Early Access

Osteoarthritis (OA) is a debilitating joint disease in which early microstructural and compositional changes in articular cartilage (AC) are challenging to detect with current diagnostic tools. We hypothesized that multispectral imaging (MSI) could capture optical signatures that predict key AC properties. To test this, we developed and evaluated an MSI-based approach for estimating tissue thickness, proteoglycan (PG) content, collagen fiber orientation, and cell morphological properties (area and circularity). Reflectance images of bovine patellar AC were acquired using a custom-built MSI system operating at six wavelengths (550–970 nm), and machine learning models were trained to predict the targeted markers. The models yielded reliable estimates for AC thickness, whereas for PG content, collagen fiber orientation, and cell circularity, they achieved moderate accuracy. This work suggests that MSI holds promise as a label-free tool for characterizing AC and detecting early degenerative changes.

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Spatial Transcriptomics (ST) technology detects gene expression from tissue biopsies, playing an emerging role in cancer diagnosis and precision medicine. However, the high cost of ST technology limits its broader application. Recently, deep learning approaches have provided insight into predicting gene expression based on H&E-stained histopathology images. Nevertheless, the relationship between morphological features and gene expression is highly complex. To address these challenges, we propose DiffBulk, a novel two-stage framework that leverages conditional diffusion models to learn expressive image representations enriched with gene expression information. In the first stage, we introduce a gene-to-image conditional diffusion model equipped with a permutationinvariant open-embedding gene encoder, which enables unified training across diverse gene panels. In the second stage, diffusion-derived features are fused with representations from a pathology foundation model, effectively bridging the domain gap and improving downstream gene expression prediction. We evaluate DiffBulk on high-quality Xenium ST data curated from the HEST dataset and the CrunchDAO challenge, constructing tile-level pseudo-bulk datasets for training and evaluation. Extensive experiments demonstrate that DiffBulk consistently outperforms state-of-the-art baselines across all metrics for gene expression prediction. These findings highlight the potential of diffusion-based gene-image representation learning and suggest promising directions for future research.

空间转录组学（ST）技术从组织活检中检测基因表达，在癌症诊断和精准医学中发挥着新兴作用。然而，ST技术的高成本限制了其更广泛的应用。最近，深度学习方法为基于H&E染色组织病理学图像预测基因表达提供了思路。然而，m…

3D reconstruction of the Right Ventricle (RV) from echocardiograms is crucial for accurate clinical evaluation of cardiac function. However, existing methods are hindered by the complex RV anatomy and the incomplete spatial information inherent in 2D multi-view echocardiograms. Therefore, we propose RVDeformer, a sparse point cloud-guided framework for RV 3D reconstruction. RVDeformer reformulates the reconstruction task as a mesh deformation problem, learning to deform a predefined template mesh to match the target structure under the guidance of the sparse anatomical point cloud. Specifically, this framework employs the end-to-end neural network RVDeformNet to extract the features of the point cloud and template mesh for predicting the displacement of each mesh vertex. We design a point cloud-mesh fusion module that can effectively align and fuse features from the two modalities to enhance the representation ability of the model. We conduct extensive validation on a clinical dataset of 1,278 cases and demonstrate that RVDeformer outperforms existing state-of-the-art methods, achieving a Chamfer Distance (CD) of 2.24±0.55 mm, an F1-score of 0.74±0.10 at the 3 mm threshold, and a Volumetric Similarity (VS) of 91.53±2.28%, with significant potential for clinical applications. The code is available at https://github.com/onezh95/RVDeformer.

从超声心动图中进行右心室（RV）的三维重建对于准确评估心脏功能至关重要。然而，现有方法受限于复杂的RV解剖结构以及二维多视图超声心动图固有的空间信息不完整。因此，我们提出了RVDeformer，一种稀疏点云引导的RV三维重建框架。RVDeformer重新表述了...

Pulmonary Arterial Wedge Pressure (PAWP) is an essential cardiovascular hemodynamics marker to detect heart failure. In clinical practice, Right Heart Catheterization is considered a gold standard for assessing cardiac hemodynamics while non-invasive methods are often needed to screen high-risk patients from a large population. In this paper, we propose a multimodal learning pipeline to predict PAWP marker. We utilize complementary information from Cardiac Magnetic Resonance Imaging (CMR) scans (short-axis and four-chamber) and Electronic Health Records (EHRs). We extract spatio-temporal features from CMR scans using tensor-based learning. We propose a graph attention network to select important EHR features for prediction, where we model subjects as graph nodes and feature relationships as graph edges using the attention mechanism. We design four feature fusion strategies: early, intermediate, late, and hybrid fusion. With a linear classifier and linear fusion strategies, our pipeline is interpretable. We validate our pipeline on a large dataset of 2, 641 subjects from our ASPIRE registry. The comparative study against state-of-the-art methods confirms the superiority of our pipeline. The decision curve analysis further validates that our pipeline can be applied to screen a large population. The code is available at https://github.com/prasunc/ hemodynamics.

肺动脉楔压（PAWP）是检测心力衰竭的重要心血管血流动力学标志物。在临床实践中，右心导管检查被认为是评估心脏血流动力学的金标准，但通常需要无创方法从大量人群中筛查高风险患者。本文提出了一种多模态学习流程来预测PA...

With the rapid development of pathology foundation models, there is a growing demand for efficient fine-tuning strategies tailored to downstream tasks. However, existing parameter-efficient fine-tuning approaches are largely task-agnostic and exhibit limited generalization to histopathological images, particularly for unseen cancers and stains, due to substantial stain variability and the complexity of tissue microenvironments. To address these challenges, we present Histopathology-induced Adapter (HiAdapter), which incorporates domain-specific insights into staining and imaging mechanisms of histopathology. HiAdapter reconstructs stain-invariant representations via a Stain-invariant Adapter (S-Adapter) and integrates morphological features through a Morphology-aware Adapter (M-Adapter), effectively bridging the gap between low-level optical properties and high-level tissue semantics. Additionally, we introduce a Pathology Prototypical Contrastive Loss (PPCLoss) to reduce inter-class similarity and mitigate intra-class heterogeneity, enhancing feature discriminability. Extensive experiments using three pathology foundation models (CTransPath, CONCH and UNI) across six benchmarks, including two public datasets, an osteosarcoma tissue classification dataset (56,178 patches) and a chondrosarcoma necrosis classification dataset (3,867 patches) for unseen cancers generalization, as well as an IHC-stained dataset (4,967 patches) and an HIF1A IHC-stained dataset (4,433 patches) for unseen stains generalization, demonstrate the effectiveness of HiAdapter in both efficiency and accuracy. HiAdapter achieves an average improvement of 2.15 in F1 and 1.55 in accuracy over the second-best performer, maintaining strong biological and diagnostic interpretability. External validation on an independent osteosarcoma dataset (9,535 patches) and WSI-level survival analysis (178 slides) further confirm the superior generalizability and underscore the potential for patient-level diagnosis and prognosis in clinical practice. Our code is available at HiAdapter.

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Abnormality detection is a crucial yet challenging task in medical image analysis. Distinguishing abnormalities from normal data by learning to reconstruct normal-only data alleviates the reliance on labeled datasets. However, many studies, even if unsupervised, rely on a labeled validation set to select the best model for inference from multiple training iterations. For many diseases labeled data are unavailable and substantially time consuming to obtain. To address this, AUC p - a novel metric that supports abnormality detection for unsupervised and self-supervised methods is proposed. Instead of evaluating the realism of reconstructed images to select the best of model for inference, it focuses on actual detection performance and without requiring an annotated test set. Assuming the pseudo ground truth of all unannotated samples in the test set as abnormal/positive and using traditional AUC calculation, AUC p scores are derived. Given a large and representative training set of normal samples, we show mathematical and empirical evidence that model selection using AUC p scores improves disease detection in terms of unsupervised and self-supervised methods over conventional metrics. Using two unsupervised methods for neurologic disease detection and self-supervised methods on diverse datasets, our results demonstrate that the AUC p score effectively identifies the optimal model for inference, significantly enhancing abnormality and disease detection. The corresponding implementations are available in https://github.com/mahfuzmohammad/AUCp.

异常检测是医学图像分析中一项关键且具有挑战性的任务。通过学习仅重构正常数据来区分异常与正常数据，减轻了对标记数据集的依赖。然而，许多研究即使是无监督的，也依赖于标记的验证集从多次训练迭代中选择最佳推理模型。对于许多疾病，标记数据...

Synthetic medical image data can unlock the potential of deep learning (DL)-based clinical decision support (CDS) systems through the creation of large scale, privacy-preserving, training sets. Despite the significant progress in this field, there is still a largely unanswered research question: “How can we quantitatively assess the similarity of a synthetically generated set of images with a set of real images in a given application domain?”. Today, answers to this question are mainly provided via user evaluation studies, inception-based measures, and the classification performance achieved on synthetic images. This paper proposes a novel measure to assess the similarity between synthetically generated and real sets of images, in terms of their utility for the development of DL-based CDS systems. Inspired by generalized neural additive models, and unlike inception-based measures, the proposed measure is interpretable (Interpretable Utility Similarity, IUS), explaining why a synthetic dataset could be more useful than another one in the context of a CDS system based on clinically relevant image features. The experimental results on publicly available benchmark datasets from various color medical imaging modalities including endoscopic, dermoscopic and fundus imaging, indicate that selecting synthetic images of high utility similarity using IUS can result in relative improvements of up to 54.6% in terms of classification performance. The generality of IUS for synthetic data assessment is demonstrated also for grayscale X-ray and ultrasound imaging modalities. IUS implementation is available at https://github.com/innoisys/ius.

合成医学图像数据可以通过创建大规模、隐私保护的训练集，释放基于深度学习（DL）的临床决策支持（CDS）系统的潜力。尽管该领域取得了显著进展，但仍有一个基本未解决的研究问题：“我们如何定量评估合成生成图像集与真实图像集的相似性？”

Coronary artery stenosis is a leading cause of cardiovascular disease, diagnosed by analyzing the coronary arteries from multiple angiography views. Although numerous deep-learning models have been proposed for stenosis detection from a single angiography view, their performance heavily relies on expensive view-level annotations, which are often not readily available in hospital systems. Moreover, these models fail to capture the temporal dynamics and dependencies among multiple views, which are crucial for clinical diagnosis. To address this, we propose SegmentMIL, a transformer-basedmulti-viewmultiple-instance learning framework for patient-level stenosis classification. Trained on a real-world clinical dataset, using patient-level supervision and without any view-level annotations, SegmentMIL jointly predicts the presence of stenosis and localizes the affected anatomical region, distinguishing between the right and left coronary arteries and their respective segments. SegmentMIL obtains high performance on internal and external evaluations and outperforms both view-level models and classical MIL baselines, underscoring its potential as a clinically viable and scalable solution for coronary stenosis diagnosis. Our code is available at https://github.com/NikolaCenic/mil-stenosis.

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Assessing the quality of automatic image segmentation is crucial in clinical practice, but often very challenging due to the limited availability of ground truth annotations. Reverse Classification Accuracy (RCA) is an approach that estimates the quality of new predictions on unseen samples by training a segmenter on those predictions, and then evaluating it against existing annotated images. In this work we introduce ConfIC-RCA (Conformal In-Context RCA), a novel method for automatically estimating segmentation quality with statistical guarantees in the absence of ground-truth annotations, which consists of two main innovations. First, In-Context RCA, which leverages recent in-context learning models for image segmentation and incorporates retrieval-augmentation techniques to select the most relevant reference images. This approach enables efficient quality estimation with minimal reference data while avoiding the need of training additional models. Second, we introduce Conformal RCA, which extends both the original RCA framework and In-Context RCA to go beyond point estimation. Using tools from split conformal prediction, Conformal RCA produces prediction intervals for segmentation quality providing statistical guarantees that the true score lies within the estimated interval with a user-specified probability. Validated across 10 different medical imaging tasks in various organs and modalities, our methods demonstrate robust performance and computational efficiency, offering a promising solution for automated quality control in clinical workflows, where fast and reliable segmentation assessment is essential. The code is available at https://github.com/mcosarinsky/Conformal-In-Context-RCA.

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Proton Density Fat Fraction (PDFF) is the gold standard for non-invasive fatty liver diagnosis, but its reliance on Magnetic Resonance Imaging (MRI) limits broad clinical applicability. Motivated by the accessibility of B-mode Ultrasound (US) in fatty liver assessment, we propose a novel framework for liver segmentation and PDFF prediction from US images. To enhance generalization ability despite limited paired US-PDFF data, our framework integrates a cross-task self-supervised pretext task that extracts semantic features to guide echo intensity capture, benefiting both liver segmentation and PDFF prediction. To address the noise and artifacts inherent in US images, our framework leverages T2-weighted imaging (T2WI) exclusively during training to establish a feature bridge between US and PDFF, thereby enhancing PDFF prediction. Once trained, the model relies solely on US for inference, making it a practical and cost-effective alternative to MRI-based PDFF estimation. Additionally, our framework introduces an uncertainty-augmented adversarial loss function to refine liver boundary delineation, further improving segmentation and PDFF prediction accuracy. Experimental results demonstrate that our method outperforms state-of-the-art methods in liver segmentation and PDFF prediction; and in a specific application study, our predicted PDFF achieves accuracy comparable to real PDFF for hepatic steatosis classification, highlighting its clinical potential. The full source code and detailed documentation are publicly available at https://github.com/D0ngZhang/SSTB.

质子密度脂肪分数（PDFF）是无创脂肪肝诊断的金标准，但其依赖于磁共振成像（MRI）限制了广泛的临床应用。受B型超声（US）在脂肪肝评估中的可及性启发，我们提出了一种新颖的框架，用于从超声图像中进行肝脏分割和PDFF预测。为了提高泛化能力，尽管...

Diffusion MRI mostly involves quantification of how water diffuses in tissue, and its relationship with tissue microstructure. The technique promises great impact for soft tissue studies, since it provides a method of studying tissue microstructure based on millimetre-scale measurements. While multiple analytical models have been proposed to describe how water diffuses in tissue, specifically by s...

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Functional neuroimaging with ultrafast ultrasound is an emerging neuroimaging tool for studying neural activities in the rodent brain. Existing methods, however, are challenged by the compromise between functional imaging sensitivity (i.e., sensitivity in detecting neural responses) and spatial resolution. For example, functional ultrasound (fUS) uses native red blood cells (RBCs) as imaging targe...

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Hyperspectral imaging delivers high-resolution spectral-spatial information to support molecular tissue characterization, but its clinical utility is far from being fully realized. Existing segmentation techniques are constrained by fixed or suboptimal band selection strategies and insufficient frequency-domain modeling, which limit their ability to fully exploit discriminative spectral cues and s...

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To be adopted in safety-critical domains like medical image analysis, AI systems must provide human-interpretable decisions. Variational Information Pursuit (VIP) offers an interpretable-by-design framework by sequentially querying input images for human-understandable concepts, using their presence or absence to make predictions. However, existing V-IP methods overlook sample-specific uncertainty...

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This paper addresses the challenge of multi-disease diagnosis by integrating causal reasoning into the diagnostic framework. In clinical practice, multiple conditions often co-occur, making multi-disease diagnosis more relevant than isolated single-disease cases. However, most deep learning methods focus on single-disease detection and fail to capture the complexity of diagnosing concurrent conditions. Even in multi-label settings, existing approaches mainly rely on correlation-based inference, capturing statistical associations rather than true causal relationships. This can lead to spurious feature-disease associations, where features linked to one disease are mistakenly attributed to another due to frequent co-occurrence, ultimately undermines diagnostic accuracy and interpretability. To address this challenge, we propose a novel framework that incorporates causal intervention into multi-label medical image diagnosis, enabling the model to identify true causal signals rather than misleading correlations arising from co-occurring diseases. Specifically, we model latent disease-related confounders and apply backdoor adjustment to disentangle genuine causal effects from spurious associations. This is achieved by implicitly learning shared feature representations that serve as confounding variables, which are then used to refine image-derived features during prediction. The resulting causal adjustment allows the model to focus on disease-specific cues, improving accuracy and interpretability. Extensive experiments on four diverse medical imaging datasets: ODIR (color fundus photography), LID-FFA (fundus fluorescein angiography), Endo (colonoscopy), and Chestpert (X-ray) demonstrate that our method consistently outperforms existing approaches. Furthermore, our model also effectively separates the diagnosis of co-occurring diseases, high-lighting the potential of causal reasoning to enhance the reliability and clinical applicability of AI-assisted diagnosis. The source code is publicly available at https://github.com/davelailai/BankCausal.git.

本文通过将因果推理融入诊断框架，解决了多疾病诊断的挑战。在临床实践中，多种疾病常常同时发生，使得多疾病诊断比孤立单疾病病例更具相关性。然而，大多数深度学习方法专注于单疾病检测，未能捕捉到诊断并发疾病的复杂性...

Accurate Cobb angle measurement is essential for scoliosis assessment but remains labor-intensive and observer-dependent. We introduce DGPDT (Detection-Guided Prompt-driven Transformer), a unified transformer-based framework that integrates vertebra detection and foundation-model segmentation for generalizable spinal analysis. A Roboflow Detection Transformer (RF-DETR) with a DINOv2 backbone localizes vertebrae, followed by post-processing to ensure anatomical continuity. The resulting bounding boxes serve as automatic prompts for a fine-tuned Segment Anything Model 2.1 (SAM 2.1), which generates high-resolution vertebral masks. Cobb angles are then computed from vertebral masks, enabling estimation of both main and compensatory curves. Evaluations on the in-house (TVGH-SpineXR) and external (SpineWeb-16) datasets demonstrate encouraging performance on both internal and external datasets, achieving mean Dice coefficients of 0.944 and 0.781, respectively, and mean absolute Cobb angle errors of approximately 2–3° in-domain and 4.93° under cross-domain evaluation. Despite being trained solely on TVGH-SpineXR, DGPDT maintains accuracy comparable to models trained directly on the benchmark dataset. By coupling detection-guided prompting with transformer-based segmentation, DGPDT achieves a clinically acceptable mean absolute error (<5°), suggesting good reproducibility and potential applicability beyond the training dataset.

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Aggregating features of tens of thousands of patches into Whole Slide Images (WSIs) representations via aggregators is a crucial step in computational pathology. However, existing aggregation strategies overlook the morphological variability of tissue regions in WSIs stemming from differences in clinical procedures and tumor characteristics, leading to two critical limitations: 1) attention collap...

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Medical Referring Image Segmentation (MRIS) involves segmenting target regions in medical images based on natural language descriptions. While achieving promising results, recent approaches usually involve complex design of multimodal fusion or multi-stage decoders. In this work, we propose NTP-MRISeg, a novel framework that reformulates MRIS as an autoregressive next-token prediction task over a ...

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Most functional magnetic resonance imaging studies rely on estimates of hierarchically organized functional brain networks whose segregation and integration reflect the cognitive and behavioral changes in humans. However, most existing methods for estimating the community structure of networks from both individual and group-level analysis methods do not account for the variability between subjects...

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Low light conditions in endoscopic imaging would lead to poor visibility, reduced contrast, and increased noise, which may hinder accurate diagnosis and surgical guidance. Against this low-light endoscopic image enhancement (LLEIE) task, inspired by the remarkable performance of pretrained CLIP in downstream vision tasks, in this paper, we carefully investigate the pretrained priors of CLIP and em...

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Accurate risk prediction and early diagnosis are crucial for the early intervention of Alzheimer's disease (AD). Current prediction models usually have limited power in capturing the complex interplays between the heterogeneous inputs or lack biological explainability required for clinical adoption and new diagnosis biomarkers discovery. Inspired by pioneering works on biologically informed networ...

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Whole slide image (WSI) classification is a critical task in computational pathology and is aimed at providing automated diagnostic support through high-resolution tissue image analysis. In weakly supervised WSI classification scenarios, the main challenge concerns the traditional multiple instance learning (MIL) methods, which rely on instance-level embeddings aggregated by an attention-based pooling mechanism. These methods often depend on data-driven statistical correlations, leading to misalignments between their attention allocation schemes and histopathological diagnostic regions and reducing the resulting prediction reliability. To address this, we propose frequency-aware causal regularized multiple instance learning (FC-MIL), an innovative framework combining that combines frequency-aware attention (FAA) and causal regularization (CR). FAA extracts more granular, fine-grained histological textures by jointly modeling spatial- and frequency- domain features, whereas CR introduces feature-level counterfactual perturbations as an intervention-inspired regularizer in the latent space, encouraging the model to rely less on spurious correlations and more on invariant pathological cues. Experimental results obtained on four WSI datasets show that FC-MIL outperforms the state-of-the-art MIL methods in terms of both accuracy and interpretability. Our source code is available at https://github.com/7FFDW/FCMIL.

全切片图像（WSI）分类是计算病理学中的一项关键任务，旨在通过高分辨率组织图像分析提供自动化诊断支持。在弱监督的WSI分类场景中，主要挑战在于传统的多实例学习（MIL）方法，这些方法依赖于基于注意力的池化聚合的实例级嵌入...

We propose EMORe, an adaptive reconstruction method designed to enhance motion robustness in free-running, free-breathing self-gated 5D cardiac magnetic resonance imaging (MRI). Traditional self-gating-based motion binning for 5D MRI often results in residual motion artifacts due to inaccuracies in cardiac and respiratory signal extraction and sporadic bulk motion, compromising clinical utility. E...

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Sparse-View CT (SVCT) reconstruction improves temporal resolution and reduces radiation dose, yet its clinical use is hindered by artifacts due to view reduction and domain shifts from scanner, protocol, or anatomical variations, leading to performance degradation in out-of-distribution (OOD) scenarios. We propose a Cross-Distribution Diffusion Priors-Driven Iterative Reconstruction (CDPIR) framew...

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Chest X-Ray Vision-Language pretraining (VLP) leverages large-scale radiograph-report pairs to develop joint image-text representations, demonstrating significant potential for medical image diagnosis. However, existing VLP approaches often overlook the multi-view nature of chest X-Rays, and some multi-view methods apply uniform feature fusion, neglecting view-key semantic contributions. Moreover, random cross-modal Masked Language Modeling (MLM) fails to facilitate effective interactions, impeding representation alignment. Additionally, global alignment in VLP may lead to the false-negative problem. To address these limitations, we propose a novel medical VLP framework comprising three core components. First, a Key Semantics-enhanced Multi-view MLM module aggregates pathology-relevant patches across views, providing semantically rich supervision for MLM. A local semantics enhancing approach, which identifies and aggregates pathology-relevant key patches across views to guide MLM. Second, a Frontal-Lateral Alignment module extracts view-specific pathological features, ensuring semantic consistency and preserving critical information during aggregation. This module independently extracts pathological features from both views to preserve view-specific information while ensuring semantic consistency, which mitigates the loss of crucial information during aggregation. Third, a High-order Semantic Alignment approach mitigates false-negative issues by aligning features with semantically consistent clusters, enhancing global alignment through prototype-level semantics. Extensive experiments across seven public datasets demonstrate that our framework outperforms state-of-the-art methods in four downstream tasks, validating its efficacy. The code is available at https://github.com/sajiutea/F-L.

胸部X射线视觉语言预训练（VLP）利用大规模放射影像-报告对来开发联合图像-文本表示，在医学图像诊断中展现出巨大潜力。然而，现有的VLP方法通常忽略胸部X射线的多视图特性，且一些多视图方法采用统一的特征融合，忽视了不同视图的关键语义贡献。此外，随机跨模态掩码语言建模（MLM）未能促进有效交互，阻碍了表示对齐。同时，VLP中的全局对齐可能导致假阴性问题。为解决这些局限，我们提出了一种新颖的医学VLP框架，包含三个核心组件。首先，关键语义增强的多视图MLM模块跨视图聚合病理相关图像块，为MLM提供语义丰富的监督。一种局部语义增强方法，识别并聚合跨视图的病理相关关键图像块以指导MLM。其次，前后位对齐模块提取视图特定的病理特征，确保聚合过程中的语义一致性并保留关键信息。该模块独立提取两个视图的病理特征以保留视图特定信息，同时确保语义一致性，从而减轻聚合过程中关键信息的丢失。第三，高阶语义对齐方法通过将特征与语义一致的聚类对齐来缓解假阴性问题，通过原型级语义增强全局对齐。在七个公共数据集上的大量实验表明，我们的框架在四个下游任务中优于最先进的方法，验证了其有效性。代码可在 https://github.com/sajiutea/F-L 获取。