Early Access

Video-based surgical action-triplet recognition represents each surgical action as an ⟨instrument, verb, target⟩ triplet, which is fundamental for understanding complex surgical scenes. However, this task remains challenging due to heterogeneous spatio-temporal characteristics and complex inter-component dependencies. To address these issues, we propose a Relation-Aware Class Activation-guided Mix...

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The clinical translation of AI in medical imaging faces critical challenges including scare annotated data, long-tailed pathological distributions, and privacy constraints in virtual imaging trials. To address these limitations, we propose a Multi-conditional Diffusion framework with Texture Constraints (MDTC) for synthesizing clinically reliable lesions in CT images. The key innovation lies in th...

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Mechanical anisotropy is a promising biomarker of tissue health because many tissues contain organized fibrous structures that can be disrupted by injury or disease. Elastographic imaging methods have recently emerged to noninvasively quantify anisotropic mechanical properties in soft fibrous tissues like brain white matter or muscle. Calibrated phantoms that reproduce anisotropic tissue behavior ...

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Accurate breast tumor segmentation in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is vital for diagnosis and treatment planning. Despite advances in deep learning, its performance remains constrained by the need for extensive voxel-wise annotations. To mitigate this burden, we propose an annotation-efficient framework that jointly optimizes data selection, unlabeled data utiliza...

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Compared to single-site brain network analysis, multi-site methods leverage larger sample sizes to achieve more robust prediction and generalizability, which has garnered increasing attention in brain disease diagnosis. However, these approaches also face heightened heterogeneity across sites (e.g., scanner protocols) and subjects (e.g., clinical variability), along with potential outliers with di...

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Multimodal approaches that integrate histology and genomics hold strong potential for precision oncology. However, phenotypic and genotypic heterogeneity limits the quality of intra-modal representations and hinders effective inter-modal integration. Furthermore, most existing methods overlook real-world clinical scenarios where genomics may be partially missing or entirely unavailable. We propose...

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Medical decision systems increasingly rely on data from multiple sources to ensure reliable and unbiased diagnosis. However, existing multimodal learning models fail to achieve this goal because they often overlook two critical challenges. First, various data modalities may learn unevenly, thereby converging to a model biased towards certain modalities. Second, the model may emphasize learning on ...

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Surgical full scene segmentation is essential for laparoscopic assistance but remains challenging due to the high visual similarity among anatomical structures, and illumination variations caused by single moving light source. Moreover, accurately segmenting thin, elongated instruments is still difficult, especially when they appear at oblique orientations. Although Mamba-based segmentation method...

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Functional connectivity networks (FCNs) derived from functional magnetic resonance imaging (fMRI) have been widely used to characterize topological alterations of brain networks in neuropsychiatric disorders (NDs). Given the frequent restrictions on direct multi-site fMRI data sharing, federated learning (FL) offers a collaborative modeling paradigm without exchanging raw neuroimaging data. Howeve...

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Medical image segmentation plays a pivotal role in computer-aided diagnosis. However, the scarcity of annotated data severely hinders the deployment of deep learning models. Few-shot learning (FSL) is designed to achieve rapid adaptation to unseen classes using limited labeled samples, among which prototype-based methods have emerged as a dominant paradigm. Nevertheless, existing approaches often ...

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The Segment Anything Model (SAM) has demonstrated groundbreaking performance in natural image segmentation, yet its direct application to medical imaging remains suboptimal due to domain shifts in data distributions and the inherent 3D nature of medical data. Although recent SAM-based methods have employed parameter-efficient transfer learning (PETL) to adapt SAM for medical tasks, they often over...

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Few-shot medical image segmentation (FSMIS) aims to segment unseen anatomical structures using only a few annotated examples, alleviating the heavy annotation burden in clinical practice. Most existing FSMIS methods adopt prototype-based learning but suffer from two limitations. First, equal treatment of foreground pixels ignores their heterogeneous reliability and weakens prototype discriminabili...

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Restoring stable somatosensory feedback through transcutaneous nerve stimulation (TNS) represents a significant challenge in neural engineering; however, the cortical dynamics underlying sustained stimulation are not yet well characterized. This study utilizes a multi-method functional near-infrared spectroscopy framework to quantify the temporal evolution of cortical engagement during temporally ...

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Surgical action triplet recognition constitutes a critical task in context-aware robot-assisted surgery, facilitating automatic surgical action perception by identifying instrument, verb, target, and their association. However, existing works struggle to analyze such complex surgical scenes due to three main issues: (1) component-level optimization conflicts caused by entangled feature spaces, (2)...

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Deep learning (DL) approaches find increasingly more applications in medical imaging analysis, where they excel at predictive tasks such as diagnosis classification, but often offer little insight on the underlying mechanisms governing complex systems, a key goal of scientific inquiry. One way to bridge this gap is to design DL architectures for modeling the underlying system reflected in imaging ...

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Photoacoustic computed tomography (PACT) is an emerging imaging modality that combines the molecular contrast of optical absorption with the high spatial resolution of ultrasound detection. Utilizing ultrasound transducers with larger surface areas can improve detection sensitivity. However, when analytic reconstruction methods that neglect the spatial impulse responses (SIRs) of the transducer ar...

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Patient motion remains a source of image degradation in brain MRI, leading to signal loss, blurring, and geometric distortion that compromise quantitative analysis. Existing deep learning methods for motion correction typically rely on paired clean-corrupted data or k-space acquisitions, which are rarely available in clinical settings. We propose SSRL-MAR, a motion artifact-aware unpaired represen...

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birefringence, retardance, Jones matrix, optic axis orientation, polarization-sensitive optical coherence tomography, retina.Polarization-sensitive optical coherence tomography (PS-OCT) extends OCT by analyzing the polarization states of backscattered light to quantify tissue birefringence. However, conventional implementations require polarization-diverse detection and are therefore incompatible ...

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Computational analysis of breast histopathological images is critical for reliable computer-aided diagnosis and treatment planning. Owing to the ultra-high resolution of whole-slide images (WSIs), most existing WSI segmentation methods rely on patch-wise processing. However, independently processing isolated patches breaks spatial continuity and weakens global tissue context, ultimately limiting s...

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In this work, we explored a computational framework for task-based adaptive SPECT imaging based on a robotic platform. The framework has two key components. First, we utilized the modified uniform Cramér–Rao bound (MUCRB) to approximate the imaging variance attainable with a given system configuration characterized by the imaging time distribution (ITD) over a discretized system configuration spac...

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Automatic medical image segmentation, as a prerequisite for clinical quantitative analysis, forms the basis of computer-aided diagnosis. However, blurry object boundaries caused by factors such as imaging quality and inherent physiological properties of tissues or lesions are the main causes of imprecise segmentation. This aligns with the common understanding that high uncertainty and misclassific...

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Medical image segmentation using deep learning (DL) has enabled the development of automated analysis pipelines for large-scale population studies. However, state-of-the-art DL methods are prone to hallucinations, which can result in anatomically implausible segmentations. With manual correction impractical at scale, automated quality control (QC) techniques have to address the challenge. While pr...

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Vision Transformer has achieved significant performance improvements in natural image segmentation tasks owing to its superior global modeling capabilities. However, applying vision Transformers to 3D medical image segmentation is challenging because of the quadratic computational complexity of the self-attention mechanism and their limited generalization on small-scale datasets. To address these ...

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In medical image analysis, it is common to collect multiple annotations from different clinical experts or raters to mitigate potential diagnostic inaccuracies. However, in computer vision research, it has been conventional to adopt ground-truth labels generated either by majority voting or by selecting a single rater’s annotation. Such practices overlook the rich information embedded in the origi...

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