Early Access

Medical image reconstruction from undersampled acquisitions is an ill-posed inverse problem requiring accurate recovery of anatomical structures from incomplete measurements. Physics-driven (PD) network models have gained prominence for this task by integrating data-consistency mechanisms with learned priors, enabling improved performance over purely data-driven approaches. However, reconstruction...

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Automated report generation is limited to static, single-image analysis, failing to address the critical clinical need for longitudinal comparison in monitoring disease progression and treatment efficacy. To bridge this gap, we introduce the new task of Change Radiology Report Generation (CRRG) which aims to automatically generate a comparative radiology report describing interval changes between ...

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Robotic-assisted surgery (RAS) is established in clinical practice, and automated surgical skill assessment utilizing multimodal data offers transformative potential for surgical analytics and education. However, developing effective multimodal methods remains challenging due to the task complexity, limited annotated datasets and insufficient techniques for cross-modal information fusion. Existing...

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Vision–language pretraining (VLP) has emerged as a powerful paradigm in medical image analysis, enabling representation learning from large-scale image–text pairs without relying on expensive expertise annotations. However, these challenges are particularly pronounced when VLP is applied to dermatology. The inherent noise and sparsity of web-crawled data hinder effective pretraining, while multifa...

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Foundation models (FMs) are rapidly reshaping medical imaging, shifting the field from narrowly trained, task-specific networks toward large, generalpurpose models that can be adapted across modalities, anatomies, and clinical tasks. In this review, we synthesize the emerging landscape of medical imaging FMs along three major axes: underlying core components of FMs, medical imaging applications of...

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The increasing use of vision-language models (VLMs) in healthcare applications presents great challenges related to hallucinations, in which the models may generate seemingly plausible results that are in fact incorrect. Such hallucinations can jeopardize clinical decision making, potentially harming the diagnosis and treatments. In this work, we propose MedHallTune, a large-scale benchmark design...

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Generalizable dense feature matching in endoscopic images is crucial for robot-assisted tasks, including 3D reconstruction, navigation, and surgical scene understanding. Yet, it remains challenging due to difficult visual conditions (e.g., weak textures, large viewpoint variations) and scarce annotated data. To address these challenges, we propose EndoMatcher, a generalizable endoscopic image matc...

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Accurate tissue point tracking in endoscopic videos is crucial for robotic-assisted surgical navigation and scene understanding, yet remains challenging due to complex tissue deformations, instrument occlusions, and the scarcity of dense trajectory annotations. Existing methods struggle with long-term tracking robustness under these conditions because they primarily rely on local motion cues that ...

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Accurate depth estimation from endoscopic images is fundamental to surgical navigation, augmented reality, and robotic assistance, yet depth foundation models trained on natural images degrade severely on surgical endoscopy, and existing adaptation methods require surgical training data for each new camera and clinical environment. We propose SurgDepth, a test-time adaptation (TTA) framework that ...

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Accurate MRI-based brain tumor analysis requires not only tumor subtype classification but also localization at an anatomical granularity that is consistent with radiology reports. Most vision-only methods address localization and classification as separate label-prediction tasks, and therefore provide limited alignment with the fine-grained anatomical semantics used in routine reporting. To addre...

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Sparse-view computed tomography (CT) effectively reduces radiation exposure, yet it degrades image signal-to-noise ratio (SNR) and compromises the reliability of clinical diagnosis. Deep unrolling networks, which integrate the merits of optimization-based and data-driven paradigms, have achieved promising performance for sparse-view CT reconstruction. However, existing learned data consistency (DC...

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Pretrained segmentation models for cardiac magnetic resonance imaging (MRI) often fail to generalize across imaging sequences due to substantial contrast variations. These variations arise from different imaging protocols, yet fundamentally, all contrasts are governed by the same underlying tissue properties, primarily captured by three components: the magnetization strength (M0), T1, and T2. Buil...

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Multi-instance learning (MIL) has significantly advanced AI-assisted cancer diagnosis using histochemically stained whole-slide images (WSIs). However, acquiring such WSIs is time-consuming, labor-intensive, and environmentally unfriendly. To address this, we propose using unlabeled autofluorescence (UAF) WSIs as a cost-effective alternative for MIL-based diagnosis. We introduce a dedicated UAF WS...

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Photon Counting CT (PCCT) employs photon counting detectors (PCDs) which possess the unique ability to partition different energy ranges of the incoming photons. This fundamental function allows PCCT to differentiate materials in objects by fully utilizing the spectrum information through material decomposition. However, due to the differences in materials, fabrication and bonding, the spectrum re...

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This paper describes a new spatial correspondence representation based on paired regions-of-interest (ROIs), for medical image registration. The distinct properties of the proposed ROI-based correspondence are discussed, in the context of potential benefits in clinical applications following image registration, compared with alternative correspondence-representing approaches, such as those based o...

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Few-shot Medical Image Segmentation (FS-MIS) has garnered increasing attention for its ability to reduce reliance on large-scale pixel-wise annotations. How ever, most existing methods rely solely on single-level encoder outputs, neglecting the complementary roles of hi erarchical features in contour refinement and regional dis crimination. This limitation often results in boundary ambi guity and ...

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Interventional radiology (IR) requires joint reasoning over procedural images and domain-specific clinical knowledge. Existing medical retrieval-augmented generation (RAG) methods are mainly text-oriented or designed for general medical vision-language tasks, and therefore remain limited in retrieving fine-grained visual-textual evidence for IR scenarios. To address this limitation, we present Pro...

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Accurate T-staging is crucial for guiding personalized treatment strategies for laryngopharyngeal cancer. However, current clinical practice relies on invasive biopsy procedures, whereas CT-based staging remains challenging due to the complex patterns of tumor invasion. Recent computer-aided approaches face two key challenges: 1) Structural relationship modeling: existing methods underrepresent an...

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Multimodal large language models (MLLMs) have recently demonstrated remarkable reasoning abilities under reinforcement learning (RL) paradigm. However, most existing multimodal medical reasoning models focus on basic reasoning, which refers to shallow inference based on visual feature matching. In contrast, real-world clinical diagnosis extends beyond basic reasoning, demanding complex reasoning t...

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Skull stripping is a critical preprocessing step for reliable neuroimaging analysis. Although recent deep learning methods have made remarkable progress in accurate brain extraction, their generalization capability remains limited across heterogeneous imaging protocols and diverse pathological conditions. This limitation arises from their predominant reliance on voxel-intensity information without...

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Prediction of genetic biomarkers, e.g., microsatellite instability in colorectal cancer is crucial for clinical decision making. However, two primary challenges hamper accurate prediction: (1) It is difficult to construct a pathology-aware representation involving the complex interconnections among pathological components. (2) Whole slide images contain a large proportion of areas unrelated to gen...

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Due to their high resolution, pathology images incur substantial storage costs. Although most vendor formats have already adopted lossy compression (typically JPEG) to process such images, the storage requirements remain considerable. However, achieving further reductions in storage requirements while ensuring truly loss-less recompression of already lossy-compressed JPEG images remains a critical...

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Magnetic Resonance Imaging (MRI) is an inherently multi-contrast modality, where cross-contrast priors can be exploited to improve image reconstruction from undersampled data. Recently, diffusion models have shown remarkable performance in MRI reconstruction. However, they still struggle to effectively utilize such priors, mainly because existing methods rely on feature-level fusion in image or la...

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Spatial transcriptomics (ST) provides essential spatial context by mapping gene expression within tissue, enabling detailed study of cellular heterogeneity and tissue organization. However, aligning ST data with histology images poses challenges due to inherent spatial distortions and modality-specific variations. Existing methods largely rely on direct alignment, which often fails to capture comp...

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Fluorescence Molecular Tomography is a promising technique for non-invasive 3D visualization of fluorescent probes, but its reconstruction remains challenging due to the inherent ill-posedness and the necessity of adopting presumed tissue optical properties, whose exact ground-truth values are arduous to acquire. Emerging deep learning approaches have shown promising reconstruction results with hi...

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