Medical contrastive Vision-Language Pre-training (VLP) has emerged as a promising approach, enabling models to learn joint representations from paired medical images and radiology reports. Despite existing methods exploring local visual representation learning techniques, they often fall short in local alignment and knowledge infusion, e.g., uniform token treatment and isolated knowledge assignment. To address these issues, we propose a novel Fine-grained Knowledge-Guided Alignment (FKGA) framework for medical VLP. Specifically, we propose a Fine-grained Disease Knowledge Integration (FDKI) module to inject detailed disease descriptions into corresponding disease tokens in reports. Based on these semantic-enriched tokens, we introduce global instance-wise and local token-wise contrastive learning to further align the semantically related visual and textual modalities. In contrast to previous local visual representation learning methods, our design of semantic-enriched token alignment and context-preserved knowledge infusion enhances the semantic understanding of diseases. Extensive experimental results on five downstream tasks demonstrate that our proposed method outperforms other state-of-the-art methods across seven datasets.

Referring Medical Image Sequence Segmentation (Ref-MISS) is a novel and challenging task that aims to segment anatomical structures in medical image sequences (e.g., endoscopy, ultrasound, CT, and MRI) based on natural language descriptions. Existing 2D and 3D segmentation models struggle to explicitly track objects of interest across medical image sequences, and lack support for interactive, text-driven guidance. To address these limitations, we propose Text-Promptable Propagation (TPP), which enables the recognition of referred objects through cross-modal referring interaction, and maintains continuous tracking across the sequence via Transformer-based triple propagation, using text embeddings as queries. To support this task, we curate a large-scale benchmark, Ref-MISS-Bench, which covers 4 imaging modalities and 20 different organs and lesions. Experimental results on this benchmark demonstrate that TPP consistently outperforms state-of-the-art methods in both medical segmentation and referring video object segmentation. Code and data are available at https://github.com/yuanruntian/TPP.

Chest X-rays (CXRs) are the most frequently performed imaging examinations in clinical settings. Recent advancements in Medical Large Multimodal Models (MLMMs) have enabled automated CXR interpretation, improving diagnostic accuracy and efficiency. However, despite their strong visual understanding, current MLMMs still face two major challenges: (1) insufficient region-level understanding and interaction, and (2) limited accuracy and interpretability due to single-step prediction. In this paper, we address these challenges by empowering MLMMs with anatomy-centric reasoning capabilities to enhance their interactivity and explainability. Specifically, we propose an Anatomical Ontology-Guided Reasoning (AOR) framework that accommodates both textual and optional visual prompts, centered on region-level information to enable multimodal multi-step reasoning. We also develop AOR-Instruction, a large instruction dataset for MLMMs training, under the guidance of expert physicians. Our experiments demonstrate AOR's superior performance in both Visual Question Answering (VQA) and report generation tasks. Code and data are available at: https://github.com/Liqq1/AOR.

Automatic generation of radiology reports holds crucial clinical value, as it can alleviate substantial workload on radiologists and remind less experienced ones of potential anomalies. Despite the remarkable performance of various image captioning methods in the natural image field, generating accurate reports for medical images still faces challenges, i.e., disparities in visual and textual data, and lack of accurate domain knowledge. To address these issues, we propose an enhanced knowledge injection framework, which utilizes two branches to extract different types of knowledge. The Weighted Concept Knowledge (WCK) branch is responsible for introducing clinical medical concepts weighted by TF-IDF scores. The Multimodal Retrieval Knowledge (MRK) branch extracts triplets from similar reports, emphasizing crucial clinical information related to entity positions and existence. By integrating this finer-grained and well-structured knowledge with the current image, we are able to leverage the multi-source knowledge gain to ultimately facilitate more accurate report generation. Extensive experiments have been conducted on two public benchmarks, demonstrating that our method achieves superior performance over other state-of-the-art methods. Ablation studies further validate the effectiveness of two extracted knowledge sources.

In clinical practice, medical imaging techniques include 2D video-based examinations that capture sequential scans, and 3D volumetric imaging that forms a comprehensive 3D representation from a stack of 2D slices. The medical image sequences produced by the above techniques provide valuable spatio-temporal characteristics for analysis and segmentation, but the annotation of image sequences is extremely time-consuming and labor-intensive. To exploit the coherence and address the scarcity of labeled data, we propose a novel semi-supervised semantic segmentation framework for medical image sequences, which consists of a conditional network and a denoising network. Specifically, we embed a Sequential Feature Reconstruction module into both networks. This module reconstructs the target frame from contiguous frames and captures their shared visual features. Guided by the context-enhancing information from the conditioning network, the denoising network suppresses background noise via a Diffusion-based Noise Elimination module. Extensive experiments are conducted on 2D and 3D tasks, including cardiac segmentation, polyp segmentation, placenta vessel segmentation and abdomen multi-organ segmentation. The results show our method is superior to existing semi-supervised methods and exhibits advantages over fully-supervised medical image segmentation methods with only 1/2 labeled data, validating its effectiveness and generalization ability.

Automatic and accurate breast mass segmentation plays a crucial role in the early diagnosis of breast cancer. However, it has been a challenging task for two main reasons: (1) Breast masses are diverse; and (2) The boundaries of masses are ambiguous. To address these problems, we propose a Spatial-Correlated and Shape-Guided Network (SCSGNet), which combines global context extraction with local boundary refinement. Specifically, the high-level features are aggregated to produce a global map as the initial guidance area, and a Series-Parallel Feature Fusion (SPFF) module is added to capture masses of different shapes and sizes. Besides, we design a Dynamic Long-range Correlation Capture (DLCC) module to capture the spatial correlation of masses at different positions. Finally, we devise a Triplet Attention Guide (TAG) module to iteratively update the feature map and refine the boundary. Experiments on two public datasets demonstrate that our method achieves superior performance over other state-of-the-art methods.

Endoscopic polyp segmentation plays a fundamental role in the diagnosis and treatment of colorectal cancer. However, polyp segmentation often suffers from limited accuracy due to its large variations in appearance, blurry boundary and severe imbalanced illumination. In this paper, we propose a novel Translation Assisted Segmentation Network (TASNet) for polyp segmentation of single-modality endoscopic images. It consists of two branches, i.e. an image-to-image translation branch and an image segmentation branch. These two branches communicate via a shared encoder. For the image-to-image translation branch, a Color Reversal Strategy is established to treat the original image as source image and synthesize target images. Moreover, we introduce a Random Color Reversal Synthesis module for progressive segmentation. Extensive experiments show that our framework achieves superior performance than state-of-the-art methods on five widely-used endoscopic image datasets.

Medical image segmentation plays a critical role in computer-aided diagnosis, while the diversity and complexity of medical images make it difficult to segment precisely. In practice, medical images of specific modalities (e.g. Magnetic Resonance Imaging, Colonoscopy and Ultrasonography) are collected as sequences independently for every patient. However, 1) there exists few works exploiting sequence information among successive frames, neglecting inter-frame relationships that are useful to locate target objects; 2) the performance of medical image segmentation is limited to the low contrast or blurry boundary of medical images, and intra-frame dependencies are not fully explored. Thus in this paper, we propose MedSeq for segmenting objects of interest in medical image sequences. Following the “locate-then-refine” paradigm, we locate target regions by modeling cross-frame relationships and then perform refinement on coarse masks. More specifically, we design a Cross-frame Attention module to learn correlations among frames, taking advantages of their similar appearances. For refinement, we propose a novel Boundary-aware Transformer to improve the segmentation of boundary patches. Extensive experiments are conducted on benchmark datasets of Cardiac Segmentation and Video Polyp Segmentation. Our method achieves superior performance over the state-of-the-art methods.