Early Access

Deep neural networks for medical image segmentation are often overconfident, compromising both reliability and clinical utility. In this work, we propose differentiable formulations of marginal L1 Average Calibration Error (mL1-ACE) as an auxiliary loss that can be computed on a per-image basis. We compare both hard-and soft-binning approaches to directly improve pixel-wise calibration. Our experiments on four datasets (ACDC, AMOS, KiTS, BraTS) demonstrate that incorporating mL1-ACE significantly reduces calibration errors, particularly Average Calibration Error (ACE) and Maximum Calibration Error (MCE), while largely maintaining high Dice Similarity Coefficients (DSCs). We find that the soft-binned variant yields the greatest improvements in calibration, over the DSC plus cross-entropy loss baseline, but often compromises segmentation performance, with hard-binned mL1-ACE maintaining segmentation performance, albeit with weaker calibration improvement. To gain further insight into calibration performance and its variability across an imaging dataset, we introduce dataset reliability histograms, an aggregation of per-image reliability diagrams. The resulting analysis highlights improved alignment between predicted confidences and true accuracies. Overall, our approach provides practitioners with explicit control over the calibration-accuracy trade-off, enabling more reliable integration of deep learning methods into clinical workflows. We share our code here: https://github.com/ cai4cai/Average-Calibration-Losses.

用于医学图像分割的深度神经网络往往过于自信，损害了可靠性和临床实用性。在这项工作中，我们提出了边际L1平均校准误差（mL1-ACE）的可微形式，作为可以在每张图像基础上计算的辅助损失。我们比较了硬分箱和软分箱方法，以直接改善像素级校准。我们在四个数据集（ACDC、AMOS、KiTS、BraTS）上的实验表明，加入mL1-ACE显著降低了校准误差，特别是平均校准误差（ACE）和最大校准误差（MCE），同时基本保持了较高的Dice相似系数（DSC）。我们发现，软分箱变体在校准方面比DSC加交叉熵损失基线有最大改进，但常常牺牲分割性能，而硬分箱mL1-ACE保持了分割性能，尽管校准改进较弱。为了进一步了解校准性能及其在成像数据集上的变异性，我们引入了数据集可靠性直方图，即每张图像可靠性图的聚合。结果分析突出了预测置信度与真实准确度之间更好的对齐。总体而言，我们的方法为从业者提供了对校准-准确性权衡的显式控制，使得深度学习方法能够更可靠地集成到临床工作流程中。我们在以下网址分享代码：https://github.com/cai4cai/Average-Calibration-Losses。

Removed.

已移除。

Accurate correspondence matching in coronary angiography images is crucial for reconstructing 3D coronary artery structures, which is essential for precise diagnosis and treatment planning of coronary artery disease (CAD). Traditional matching methods for natural images often fail to generalize to X-ray images due to inherent differences such as lack of texture, lower contrast, and overlapping structures, compounded by insufficient training data. To address these challenges, we propose a novel pipeline that generates realistic paired coronary angiography images using a diffusion model conditioned on 2D projections of 3D reconstructed meshes from Coronary Computed Tomography Angiography (CCTA), providing high-quality synthetic data for training. Additionally, we employ large-scale image foundation models to guide feature aggregation, enhancing correspondence matching accuracy by focusing on semantically relevant regions and keypoints. Our approach demonstrates superior matching performance on synthetic datasets and effectively generalizes to real-world datasets, offering a practical solution for this task. Furthermore, our work investigates the efficacy of different foundation models in correspondence matching, providing novel insights into leveraging advanced image foundation models for medical imaging applications.

在冠状动脉造影图像中实现精确的对应匹配对于重建三维冠状动脉结构至关重要，这对于冠状动脉疾病的精确诊断和治疗规划具有重要意义。由于缺乏纹理、对比度较低以及结构重叠等固有差异，加上训练数据不足，传统的自然图像匹配方法往往难以推广到X射线图像。为了解决这些挑战，我们提出了一种新颖的流程，该流程利用扩散模型生成逼真的配对冠状动脉造影图像，该模型以冠状动脉计算机断层扫描血管造影（CCTA）的三维重建网格的二维投影为条件，为训练提供高质量的合成数据。此外，我们采用大规模图像基础模型来引导特征聚合，通过关注语义相关区域和关键点来提高对应匹配的准确性。我们的方法在合成数据集上表现出优越的匹配性能，并能有效泛化到真实数据集，为这一任务提供了实用的解决方案。此外，我们的工作研究了不同基础模型在对应匹配中的有效性，为利用先进的图像基础模型进行医学成像应用提供了新的见解。

Vision-Language Models (VLMs) in visual question answering (VQA) offer a unique opportunity to enhance intra-operative decision-making, promote intuitive interactions, and significantly advance surgical education. However, the development of VLMs for surgical VQA is challenging due to limited datasets and the risk of overfitting and catastrophic forgetting during full fine-tuning of pretrained weights. While parameter-efficient techniques like Low-Rank Adaptation (LoRA) and Matrix of Rank Adaptation (MoRA) address adaptation challenges, their uniform parameter distribution overlooks the feature hierarchy in deep networks, where earlier layers, that learn general features, require more parameters than later ones. This work introduces PitVQA++ with an Open-ended PitVQA dataset and vector matrix-low-rank adaptation (Vector-MoLoRA), an innovative VLM fine-tuning approach for adapting GPT-2 to pituitary surgery. Open-Ended PitVQA comprises 109,173 frames from 25 procedural videos with 795,270 question-answer sentence pairs, covering key surgical elements such as phase and step recognition, context understanding, tool detection, localization, and interactions recognition. Vector-MoLoRA incorporates the principles of LoRA and MoRA to develop a matrix-low-rank adaptation strategy that employs rank vectors to allocate more parameters to earlier layers, gradually reducing them in the later layers. Our approach, validated on the Open-Ended PitVQA and EndoVis18-VQA datasets, effectively mitigates catastrophic forgetting while significantly enhancing performance over recent baselines. Performance-rejection analysis further highlights Vector-MoLoRA’s enhanced reliability and trust-worthiness in handling uncertain predictions. Our source code and dataset is available at https://github.com/ HRL-Mike/PitVQA-Plus.

视觉语言模型在视觉问答中为增强术中决策、促进直观交互以及显著推进外科教育提供了独特的机会。然而，由于数据集有限，以及在完全微调预训练权重时存在过拟合和灾难性遗忘的风险，开发用于外科VQA的视觉语言模型具有挑战性。虽然低秩自适应和秩矩阵自适应等参数高效技术解决了自适应挑战，但它们的均匀参数分布忽略了深度网络中的特征层次结构，其中学习通用特征的早期层需要比后期层更多的参数。本文介绍了PitVQA++，包含一个开放式PitVQA数据集和向量矩阵低秩自适应（Vector-MoLoRA），这是一种创新的视觉语言模型微调方法，用于将GPT-2适应于垂体手术。开放式PitVQA包含来自25个手术视频的109,173帧，以及795,270个问答句子对，涵盖了关键手术元素，如阶段和步骤识别、上下文理解、工具检测、定位和交互识别。Vector-MoLoRA结合了LoRA和MoRA的原理，开发了一种矩阵低秩自适应策略，该策略使用秩向量为早期层分配更多参数，并在后期层逐渐减少参数。我们的方法在开放式PitVQA和EndoVis18-VQA数据集上进行了验证，有效缓解了灾难性遗忘，同时显著提升了相对于近期基线的性能。性能拒绝分析进一步突出了Vector-MoLoRA在处理不确定预测时的增强可靠性和可信度。我们的源代码和数据集可在https://github.com/HRL-Mike/PitVQA-Plus获取。

Intraoperative registration of real-time ultra-sound (iUS) to preoperative Magnetic Resonance Imaging (MRI) remains an unsolved problem due to severe modality-specific differences in appearance, resolution, and field-of-view. To address this, we propose a novel 3D cross-modal keypoint descriptor for MRI–iUS matching and registration. Our approach employs a patient-specific matching-by-synthesis approach, generating synthetic iUS volumes from preoperative MRI. This enables supervised contrastive training to learn a shared descriptor space. A probabilistic keypoint detection strategy is then employed to identify anatomically salient and modality-consistent locations. During training, a curriculum-based triplet loss with dynamic hard negative mining is used to learn descriptors that are i) robust to iUS artifacts such as speckle noise and limited coverage, and ii) rotation-invariant. At inference, the method detects keypoints in MR and real iUS images and identifies sparse matches, which are then used to perform rigid registration. Our approach is evaluated using 3D MRI-iUS pairs from the ReMIND dataset. Experiments show that our approach outperforms state-of-the-art keypoint matching methods across 11 patients, with an average precision of 69.8%. For image registration, our method achieves a competitive mean Target Registration Error of 2.39 mm on the ReMIND2Reg benchmark. Compared to existing iUS-MR registration approaches, our framework is interpretable, requires no manual initialization, and shows robustness to iUS field-of-view variation. Code, data and model weights are available at https://github.com/morozovdd/CrossKEY .

术中实时超声（iUS）与术前磁共振成像（MRI）的配准问题仍未解决，原因是模态间在表观、分辨率和视野上存在显著差异。为此，我们提出了一种新颖的3D跨模态关键点描述符，用于MRI-iUS匹配与配准。我们的方法采用患者特定的合成匹配策略，从术前MRI生成合成iUS体积，从而通过监督对比训练学习共享的描述符空间。随后采用概率关键点检测策略来识别解剖上显著且模态一致的位置。训练过程中，使用基于课程的三元组损失和动态难例挖掘来学习描述符，使其对iUS伪影（如散斑噪声和有限覆盖）具有鲁棒性，并且旋转不变。推理时，该方法在MR和真实iUS图像中检测关键点并识别稀疏匹配，进而用于刚性配准。我们使用ReMIND数据集中的3D MRI-iUS对进行评估。实验表明，我们的方法在11名患者中优于最先进的关键点匹配方法，平均精度为69.8%。在图像配准方面，我们的方法在ReMIND2Reg基准上实现了具有竞争力的平均目标配准误差2.39毫米。与现有的iUS-MR配准方法相比，我们的框架可解释、无需手动初始化，并且对iUS视野变化具有鲁棒性。代码、数据和模型权重可在https://github.com/morozovdd/CrossKEY获取。

High-fidelity reconstruction of the Posterior Eyeball Shape (PES) is crucial for early diagnosis and timely intervention of sight-threatening diseases such as high myopia, diabetic retinopathy, and glaucoma. However, existing magnetic resonance imaging (MRI)- and optical coherence tomography (OCT)-based methods either provide only coarse scleral geometry or suffer from suboptimal PES representations due to limited field of view (FOV) and detail loss, hindering accurate assessment of intact retinal pigment epithelium (RPE) abnormalities. In this study, we propose the Polar Subarea-Aware Fusion Net (PSAFNet), a novel end-to-end framework that reconstructs complete and high-fidelity PES directly from a single local OCT scan, even under clinically common settings with only 6.25% FOV. To avoid information loss, we reformulate PES reconstruction as a 2D dense regression task and introduce the Ocular Shape Map (OSM), an innovative lossless 2D representation that encodes 3D coordinate attributes into corresponding image channels. PSAFNet then leverages three dedicated modules—Subarea Feature Embedding Module (SFEM), Channel- and Patch-wise Fusion Blocks (CFB/PFB), and Reassemble and Up-sample Module (RUM)—to enhance positional awareness, integrate local–global features, and achieve high-resolution OSM prediction. Furthermore, we construct two large-scale datasets, POSDiag and PESGen, comprising 794 ultra-widefield OCT scans from diverse health conditions and imaging devices, providing a comprehensive benchmark for PES reconstruction. Extensive experiments demonstrate that PSAFNet consistently outperforms existing methods (e.g., EMD=5.58, AAL=97.3%) and exhibits strong clinical relevance, validated by superior performance in downstream disease classification and ophthalmologist evaluations (Expert-Score=82.78%). The source code of the proposed PSAFNet is released at https://github.com/HKUZJ77/PSAFNet.

后眼杯形状的高保真重建对于高度近视、糖尿病视网膜病变和青光眼等威胁视力疾病的早期诊断和及时干预至关重要。然而，现有的基于磁共振成像和光学相干断层扫描的方法要么仅提供粗略的巩膜几何形状，要么由于视野有限和细节丢失而导致后眼杯表示欠佳，阻碍了对完整视网膜色素上皮异常的准确评估。在本研究中，我们提出了极地区域感知融合网络（PSAFNet），一种新颖的端到端框架，可直接从单个局部OCT扫描中重建完整且高保真的后眼杯，即使在仅6.25%视野的临床常见设置下也能实现。为避免信息丢失，我们将后眼杯重建重新表述为2D密集回归任务，并引入眼形图（OSM），一种创新的无损2D表示，将3D坐标属性编码到相应的图像通道中。PSAFNet随后利用三个专用模块——子区域特征嵌入模块（SFEM）、通道和块级融合模块（CFB/PFB）以及重组和上采样模块（RUM）——来增强位置感知、整合局部-全局特征并实现高分辨率OSM预测。此外，我们构建了两个大规模数据集POSDiag和PESGen，包含来自不同健康状况和成像设备的794个超宽视野OCT扫描，为后眼杯重建提供了全面的基准。大量实验表明，PSAFNet始终优于现有方法（例如，EMD=5.58，AAL=97.3%），并表现出强烈的临床相关性，通过在下游疾病分类和眼科医生评估（专家评分=82.78%）中的优越性能得到验证。所提出的PSAFNet的源代码已在https://github.com/HKUZJ77/PSAFNet发布。

Glaucoma is a leading cause of irreversible blindness worldwide, with asymptomatic early stages often delaying diagnosis and treatment. Early and accurate diagnosis requires integrating complementary information from multiple ocular imaging modalities. However, most existing studies rely on single- or dual-modality imaging, such as fundus and optical coherence tomography (OCT), for coarse binary c...

青光眼是全球范围内导致不可逆失明的主要原因，其无症状的早期阶段常常延误诊断和治疗。早期准确诊断需要整合来自多种眼部成像模式的互补信息。然而，现有大多数研究依赖于单模态或双模态成像，如眼底和光学相干断层扫描（OCT），进行粗略的二元分类...

Two-dimensional cell culture models have long been a cornerstone of biomedical research; however, they often fail to accurately replicate the in vivo environment. In recent years, three-dimensional (3D) cell cultures, particularly 3D spheroid models, have gained recognition for their ability to better mimic the complexities of the in vivo environment, making them valuable tools for studying cellular behavior and responses. Tumor spheroids, in particular, have significant applications in anticancer therapy evaluation, providing a more physiologically relevant model by simulating the spatial architecture and microenvironment of tumors. However, due to the limitations imposed by optical diffraction and background noise in 3D imaging, traditional imaging methods are unable to accurately resolve the growth, morphological changes, and drug responses of tumor spheroids. To address this issue, super-resolution imaging technologies have emerged. Structured illumination microscopy (SIM) combined with reconstruction algorithms can effectively enhance resolution, but challenges such as limited light penetration of single-photon imaging and high background noise remain in 3D imaging. In this paper, an advanced SIM technology with large depth and low noise 3D imaging capability is developed. This study introduces a novel frequency-specific denoising method (FSDM) to effectively reduce noise through adjusting the weights of high-frequency signals to preserve image details. The FSDM optimization significantly reduces background interference from deeper tissue layers, improving image details and the overall quality of 3D imaging. For the first time, scanning SIM is integrated with two-photon microscopy (TPEF-SIM) for 3D imaging, leveraging the strengths of both techniques to enhance resolution and overcome light penetration limitations.

二维细胞培养模型长期以来一直是生物医学研究的基石，但它们往往无法准确复制体内环境。近年来，三维（3D）细胞培养，特别是3D球体模型，因其更好地模拟体内环境的复杂性而获得认可，成为研究细胞行为和反应的有价值工具。肿瘤球体在抗癌治疗评估中具有重要应用，通过模拟肿瘤的空间结构和微环境提供更生理相关的模型。然而，由于光学衍射和3D成像中背景噪声的限制，传统成像方法无法准确解析肿瘤球体的生长、形态变化和药物反应。为解决这一问题，超分辨成像技术应运而生。结构光照显微镜（SIM）结合重建算法可有效提高分辨率，但在3D成像中仍面临单光子成像光穿透有限和高背景噪声等挑战。本文开发了一种具有大深度和低噪声3D成像能力的先进SIM技术。本研究引入了一种新颖的频率特异性去噪方法（FSDM），通过调整高频信号的权重来有效降低噪声，保留图像细节。FSDM优化显著减少了来自深层组织的背景干扰，改善了图像细节和3D成像的整体质量。首次将扫描SIM与双光子显微镜（TPEF-SIM）集成用于3D成像，利用两种技术的优势提高分辨率并克服光穿透限制。

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 m...

空间转录组学（ST）技术可检测组织活检中的基因表达，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染色的组织病理学图像预测基因表达提供了见解。然而，m...

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 PA...

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，而通常需要无创方法从大量人群中筛查高危患者。在本文中，我们提出了一种多模态学习流程来预测PA...

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...

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。通过学习重建仅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，也依赖标记验证集从多个训练迭代中选择最佳模型进行推理。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 ...

合成的医疗图像数据可以通过创建大规模、隐私保护的训练集来解锁深度学习 (DL) 基于临床决策支持 (CDS) 系统的潜力。尽管在该领域取得了重大进展，但仍存在一个主要未解决的研究问题：“如何量化一个合成生成的图像集与一个集……的相似性？”

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...

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，现有方法受到复杂的RV解剖结构和二维多视图超声心动图中固有的不完整空间信息的阻碍。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重新定义了...

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 complexi...

随着病理基础模型的快速发展，针对下游任务的高效微调策略需求日益增长。然而，现有的参数高效微调方法大多与任务无关，且对组织病理学图像的泛化能力有限，尤其是在处理未见过的癌症和染色时，这是由于显著的染色变异和复杂性所致。

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 t...

评估自动图像分割的质量在临床实践中至关重要，但由于真实标注的有限可用性，通常非常具有挑战性。反向分类准确性（RCA）是一种通过在新预测上训练分割器，然后针对现有标注图像进行评估来估计未见样本预测质量的方法。在...

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...

扩散MRI主要涉及量化水在组织中的扩散及其与组织微结构的关系。该技术有望对软组织研究产生重大影响，因为它提供了一种基于毫米级测量来研究组织微结构的方法。虽然已经提出了多种分析模型来描述水在组织中的扩散，特别是通过...

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...

高光谱成像提供高分辨率的光谱-空间信息以支持分子组织表征，但其临床实用性远未完全实现。现有分割技术受限于固定或次优的波段选择策略以及不足的频域建模，这限制了它们充分利用判别性光谱线索的能力。

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...

为了在安全关键领域（如医学图像分析）中得到应用，AI系统必须提供人类可解释的决策。变分信息追求（VIP）通过顺序查询输入图像中人类可理解的概念，并利用这些概念的存在与否进行预测，提供了一个可解释的设计框架。然而，现有的VIP方法忽略了样本特定的不确定性……

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 ...

质子密度脂肪分数（PDFF）是非侵入性脂肪肝诊断的金标准，但其依赖于磁共振成像（MRI）限制了广泛的临床适用性。鉴于B型超声（US）在脂肪肝评估中的可及性，我们提出了一种新颖的框架，用于从US图像进行肝脏分割和PDFF预测。为了提高泛化能力，尽管...

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...

通过聚合器将数万个补丁的特征聚合为整张切片图像（WSI）表示是计算病理学中的关键步骤。然而，现有的聚合策略忽略了由于临床程序和肿瘤特征差异导致的WSI中组织区域的形态变异性，导致两个关键限制：1）注意力崩溃...

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...

大多数功能磁共振成像研究依赖于对分层组织的功能脑网络的估计，这些网络的分隔和整合反映了人类的认知和行为变化。然而，现有的从个体和群体层面分析方法中估计网络社区结构的大多数方法并未考虑受试者之间的变异性...

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整合到诊断框架中，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的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...

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...

中文摘要翻译待生成

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...

中文摘要翻译待生成

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 ...

中文摘要翻译待生成