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Rank #123 · 8 unique linked submissions.
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To address the persistent loss of connectivity and thickness instability in 3D microscopy segmentation of tubular structures, we propose a topology-aware learning framework that explicitly supervises tubular geometry beyond voxel-wise overlap.
Nuclei segmentation in histopathology images is fundamental for cancer diagnosis and quantitative analysis, yet existing supervised and weakly supervised methods require extensive manual annotations. Although vision–language pre-trained models enable zero-sho...
To develop and validate a robust CT-radiomics framework using feature-level multicenter distribution adaptation (MDA) to differentiate pulmonary tuberculosis (TB) from fungal pneumonia (FP), explicitly addressing the challenge of inter-scanner variability and...
To develop and validate a deep learning framework for high-accuracy pseudo-CT synthesis from rapid, multi-parametric Magnetic Resonance Fingerprinting (MRF) for liver radiotherapy. This work addresses the technical challenge of translating quantitative tissue...
Magnetic Resonance Fingerprinting (MRF) provides quantitative T1/T2 mapping that can support diagnosis, treatment planning, and longitudinal assessment in brain glioma. Clinical time limits necessitate accelerated MRF, which may introduce artifacts and bias....
The global prevalence of Parkinson’s disease (PD) is rising in tandem with a rapidly aging population. Transcranial sonography (TCS) has emerged as a promising tool for early diagnosis, as it is radiation-free, highly accessible, and cost-effective. Specifica...
To develop a deep learning framework that simultaneously synthesizes lung perfusion and ventilation images from three-dimensional (3D) CT and to evaluate its potential clinical utility.
To advance the clinical application of a fully automatic planning system for functional lung avoidance radiotherapy (AP-FLART) through clinical implementation and comprehensive evaluation.