Early Access

Simultaneous dual-tracer PET provides more comprehensive information for clinical diagnosis than standard PET imaging, but separating the hybrid dual-tracer signal remains challenging. Deep learning (DL) offers a promising solution. However, most DL methods rely on large datasets with spatiotemporal alignment between dual-tracer and two single-tracer scans. Precise alignment across different scans...

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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。

Developing robust and effective computer-aided diagnostic (CAD) methods for thyroid ultrasound (TUS) remains a key challenge in medical imaging. Prior work has largely focused on binary or multi-class lesion classification, whereas real-world diagnosis follows standardized guidelines based on combinations of lexicon-level descriptors. These combinations naturally exhibit long-tailed distributions ...

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Whole Slide Imaging (WSI) has become a gold standard in cancer diagnosis, inspecting multi-scale information from cellular to tissue levels. Processing an entire WSI directly is infeasible due to GPU memory constraints; thus, Multiple Instance Learning (MIL) has emerged as the standard solution by partitioning WSIs into tiles. While recent two-stage MIL frameworks partially achieve memory efficiency by decoupling tile-level extraction from slide-level modeling, they still face four limitations: (1) the conflict between training throughput and inference memory efficiency, (2) the high susceptibility to overfitting on small-scale WSI datasets with sparse supervision, (3) the disruption of spatial structural integrity during sampling-based training, and (4) the inadequate modeling of multi-scale feature interactions within long sequences. We therefore introduce PathRWKV, a novel State Space Model designed for efficient and robust WSI analysis. To resolve the computational trade-off, we propose an asymmetric structure utilizing max pooling aggregation, enabling parallelized training for high throughput and recurrent inference with constant ( O (1)) memory complexity. To mitigate overfitting, we employ random sampling to enhance data diversity, with a multi-task learning module to regularize feature learning on limited data. To restore spatial context, we introduce 2D sinusoidal position encoding to perceive the relative locations of tissue tiles. To capture comprehensive representations, we integrate TimeMix and ChannelMix modules, enabling dynamic multi-scale feature modeling across temporal and spatial dimensions. Experiments on 29,073 WSIs across 11 datasets demonstrate that PathRWKV outperforms 11 state-of-the-art methods on 10 datasets, establishing it as a scalable and solution with application potential.

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Accurate segmentation of cardiac MRI is essential for assessment of cardiac function through biomarkers such as the left and right ventricular ejection fraction (LVEF, RVEF). Although AI methods have achieved high average segmentation accuracy, the precision of biomarkers for individual patients – quantified by estimation variance, remains critical for reliable diagnosis. Calibrated biomarkers, whose uncertainty accurately reflects the true variability, are highly desirable. However, existing evaluations predominantly focus on population-level segmentation accuracy, leaving biomarker-level uncertainty and calibration largely underexplored. Intrinsic anatomical ambiguity and annotation variability are major sources of biomarker variability and cannot be fully eliminated, even when training on a single annotation set. To address this, we propose a probabilistic segmentation framework that explicitly models aleatoric uncertainty with the goal of improving calibration in the biomarker space. The framework disentangles two key sources of uncertainty: (1) detection uncertainty , arising from ambiguous inclusion of basal or apical slices in 2D cardiac MRI, modeled via objectness probabilities; and (2) contour uncertainty , reflecting variability in ventricular boundary delineation, modeled through mean–variance regression of elliptic Fourier descriptors, a compact representation of closed contours. By propagating these uncertainties to derived biomarkers, the proposed method produces more informative and better-calibrated confidence estimates for ejection fraction. Compared to conventional pixel-wise approaches, our framework improves biomarker reliability, particularly in realistic settings dominated by annotation ambiguity and limited domain shift.

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Accurately and preoperatively predicting survival for high-grade gliomas (HGGs) is important for optimizing treatment strategies. Increasing evidence suggests that brain structural and functional connectivity networks derived from advanced magnetic resonance imaging (MRI) are promising predictors for HGG survival. However, advanced MRIs (e.g., diffusion MRI and functional MRI) are generally clinic...

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Timely identification of Alzheimer’s disease (AD) benefits from combining neuroimaging, fluid biomarkers, and cognitive assessments, yet in practice one or more modalities are often unavailable due to various factors such as cost, patient compliance, and procedural risks. Furthermore, conventional convolutional neural network (CNN) architectures and even Transformer-based models struggle to effici...

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Pneumonia is an acute respiratory infection, posing a serious threat to health and lives. Lung ultrasound (LUS), as a non-invasive and rapid imaging technique, can monitor real-time changes in lung, providing valuable assistance in clinical diagnosis. However, most LUS studies are limited to frame-level analysis and ignore respiratory cycle changes, leading to diagnostic errors. To address these p...

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Conventional registration approaches frequently underperform when applied to sparse feature alignment (e.g., retinal vessels and filamentous collagen fibers in second-harmonic generation (SHG) and bright-field (BF) images), as these tasks demand simultaneous handling of global affine registration and local deformation correction. End-to-end learning-based approaches struggle with minimal effective...

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Lymph node (LN) assessment is an essential task in the routine radiology workflow, providing valuable insights for cancer staging and treatment planning. Identifying scatteredly-distributed and low-contrast LNs in 3D CT scans is highly challenging, even for experienced clinicians. Previous lesion and LN detection methods demonstrate the effectiveness of 2.5D approaches (i.e., using 2D backbone with multi-slice inputs), leveraging pretrained 2D model weights and showing improved accuracy as compared to separate 2D or 3D detectors. However, slice-based 2.5D detectors do not explicitly model inter-slice consistency for LN as a 3D object, requiring heuristic post-merging steps to generate final 3D LN instances, which can involve tuning a set of parameters for each dataset. In this work, we formulate 3D LN detection as a slice-by-slice tracking task along the z-axis and propose LN-Tracker, a novel LN tracking transformer, for joint end-to-end detection and 3D instance association. Built upon a DETR-based detector, LN-Tracker decouples transformer queries into distinct track and detection groups with independent matching, enabling comprehensive LN detection while maintaining trajectory consistency. A masked attention mechanism further separates learning between these query groups, and a similarity loss promotes robust interslice LN association, particularly in low-contrast scenarios. Extensive evaluation on four LN datasets shows LN-Tracker’s superior performance, with at least 2.49% gain in average sensitivity when compared to top 3D/2.5D/tracking detectors. Further validation on public lung nodule and prostate tumor detection tasks confirms the generaliz-ability of LN-Tracker as it achieves top performance on both tasks. Code is available at https://github.com/alibaba-damo-academy/LN-Tracker.

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Objective, task-based measures of image quality (IQ) have been widely advocated for assessing and optimizing medical imaging technologies. Besides signal detection theory-based measures, information-theoretic quantities have been proposed to quantify task-based IQ. For example, task-specific information (TSI), defined as the mutual information between an image and a task variable, represents an op...

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High-field magnetic resonance imaging (MRI) suffers from pronounced magnetic field inhomogeneities and subject-specific field variations, motivating the inscanner design of individually tailored excitation pulses to exploit the full capabilities of high-field MRI. Contemporary methods may employ piecewise-constant (PWC) waveform parameterizations to design excitation pulses, which require many opt...

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Cardiac Magnetic Resonance (CMR) imaging is widely used to personalize heart models for cardiac digital twin analysis because of its ability to visualize soft tissues and capture dynamic functions. However, CMR images have an anisotropic nature, characterized by large inter-slice distances and misalignments from cardiac motion. These limitations result in data loss and measurement inaccuracies, hi...

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Automatic radiology report generation has gained increasing attention for its potential to assist in clinical reporting and reduce the workload of radiologists. Existing 3D radiology report generation methods employ multi-modal foundation model to encode volume-text inputs and produce diagnosis reports, while they ignore the characteristics of 3D volumes including much background regions and suffe...

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Focused transmits are the most commonly used transmit strategy for echocardiograms, but suffer from relatively low frame rates, and in 3D, even lower volume rates. Fast imaging based on unfocused transmits has disadvantages such as motion decorrelation and limited harmonic imaging capabilities. This work introduces a patient-adaptive focused transmit and receive scheme that has the ability to dras...

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Clinically, bi-parametric MRI (bp-MRI), including T2-weighted imaging, diffusion-weighted imaging, and apparent diffusion coefficient map, offers essential prior localization of biopsy and focal therapy for suspicious clinically significant prostate cancer (csPCa), and accurate csPCa delineation from bp-MRI is crucial for better outcomes. However, due to the complexity and high variability in appe...

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Precise segmentation of medical images plays a crucial role in modern clinical practice, providing important foundations for the quantitative analysis of medical images and clinical decision making. However, although deep learning techniques have achieved significant success in conventional medical image segmentation, they still exhibit obvious limitations when faced with complex structure segment...

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With the growing application of deep learning (DL) in dental image analysis, numerous datasets and models have been proposed. Periapical radiographs (PR), as one of the most common imaging modalities in clinical dentistry, play a critical role in endodontics. However, due to the high cost of manual annotation and interpretation challenges caused by poor projection and imaging artifacts, publicly available high-quality PR datasets remain scarce, severely limiting the development of DL-based PR analysis models that rely on large-scale annotated data. To address this issue, we introduce PRAD++, a large-scale PR analysis dataset annotated by clinical experts, consisting of 10,000 PR images with multi-level annotations, including 9 pixel-level segmentation categories and 17 image-level classification labels. Building upon PRAD++, we propose PRNet++, an end-to-end PR analysis network. The framework leverages the Multi-scale Wavelet Convolution (MWCN) network and the Channel Fusion Attention (CFA) mechanism to effectively model and integrate multi-scale features. In addition, an Expert Prior Injection (EPI) loss is designed to incorporate domain-specific dental knowledge into the learning process, refining classification predictions based on segmentation outputs to ensure accuracy and clinical interpretability. Extensive experiments on the PRAD++ dataset demonstrate that PRNet++ consistently outperforms state-of-the-art (SOTA) methods, achieving an average DSC of 81.25% for segmentation, alongside macroand micro-averaged PR-AUCs of 66.58% and 79.10% for classification. Significantly surpassing the runner-up, PRNet++ exhibits enhanced robustness and interpretability in clinically challenging categories. Furthermore, comprehensive ablation and visualization analyses validate the efficacy of individual components and the parameter sensitivity of the EPI loss.

随着深度学习在牙科图像分析中的应用日益广泛，已经提出了许多数据集和模型。根尖周X光片（PR）作为临床牙科中最常见的影像学方法之一，在牙髓病学中起着关键作用。然而，由于手动标注成本高以及投影不良和成像伪影导致的解读挑战，公开的...

Magnetic resonance imaging (MRI) is highly susceptible to patient motion due to its relatively long acquisition times and the fact that data are acquired sequentially in k-space. Even small patient movements introduce phase inconsistencies across measurements, leading to severe artifacts such as blurring, ghosting, and geometric distortions that can compromise diagnostic quality. Retrospective mot...

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Brain network analysis based on functional magnetic resonance imaging (fMRI) is crucial for the diagnosis of neurological disorders. Recently, Transformers have been adopted for brain network analysis to mitigate the over-smoothing issue in GNNs. However, they often fail to account for complex topological properties of brain networks and tend to rely on a limited set of regions of interest (ROIs) ...

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Accurate identification of neurological disorders such as Alzheimer’s disease (AD), Parkinson’s disease (PD), and Autism Spectrum Disorder (ASD) is challenging due to subtle early-stage symptoms and heterogeneous brain dynamics. Resting-state functional MRI (rs-fMRI) enables the construction of functional brain networks, where Graph Neural Networks (GNNs) have shown promise for disease classificat...

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