Partially-supervised learning can be challenging for segmentation due to the lack of supervision for unlabeled structures, and the methods directly applying fully-supervised learning could lead to incompatibility, meaning ground truth is not in the solution set of the optimization problem given the loss function. To address the challenge, we propose a deep compatible learning (DCL) framework, which trains a single multi-label segmentation network using images with only partial structures annotated. We first formulate the partially-supervised segmentation as an optimization problem compatible with missing labels, and prove its compatibility. Then, we equip the model with a conditional segmentation strategy, to propagate labels from multiple partially-annotated images to the target. Additionally, we propose a dual learning strategy, which learns two opposite mappings of label propagation simultaneously, to provide substantial supervision for unlabeled structures. The two strategies are formulated into compatible forms, termed as conditional compatibility and dual compatibility, respectively. We show this framework is generally applicable for conventional loss functions. The approach attains significant performance improvement over existing methods, especially in the situation where only a small training dataset is available. Results on three segmentation tasks have shown that the proposed framework could achieve performance matching fully-supervised models.
Foundation Model-guided Iteratively Prompting and Pseudo-Labeling for Partially Labeled Medical Image Segmentation
Department of Biomedical Engineering, Columbia University（哥伦比亚大学生物医学工程系）；Department of Radiation Oncology, Columbia University Irving Medical Center（哥伦比亚大学欧文医学中心放射肿瘤科）；Data Science Institute, Columbia University（哥伦比亚大学数据科学研究所）；Herbert Irving Comprehensive Cancer Center, Columbia University（哥伦比亚大学赫伯特·欧文综合癌症中心）；Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院放射肿瘤科）
Partially Supervised Unpaired Multi-Modal Learning for Label-Efficient Medical Image Segmentation
Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR)（高性能计算研究所（A*STAR））；The Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University（山东大学-新加坡南洋理工大学人工智能联合研究中心）
University of Oxford（牛津大学）；UiT The Arctic University of Norway（挪威北极大学）；Sun Yat-sen University（中山大学）；Carnegie Mellon University（卡内基梅隆大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
The data-driven nature of deep learning (DL) models for semantic segmentation requires a large number of pixel-level annotations. However, large-scale and fully labeled medical datasets are often unavailable for practical tasks. Recently, partially supervised methods have been proposed to utilize images with incomplete labels in the medical domain. To bridge the methodological gaps in partially supervised learning (PSL) under data scarcity, we propose Vicinal Labels Under Uncertainty (VLUU), a simple yet efficient framework utilizing the human structure similarity for partially supervised medi…
Northwestern Polytechnical University（西北工业大学）；The University of Adelaide（阿德莱德大学）
Due to the intensive cost of labor and expertise in annotating 3D medical images at a voxel level, most benchmark datasets are equipped with the annotations of only one type of organs and/or tumors, resulting in the so-called partially labeling issue. To address this, we propose a dynamic on-demand network (DoDNet) that learns to segment multiple organs and tumors on partially labeled datasets. DoDNet consists of a shared encoder-decoder architecture, a task encoding module, a controller for generating dynamic convolution filters, and a single but dynamic segmentation head. The information of …
Deep Convolutional Neural Networks (CNNs) i.e. Residual Networks (ResNets) have been used successfully for many computer vision tasks, but are difficult to scale to 3D volumetric medical data. Memory is increasingly often the bottleneck when training 3D Convolutional Neural Networks (CNNs). Recently, invertible neural networks have been applied to significantly reduce activation memory footprint when training neural networks with backpropagation thanks to the invertible functions that allow retrieving its input from its output without storing intermediate activations in memory to perform the b…
Northwestern Polytechnical University（西北工业大学）；The University of Adelaide（阿德莱德大学）；Alibaba DAMO Academy（阿里巴巴达摩院）；Zhejiang University（浙江大学）
Medical image benchmarks for the segmentation of organs and tumors suffer from the partially labeling issue due to its intensive cost of labor and expertise. Current mainstream approaches follow the practice of one network solving one task. With this pipeline, not only the performance is limited by the typically small dataset of a single task, but also the computation cost linearly increases with the number of tasks. To address this, we propose a Transformer based dynamic on-demand network (TransDoDNet) that learns to segment organs and tumors on multiple partially labeled datasets. Specifical…
Recently, several Bayesian deep learning methods have been proposed for semi-supervised medical image segmentation. Although they have achieved promising results on medical benchmarks, some problems are still existing. Firstly, their overall architectures belong to the discriminative models, and hence, in the early stage of training, they only use labeled data for training, which might make them overfit to the labeled data. Secondly, in fact, they are only partially based on Bayesian deep learning, as their overall architectures are not designed under the Bayesian framework. However, unifying …
Xidian University（西安电子科技大学）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）
3D image segmentation is one of the most important and ubiquitous problems in medical image processing. It provides detailed quantitative analysis for accurate disease diagnosis, abnormal detection, and classification. Currently deep learning algorithms are widely used in medical image segmentation, most algorithms trained models with full annotated datasets. However, obtaining medical image datasets is very difficult and expensive, and full annotation of 3D medical image is a monotonous and time-consuming work. Partially labelling informative slices in 3D images will be a great relief of manu…
Stitching, Fine-tuning, Re-training: A SAM-enabled Framework for Semi-supervised 3D Medical Image Segmentation
State Key Laboratory for Novel Software Technology, Nanjing University（南京大学现代软件工程国家重点实验室）；National Institute of Healthcare Data Science, Nanjing University（南京大学医疗数据科学国家研究院）
SAM-aware Test-time Adaptation for Universal Medical Image Segmentation
Department of Data Science & AI, Faculty of Information Technology, Monash University（数据科学与人工智能系，信息科技学院，莫纳什大学）；Department of Computing and Department of Brain Sciences, Imperial College London（计算系和脑科学系，伦敦帝国学院）；Mohamed bin Zayed University of Artificial Intelligence（莫卧儿·本·扎耶德人工智能大学）；Department of Radiation Oncology, UT Southwestern Medical Center（放射肿瘤科，德克萨斯西南医学中心）
Universal Topology Refinement for Medical Image Segmentation with Polynomial Feature Synthesis
Imperial College London（帝国理工学院）；University of Oxford（牛津大学）；Klinikum rechts der Isar, Technical University of Munich（慕尼黑工业大学右侧医院）；Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）
R$^{2}$Seg: Training-Free OOD Medical Tumor Segmentation via Anatomical Reasoning and Statistical Rejection
Carnegie Mellon University（卡内基梅隆大学）；University of Cambridge（剑桥大学）；Zhejiang University（浙江大学）；ETH Zurich（苏黎世联邦理工学院）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
Clinical Imaging Research Centre, Yong Loo Lin School of Medicine, National University of Singapore（新加坡国立大学杨潞龄医学院临床影像研究中心）；Savana Medica（萨瓦纳医疗公司）；Department of Orthopaedic Surgery, National University Hospital, National University Health System（新加坡国立大学卫生系统国立大学医院骨科）；Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore（新加坡国立大学杨潞龄医学院外科）；BioIonic Currents Electromagnetic Pulsing Systems Laboratory, BICEPS, National University of Singapore（新加坡国立大学生物离子电流电磁脉冲系统实验室）
Objective: Medical image datasets with pixel-level labels tend to have a limited number of organ or tissue label classes annotated, even when the images have wide anatomical coverage. With supervised learning, multiple classifiers are usually needed given these partially annotated datasets. In this work, we propose a set of strategies to train one single classifier in segmenting all label classes that are heterogeneously annotated across multiple datasets without moving into semi-supervised learning. Methods: Masks were first created from each label image through a process we termed presence m…
University of Oxford（牛津大学）；UiT The Arctic University of Norway（挪威北极大学）
Using decentralized data for federated training is one promising emerging research direction for alleviating data scarcity in the medical domain. However, in contrast to large-scale fully labeled data commonly seen in general object recognition tasks, the local medical datasets are more likely to only have images annotated for a subset of classes of interest due to high annotation costs. In this paper, we consider a practical yet under-explored problem, where underrepresented classes only have few labeled instances available and only exist in a few clients of the federated system. We show that…
Due to the high human cost of annotation, it is non-trivial to curate a large-scale medical dataset that is fully labeled for all classes of interest. Instead, it would be convenient to collect multiple small partially labeled datasets from different matching sources, where the medical images may have only been annotated for a subset of classes of interest. This paper offers an empirical understanding of an under-explored problem, namely partially supervised multi-label classification (PSMLC), where a multi-label classifier is trained with only partially labeled medical images. In contrast to …
University of Porto（波尔图大学）；INESC TEC（葡萄牙系统与计算机工程研究所）
The use of Deep Learning in the medical field is hindered by the lack of interpretability. Case-based interpretability strategies can provide intuitive explanations for deep learning models' decisions, thus, enhancing trust. However, the resulting explanations threaten patient privacy, motivating the development of privacy-preserving methods compatible with the specifics of medical data. In this work, we analyze existing privacy-preserving methods and their respective capacity to anonymize medical data while preserving disease-related semantic features. We find that the PPRL-VGAN deep learning…
Federated Learning with Partially Labeled Data: A Conditional Distillation Approach
Department of Computer Science and Information Engineering, National Taiwan University（计算机科学与信息工程系，国立台湾大学）；Graduate School of Informatics, Nagoya University（资讯研究生院，名古屋大学）；Institute of Applied Mathematical Sciences, National Taiwan University（应用数学科学研究所，国立台湾大学）；NVIDIA Corporation（NVIDIA公司）
The University of Texas Arlington（德克萨斯大学阿灵顿分校）；RIKEN（理化学研究所）；University of Tokyo（东京大学）；National Cancer Center Research Institute（国立癌症中心研究所）；National Library of Medicine - National Institutes of Health（美国国立卫生研究院国家医学图书馆）；Temple University（天普大学）
Medical visual question answering (VQA) aims to answer clinically relevant questions regarding input medical images. This technique has the potential to improve the efficiency of medical professionals while relieving the burden on the public health system, particularly in resource-poor countries. Existing medical VQA methods tend to encode medical images and learn the correspondence between visual features and questions without exploiting the spatial, semantic, or medical knowledge behind them. This is partially because of the small size of the current medical VQA dataset, which often includes…
DiffuSAM: Diffusion-Based Prompt-Free SAM2 for Few-Shot and Source-Free Medical Image Segmentation
School of Electrical Engineering, Tel Aviv University, Israel（特拉维夫大学电气工程学院）；School of Biomedical Engineering, Tel Aviv University, Israel（特拉维夫大学生物医学工程学院）
Multi-atlas segmentation is a widely used tool in medical image analysis, providing robust and accurate results by learning from annotated atlas datasets. However, the availability of fully annotated atlas images for training is limited due to the time required for the labelling task. Segmentation methods requiring only a proportion of each atlas image to be labelled could therefore reduce the workload on expert raters tasked with annotating atlas images. To address this issue, we first re-examine the labelling problem common in many existing approaches and formulate its solution in terms of a…
Fully annotated large-scale medical image datasets are highly valuable. However, because labeling medical images is tedious and requires specialized knowledge, the large-scale datasets available often have missing annotation issues. For instance, DeepLesion, a large-scale CT image dataset with labels for various kinds of lesions, is reported to have a missing annotation rate of 50\%. Directly training a lesion detector on it would suffer from false negative supervision caused by unannotated lesions. To address this issue, previous works have used sophisticated multi-stage strategies to switch …
University of Pittsburgh（匹兹堡大学）；The University of Melbourne（墨尔本大学）；Carnegie Mellon University（卡内基梅隆大学）
Deep learning has achieved remarkable success in medicalimage segmentation, but it usually requires a large numberof images labeled with fine-grained segmentation masks, andthe annotation of these masks can be very expensive andtime-consuming. Therefore, recent methods try to use un-supervised domain adaptation (UDA) methods to borrow in-formation from labeled data from other datasets (source do-mains) to a new dataset (target domain). However, due tothe absence of labels in the target domain, the performance ofUDA methods is much worse than that of the fully supervisedmethod. In this paper, w…
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；Southeast University（东南大学）
Annotating multiple organs in medical images is both costly and time-consuming; therefore, existing multi-organ datasets with labels are often low in sample size and mostly partially labeled, that is, a dataset has a few organs labeled but not all organs. In this paper, we investigate how to learn a single multi-organ segmentation network from a union of such datasets. To this end, we propose two types of novel loss function, particularly designed for this scenario: (i) marginal loss and (ii) exclusion loss. Because the background label for a partially labeled image is, in fact, a `merged' lab…
Cross-Dimensional Medical Self-Supervised Representation Learning Based on a Pseudo-3D Transformation
School of Computer Science, Peking University（北京大学计算机学院）；Deepwise AI Lab（深度智源人工智能实验室）；Harvard Medical School（哈佛医学院）；CFCS, School of Computer Science, Peking University（北京大学计算机学院CFCS）；Institute for Artificial Intelligence, Peking University（北京大学人工智能研究院）；Children’s Hospital of Zhejiang University School of Medicine（浙江大学医学院附属儿童医院）；Department of Computer Science, The University of Hong Kong（香港大学计算机系）
LIPN-UMR-CNRS 7030（法国国家科学研究中心7030联合研究单元LIPN实验室）；Sorbonne University Paris Nord（巴黎北索邦大学）；Amirkabir University of Technology（阿米尔卡比尔理工大学）；Laboratory of Images, Signals and Intelligent Systems（图像、信号与智能系统实验室）；Université Paris-Est Créteil（巴黎东克雷泰伊大学）
Manual segmentation of medical images (e.g., segmenting tumors in CT scans) is a high-effort task that can be accelerated with machine learning techniques. However, selecting the right segmentation approach depends on the evaluation function, particularly in medical image segmentation where we must deal with dependency between voxels. For instance, in contrast to classical systems where the predictions are either correct or incorrect, predictions in medical image segmentation may be partially correct and incorrect simultaneously. In this paper, we explore this expressiveness to extract the use…
Zhejiang University（浙江大学）；University of Notre Dame（圣母大学）
In clinical practice, medical image interpretation often involves multi-labeled classification, since the affected parts of a patient tend to present multiple symptoms or comorbidities. Recently, deep learning based frameworks have attained expert-level performance on medical image interpretation, which can be attributed partially to large amounts of accurate annotations. However, manually annotating massive amounts of medical images is impractical, while automatic annotation is fast but imprecise (possibly introducing corrupted labels). In this work, we propose a new regularization approach, …
With the advents of deep learning, improved image classification with complex discriminative models has been made possible. However, such deep models with increased complexity require a huge set of labeled samples to generalize the training. Such classification models can easily overfit when applied for medical images because of limited training data, which is a common problem in the field of medical image analysis. This paper proposes and investigates a reinforced classifier for improving the generalization under a few available training data. Partially following the idea of reinforcement lea…
The outbreak of COVID-19 Disease due to the novel coronavirus has caused a shortage of medical resources. To aid and accelerate the diagnosis process, automatic diagnosis of COVID-19 via deep learning models has recently been explored by researchers across the world. While different data-driven deep learning models have been developed to mitigate the diagnosis of COVID-19, the data itself is still scarce due to patient privacy concerns. Federated Learning (FL) is a natural solution because it allows different organizations to cooperatively learn an effective deep learning model without sharing…
University of Electronic Science and Technology of China（电子科技大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Johns Hopkins University（约翰斯·霍普金斯大学）；West China Hospital, Sichuan University（四川大学华西医院）
Background and Objective: Open-source deep learning toolkits are one of the driving forces for developing medical image segmentation models. Existing toolkits mainly focus on fully supervised segmentation and require full and accurate pixel-level annotations that are time-consuming and difficult to acquire for segmentation tasks, which makes learning from imperfect labels highly desired for reducing the annotation cost. We aim to develop a new deep learning toolkit to support annotation-efficient learning for medical image segmentation. Methods: Our proposed toolkit named PyMIC is a modular de…
NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal（蒙特利尔理工大学生物医学工程研究所神经多实验室）；AIMI, Stanford University（斯坦福大学AIMI）；Mila, Quebec AI Institute（魁北克人工智能研究所Mila）；Polytechnique Montreal（蒙特利尔理工大学）
ivadomed is an open-source Python package for designing, end-to-end training, and evaluating deep learning models applied to medical imaging data. The package includes APIs, command-line tools, documentation, and tutorials. ivadomed also includes pre-trained models such as spinal tumor segmentation and vertebral labeling. Original features of ivadomed include a data loader that can parse image metadata (e.g., acquisition parameters, image contrast, resolution) and subject metadata (e.g., pathology, age, sex) for custom data splitting or extra information during training and evaluation. Any dat…
