Semi-supervised learning (SSL) provides an effective means of reducing reliance on large-scale annotated datasets by leveraging unlabeled data. However, existing SSL methods often struggle with semantic ambiguity, especially under limited supervision. Recent studies have incorporated textual information to provide contextual guidance, yet most focus on feature fusion rather than emphasizing target semantics critical for segmentation. In this paper, we proposed a novel Text-anchored Visual Decoupling (TeViD) framework for semi-supervised medical image segmentation. TeViD is built upon a teacher-student architecture with a dual-decoder design that explicitly disentangles target and background representations using both labeled and unlabeled data. For unlabeled data, a reversed cross-supervision mechanism is introduced to enhance decoder diversity and semantic separation. Furthermore, two contrastive learning objectives are proposed: a teacher-guided visual contrastive loss and a text-anchored contrastive loss, both designed to reinforce semantic disentanglement from visual and textual perspectives. Extensive experiments on five public datasets (covering X-ray, pathology, ultrasound, MRI, and CT) demonstrate that TeViD consistently outperforms both standard SSL and text-enhanced SSL methods, achieving average improvements of 5.72% in Dice and 8.15% in mIoU over the second-best competitor. The code is available at: https://github.com/jgfiuuuu/TeViD.

Effective representation of Regions of Interest (ROI) and independent alignment of these ROIs can significantly enhance the performance of deformable medical image registration (DMIR). However, current learning-based DMIR methods have limitations. Unsupervised techniques disregard ROI representation and proceed directly with aligning pairs of images, while weakly-supervised methods heavily depend on label constraints to facilitate registration. To address these issues, we introduce a weakly-supervised ROI-based registration approach named EASR-DCN. Our method represents medical images through effective ROIs and achieves independent alignment of these ROIs without requiring labels. Specifically, we first used a Gaussian mixture model for intensity analysis to represent images using multiple effective ROIs with distinct intensities. Furthermore, we propose a novel Divide-and-Conquer Network (DCN) that processes ROIs through separate channels to independently align their features. The resulting sub-deformation fields are seamlessly integrated to generate a comprehensive displacement vector field. Extensive experiments were performed on three MRI and one CT datasets to showcase the superior accuracy and deformation reduction efficacy of our EASR-DCN. Compared to VoxelMorph, our EASR-DCN achieved improvements of 10.31% in the Dice score for brain MRI, 13.01% for cardiac MRI, and 5.75% for hippocampus MRI, highlighting its promising potential for clinical applications.

Computational pathology has emerged as a transformative paradigm by leveraging artificial intelligence to automate and enhance diagnostic procedures. However, existing models often target narrow tasks or specific tumor types, missing opportunities to unify diverse datasets and tasks through joint learning. In this work, we introduce PathBot, a foundation model tailored for comprehensive pathological image analysis. Central to PathBot is a ViT-Giant encoder with one billion parameters, the largest model to date trained on publicly available pathological data. We pre-train this encoder using a novel Masked Distillation Network (MDN) and an integrated learning strategy that combines contrastive and generative objectives. The pre-training leverages over 30 million image patches derived from 11,765 whole slide images (WSIs) across 32 cancer types in the Cancer Genome Atlas (TCGA). To evaluate its versatility, we pair the encoder with task-specific decoders for segmentation, detection, classification, and regression. Extensive experiments across 20 downstream tasks demonstrate that PathBot achieves state-of-the-art performance in most cases, showcasing its robustness and generalizability.

Medical Visual Question Answering (Med-VQA) aims to answer questions regarding the content of medical images, crucial for enhancing diagnostics and education in healthcare. However, progress in this field is hindered by data scarcity due to the resource-intensive nature of medical data annotation. While existing Med-VQA approaches often rely on pre-training to mitigate this issue, bridging the semantic gap between pre-trained models and specific tasks remains a significant challenge. This paper presents the Dynamic Semantic-Adaptive Prompting (DSAP) framework, leveraging prompt learning to enhance model performance in Med-VQA. To this end, we introduce two prompting strategies: Semantic Alignment Prompting (SAP) and Dynamic Question-Aware Prompting (DQAP). SAP prompts multi-modal inputs during fine-tuning, reducing the semantic gap by aligning model outputs with domain-specific contexts. Simultaneously, DQAP enhances answer selection by leveraging grammatical relationships between questions and answers, thereby improving accuracy and relevance. The DSAP framework was pre-trained on three datasets-ROCO, MedICaT, and MIMIC-CXR-and comprehensively evaluated against 15 existing Med-VQA models on three public datasets: VQA-RAD, SLAKE, and PathVQA. Our results demonstrate a substantial performance improvement, with DSAP achieving a 1.9% enhancement in average results across benchmarks. These findings underscore DSAP's effectiveness in addressing critical challenges in Med-VQA and suggest promising avenues for future developments in medical AI.

The scarcity of annotations has become a significant obstacle in training powerful deep-learning models for medical image segmentation, limiting their clinical application. To overcome this, semi-supervised learning that leverages abundant unlabeled data is highly desirable to enhance model training. However, most existing works still focus on specific medical tasks and underestimate the potential of learning across diverse tasks and datasets. In this paper, we propose a Versatile Semi-supervised framework (VerSemi) to present a new perspective that integrates various SSL tasks into a unified model with an extensive label space, exploiting more unlabeled data for semi-supervised medical image segmentation. Specifically, we introduce a dynamic task-prompted design to segment various targets from different datasets. Next, this unified model is used to identify the foreground regions from all labeled data, capturing cross-dataset semantics. Particularly, we create a synthetic task with a CutMix strategy to augment foreground targets within the expanded label space. To effectively utilize unlabeled data, we introduce a consistency constraint that aligns aggregated predictions from various tasks with those from the synthetic task, further guiding the model to accurately segment foreground regions during training. We evaluated our VerSemi framework against seven established SSL methods on four public benchmarking datasets. Our results suggest that VerSemi consistently outperforms all competing methods, beating the second-best method with a 2.69% average Dice gain on four datasets and setting a new state of the art for semi-supervised medical image segmentation. Code is available at https://github.com/maxwell0027/VerSemi.

Semi-supervised learning (SSL) has been proven beneficial for mitigating the issue of limited labeled data, especially on volumetric medical image segmentation. Unlike previous SSL methods which focus on exploring highly confident pseudo-labels or developing consistency regularization schemes, our empirical findings suggest that differential decoder features emerge naturally when two decoders strive to generate consistent predictions. Based on the observation, we first analyze the treasure of discrepancy in learning towards consistency, under both pseudo-labeling and consistency regularization settings, and subsequently propose a novel SSL method called LeFeD, which learns the feature-level discrepancies obtained from two decoders, by feeding such information as feedback signals to the encoder. The core design of LeFeD is to enlarge the discrepancies by training differential decoders, and then learn from the differential features iteratively. We evaluate LeFeD against eight state-of-the-art (SOTA) methods on three public datasets. Experiments show LeFeD surpasses competitors without any bells and whistles, such as uncertainty estimation and strong constraints, as well as setting a new state of the art for semi-supervised medical image segmentation. Code has been released at https://github.com/maxwell0027/LeFeD.

Distribution shift widely exists in medical images acquired from different medical centres and poses a significant obstacle to deploying the pretrained semantic segmentation model in real-world applications. Test-time adaptation has proven its effectiveness in tackling the cross-domain distribution shift during inference. However, most existing methods achieve adaptation by updating the pretrained models, rendering them susceptible to error accumulation and catastrophic forgetting when encountering a series of distribution shifts (i.e., under the continual test-time adaptation setup). To overcome these challenges caused by updating the models, in this paper, we freeze the pretrained model and propose the Visual Prompt-based Test-Time Adaptation (VPTTA) method to train a specific prompt for each test image to align the statistics in the batch normalization layers. Specifically, we present the low-frequency prompt, which is lightweight with only a few parameters and can be effectively trained in a single iteration. To enhance prompt initialization, we equip VPTTA with a memory bank to benefit the current prompt from previous ones. Additionally, we design a warm-up mechanism, which mixes source and target statistics to construct warm-up statistics, thereby facilitating the training process. Extensive experiments demonstrate the superiority of our VPTTA over other state-of-the-art methods on two medical image segmentation benchmark tasks. The code and weights of pretrained source models are available at https://github.com/Chen-Ziyang/VPTTA.

The rapid advances in deep learning-based computational pathology and radiology have demonstrated the promise of using whole slide images (WSIs) and radiology images for survival prediction in cancer patients. However, most image-based survival prediction methods are limited to using either histology or radiology alone, leaving integrated approaches across histology and radiology relatively underdeveloped. There are two main challenges in integrating WSIs and radiology images: (1) the gigapixel nature of WSIs and (2) the vast difference in spatial scales between WSIs and radiology images. To address these challenges, in this work, we propose an interpretable, weakly-supervised, multimodal learning framework, called Hierarchical Multimodal Co-Attention Transformer (HMCAT), to integrate WSIs and radiology images for survival prediction. Our approach first uses hierarchical feature extractors to capture various information including cellular features, cellular organization, and tissue phenotypes in WSIs. Then the hierarchical radiology-guided co- attention (HRCA) in HMCAT characterizes the multimodal interactions between hierarchical histology-based visual concepts and radiology features and learns hierarchical co- attention mappings for two modalities. Finally, HMCAT combines their complementary information into a multimodal risk score and discovers prognostic features from two modalities by multimodal interpretability. We apply our approach to two cancer datasets (365 WSIs with matched magnetic resonance [MR] images and 213 WSIs with matched computed tomography [CT] images). Our results demonstrate that the proposed HMCAT consistently achieves superior performance over the unimodal approaches trained on either histology or radiology data alone, as well as other state-of-the-art methods.