledge: Customized Modulation and Prototypes for Multi-Modality Semi-supervised Medical Image Segmentation
College of Computer Science, Sichuan University（四川大学计算机学院）；Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局）；College of Electrical Engineering, Sichuan University（四川大学电气工程学院）；School of Cyber Science and Engineering, Sichuan University（四川大学网络空间安全学院）
Medical image annotation typically requires expert knowledge and hence incurs time-consuming and expensive data annotation costs. To alleviate this burden, we propose a novel learning scenario, Exemplar Learning (EL), to explore automated learning processes for medical image segmentation with a single annotated image example. This innovative learning task is particularly suitable for medical image segmentation, where all categories of organs can be presented in one single image and annotated all at once. To address this challenging EL task, we propose an Exemplar Learning-based Synthesis Net (…
Embracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision
School of Computer Science and Technology, Tongji University（同济大学计算机科学与技术学院）；Artificial Intelligence Institute, Shanghai University（上海大学人工智能研究院）；Department of Computer and Data Sciences, Case Western Reserve University（凯斯西储大学计算机与数据科学系）；Department of Biomedical Engineering, Case Western Reserve University（凯斯西储大学生物医学工程系）
Imperial College London（帝国理工学院）；BioMedIA Group, Department of Computing（计算系BioMedIA小组）
Few-shot semantic segmentation (FSS) has great potential for medical imaging applications. Most of the existing FSS techniques require abundant annotated semantic classes for training. However, these methods may not be applicable for medical images due to the lack of annotations. To address this problem we make several contributions: (1) A novel self-supervised FSS framework for medical images in order to eliminate the requirement for annotations during training. Additionally, superpixel-based pseudo-labels are generated to provide supervision; (2) An adaptive local prototype pooling module pl…
Neural networks pre-trained on a self-supervision scheme have become the standard when operating in data rich environments with scarce annotations. As such, fine-tuning a model to a downstream task in a parameter-efficient but effective way, e.g. for a new set of classes in the case of semantic segmentation, is of increasing importance. In this work, we propose and investigate several contributions to achieve a parameter-efficient but effective adaptation for semantic segmentation on two medical imaging datasets. Relying on the recently popularized prompt tuning approach, we provide a prompt-a…
Institute of Information Science, Beijing Jiaotong University（北京交通大学信息科学研究所）；Beijing Key Laboratory of Advanced Information Science and Network Technology, Beijing Jiaotong University（北京交通大学先进信息科学与网络技术北京市重点实验室）；Department of Computer Science and Engineering, Michigan State University（密歇根州立大学计算机科学与工程系）
To bridge the gap between the source and target domains in unsupervised domain adaptation (UDA), the most common strategy puts focus on matching the marginal distributions in the feature space through adversarial learning. However, such category-agnostic global alignment lacks of exploiting the class-level joint distributions, causing the aligned distribution less discriminative. To address this issue, we propose in this paper a novel margin preserving self-paced contrastive Learning (MPSCL) model for cross-modal medical image segmentation. Unlike the conventional construction of contrastive p…
Illinois Institute of Technology（伊利诺伊理工学院）；University of Michigan（密歇根大学）
Recently, due to the increasing requirements of medical imaging applications and the professional requirements of annotating medical images, few-shot learning has gained increasing attention in the medical image semantic segmentation field. To perform segmentation with limited number of labeled medical images, most existing studies use Proto-typical Networks (PN) and have obtained compelling success. However, these approaches overlook the query image features extracted from the proposed representation network, failing to preserving the spatial connection between query and support images. In th…
DMAF-Net: An Effective Modality Rebalancing Framework for Incomplete Multi-Modal Medical Image Segmentation
College of Computer Science and Engineering, Chongqing University of Technology（重庆理工大学计算机科学与工程学院）；School of Computer Science and Engineering, Southeast University（东南大学计算机科学与工程学院）
Leveraging Labelled Data Knowledge: A Cooperative Rectification Learning Network for Semi-supervised 3D Medical Image Segmentation
School of Mechanical Engineering and Automation, Northeastern University（东北大学机械工程与自动化学院）；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）；Centre for Vision, Speech and Signal Processing (CVSSP), University of Surrey（萨里大学视觉、语音与信号处理中心）
A weakly-supervised semantic segmentation framework with a tied deconvolutional neural network is presented. Each deconvolution layer in the framework consists of unpooling and deconvolution operations. 'Unpooling' upsamples the input feature map based on unpooling switches defined by corresponding convolution layer's pooling operation. 'Deconvolution' convolves the input unpooled features by using convolutional weights tied with the corresponding convolution layer's convolution operation. The unpooling-deconvolution combination helps to eliminate less discriminative features in a feature extr…
Dual Cross-image Semantic Consistency with Self-aware Pseudo Labeling for Semi-supervised Medical Image Segmentation
School of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University（生物医学工程学院及先进医疗材料与器件国家重点实验室，上海科技大学）；Lingang Laboratory（临港实验室）；Department of Radiology, Stanford University（放射科，斯坦福大学）
Xiaohe Healthcare, ByteDance（字节跳动小荷健康）；The University of Hong Kong（香港大学）
Medical image classification has been widely adopted in medical image analysis. However, due to the difficulty of collecting and labeling data in the medical area, medical image datasets are usually highly-imbalanced. To address this problem, previous works utilized class samples as prior for re-weighting or re-sampling but the feature representation is usually still not discriminative enough. In this paper, we adopt the contrastive learning to tackle the long-tailed medical imbalance problem. Specifically, we first propose the category prototype and adversarial proto-instance to generate repr…
Semi-supervised segmentation remains challenging in medical imaging since the amount of annotated medical data is often scarce and there are many blurred pixels near the adhesive edges or in the low-contrast regions. To address the issues, we advocate to firstly constrain the consistency of pixels with and without strong perturbations to apply a sufficient smoothness constraint and further encourage the class-level separation to exploit the low-entropy regularization for the model training. Particularly, in this paper, we propose the SS-Net for semi-supervised medical image segmentation tasks,…
Diverse Normal Prototypes-Guided Contrastive Reconstruction for Medical Anomaly Detection
School of Mathematics, Shandong University（山东大学数学学院）；Department of Interventional Medicine and Minimally Invasive Oncology, The Second Qilu Hospital of Shandong University（山东大学第二齐鲁医院介入医学与微创肿瘤科）；Department of Neurosurgery, The Second Qilu Hospital of Shandong University（山东大学第二齐鲁医院神经外科）
