Volume 45, Issue 6

With the rapid development of deep generative models (DGMs), the performance of decoding language and reconstructing images from Functional Magnetic Resonance Imaging (fMRI) has been improved. Nevertheless, the accurate representation of brain activity remains highly challenging, primarily due to the limited paired samples and the low signal-to-noise ratios of fMRI. To tackle these challenges, we ...

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Precise cortical and cancellous bone segmentation is essential for safe laminectomy. However, it remains challenging due to limited annotations and high anatomical similarity. To mitigate these challenges, we propose MambaMatch, an end-to-end semi-supervised segmentation framework collaboratively optimized under a teacher–student paradigm. The student branch adopts an innovative Mamba-ASPP-Unet (M...

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Immunohistochemical (IHC) staining enables precise molecular profiling of protein expression, with over 200 clinically available antibody-based tests in modern pathology. However, comprehensive IHC analysis is frequently limited by insufficient tissue quantities in small biopsies. Therefore, virtual multiplex staining emerges as an innovative solution to digitally transform H&E images into multipl...

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Rapid advancements in diffusion models have enabled synthesis of realistic and anonymized imagery in radiography. However, due to their complexity, these models typically require large training volumes, often exceeding 10,000 images. Pre-training on natural images can partly mitigate this issue, but often fails to generate anatomically accurate shapes due to the significant domain gap. This prohib...

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Artery extraction from X-ray coronary angiography (XCA) images is essential for the accurate diagnosis and treatment of coronary artery diseases. However, vessel visibility is significantly obscured by superimposed fluoroscopic densities from bones and soft tissues. Traditional digital subtraction angiography techniques are ineffective due to severe image degradation caused by cardiac motion. The ...

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Functional magnetic resonance imaging (fMRI) enables non-invasive brain disorder classification by capturing blood-oxygen-level-dependent (BOLD) signals. However, most existing methods rely on functional connectivity (FC) via Pearson correlation, which reduces 4D BOLD signals to static 2D matrices—discarding temporal dynamics and capturing only linear inter-regional relationships. In this work, we...

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Esophageal cancer is one of the most lethal cancers, with 5-year survival rate of only 20%. Patient outcomes can vary significantly even though they are at the same cancer stage and receive similar treatments. Accurate prognostic prediction for esophageal cancer patients is highly desired to receive personalized precise treatment. Nevertheless, there are very few automated methods yet to fully exp...

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Immunohistochemistry (IHC) staining is crucial for determining tumor subtypes, obtaining protein expression information, and developing personalized treatment plans. But compared with hematoxylin and eosin (H&E) staining, IHC staining is more complex and expensive. With the advancement of deep learning, converting H&E stained images into IHC stained images has gradually emerged as a solution for o...

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Histopathology remains the gold standard for cancer diagnosis and prognosis. With the advent of transcriptome profiling, multi-modal learning combining transcriptomics with histology offers more comprehensive information. However, existing multi-modal approaches are challenged by intrinsic multi-modal heterogeneity, insufficient multi-scale integration, and reliance on paired data, restricting cli...

组织病理学仍然是癌症诊断和预后的金标准。随着转录组分析的出现，将转录组学与组织学相结合的多模态学习提供了更全面的信息。然而，现有的多模态方法面临多模态异质性、多尺度整合不足以及对配对数据的依赖等挑战，限制了临床应用...

Cardiac CT provides comprehensive structural and functional heart imaging, but motion artifacts remain a fundamental challenge. Although modern scanners have improved temporal resolution through hardware advancements and electrocardiogram-gated scan protocols, cardiac CT is still limited to specific cardiac phases and may fail in clinical practice. Here, to overcome this challenge, we propose the ...

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Federated learning (FL) enables collaborative model training across decentralized medical datasets while preserving data privacy. Its practical adoption remains limited due to data heterogeneity, specifically, differences in input imaging modality (e.g., CT or MRI) and client task (e.g., segmentation or classification) across participating institutions (clients). Such data heterogeneity poses sign...

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Fully supervised polyp segmentation relies on costly pixel-level annotations. Although semi- and weakly supervised methods reduce annotation requirements, they still depend on partial mask supervision. Text-supervised segmentation is a promising alternative; however, for polyps, the key challenge is to ground instance-specific phrases to the correct lesion region under cluttered backgrounds and la...

全监督息肉分割依赖于昂贵的像素级标注。虽然半监督和弱监督方法减少了标注需求，但它们仍然依赖于部分掩码监督。文本监督分割是一种有前景的替代方案；然而，对于息肉，关键挑战是在杂乱背景和...下将实例特定短语定位到正确的病变区域。

Convolutional Neural Networks struggle with long-range dependencies modeling in medical image segmentation, and traditional Transformer models rely on Multi-Layer Perceptron (MLP) for channel information mixing, with performance issues as data dimensions increase. These issues prompt a reassessment of the model’s design to enhance segmentation performance and effectively capture long-range depende...

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Computed tomography (CT) is a major medical imaging modality. Clinical CT scenarios, such as low-dose screening, sparse-view scanning, and metal implants, often lead to severe noise and artifacts in reconstructed images, requiring improved reconstruction techniques. The introduction of deep learning has significantly advanced CT image reconstruction. However, obtaining paired training data remains...

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Atherosclerosis is a major cause of cardiovascular disease. Photothermal ablation provides a minimally invasive therapeutic approach but remains constrained by the lack of reliable temperature monitoring. Conventional thermometry provides only single-point readings and is easily affected by contact and flow, limiting spatial accuracy. Although array-based photoacoustic thermometry allows non-conta...

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Magnetic Particle Imaging (MPI) enables noninvasive temperature imaging without depth limitations. However, due to the lack of effective calibration strategies that can simultaneously address issues such as calibration infeasibility and environmental mismatch, its practical in vivo application remains challenging. In this work, we propose a novel in vivo temperature imaging method based on a dual-...

磁粒子成像（MPI）能够实现无深度限制的非侵入式温度成像。然而，由于缺乏能够同时解决校准不可行性和环境失配等问题的有效校准策略，其在实际体内应用中仍面临挑战。在这项工作中，我们提出了一种基于双模式的新型体内温度成像方法，...

Retinal diseases (RD) are major causes of global vision impairment. Automated diagnosis using fundus images has significant clinical value, particularly in multi-label classification of RD. Recently, hierarchy-aware methods have shown potential in improving classification performance by leveraging hierarchical relationships among disease categories. However, implicit hierarchy-aware methods often ...

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Atrial fibrillation, characterized by high prevalence and poor prognosis, presents a significant global health burden. Accurate segmentation and measurement of left ventricular and left atrial appendage morphology and function are essential for reliable risk assessment. However, these tasks are hindered by ambiguous boundaries, complex cardiac motion, and sparse annotations. To address these chall...

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Accurate segmentation of organelles in electron microscopy (EM) volumes is essential for understanding intracellular organization. While promising, deep learning-based methods could be unstable and unreliable without sufficient annotations. Masked image modeling (MIM), a powerful pretraining technique, has proven effective in enhancing segmentation by extracting meaningful representations from lar...

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Multiple instance learning (MIL) is a commonly used paradigm for histopathological analysis due to the ultra-high resolution and coarse-grained labels of Whole Slide Images (WSIs). Recent studies apply Mamba architecture to WSI classification by modeling MIL as long-sequence tasks, but a key discrepancy remains: Mamba’s output is sensitive to scanning modes, whereas MIL requires scan-invariant pre...

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Despite the promising potential of multi-modal learning in medical image segmentation, real-world applications often encounter modal incompleteness sourced from diverse domains and institutions, sparking significant discussions on incomplete multi-modal learning. Existing approaches either train a unified model for all or develop individual models for specific multi-modal combinations to ensure mo...

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Cancer survival analysis commonly integrates information across diverse medical modalities to make survival-time predictions. Existing methods primarily focus on extracting different decoupled features of modalities and performing fusion operations such as concatenation, attention, and Mixture-of-Experts (MoE)-based fusion. However, these methods still face two key challenges: 1) fixed fusion sche...

癌症生存分析通常整合来自不同医学模态的信息以进行生存时间预测。现有方法主要侧重于提取模态的不同解耦特征，并执行诸如拼接、注意力和基于专家混合（MoE）的融合等融合操作。然而，这些方法仍面临两个关键挑战：1）固定的融合方案...

Semi-supervised segmentation (S3) is one of the preferred choices for histopathological image segmentation tasks, while how to improve model’s learning capability for unlabeled data remains a key challenge in S3. The remarkable feature extraction abilities of Segment Anything Model (SAM) offers a potential opportunity. However, SAM’s performance on contextual complex histopathological images is no...

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Accurate 3D reconstruction in endoscopy enables quantitative and holistic lesion characterization within the gastrointestinal (GI) tract. To achieve this, reliable depth and pose estimation is required. However, endoscopy systems are monocular, and existing methods relying on synthetic datasets or complex models often lack generalizability in challenging endoscopic conditions. We propose a robust ...

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