Latest AI and machine learning research in medical ethics / professional responsibility for healthcare professionals.
The growing number of medical vision foundation models highlights the need for effective model selection. However, mainstream selection methods rely on exhaustive fine-tuning, which is computationally expensive. Most of the existing Transferability Estimation (TE) metrics are primarily designed for image-level classification. They fail to preserve spatial relationships and fine-grained boundary de...
Segmentation of biomarkers in medical images is frequently viewed as a first step towards medical image analysis in any bioinformatics or biomedical application. Despite progress, existing methods still struggle to capture information at multiple scales and to perform upsampling effectively across different datasets. These shortcomings often result in suboptimal generalization capabilities. Recent...
Despite increasing scale and resolution, many biological measurements remain destructive, revealing only spatial information rather than the dynamics ...
Low-dose computed tomography (LDCT) reduces radiation exposure but introduces stronger quantum noise, streak artifacts, and local texture degradation,...
Early detection of suspicious moles remains the most effective means of reducing mortality from skin cancer, yet systematic screening is constrained b...
Accurate prediction of stress and strain fields in hierarchical composite microstructures is critical for physics-informed material design, yet conven...
Unified visual anomaly detection seeks to train a single detector that can be deployed across categories, domains, and application scenarios. In the f...
Accurate coronary Digital Subtraction Angiography (DSA) vessel segmentation is essential for computer-aided diagnosis and treatment planning of corona...
Urban building change detection from bi-temporal aerial imagery is important for redevelopment monitoring, infrastructure management, and unauthorized...
A synthetic measurement of model competence is useful only if it survives the move to real data, yet the real labels that would verify it are exactly ...
Can a vision model truly see an object, or does it only fit surface-level visual cues? Following Wittgenstein's view that the limits of language are t...
While interpretable models such as concept bottleneck models (CBMs) and program synthesis methods enable verification of model decisions, their evalua...
Skin lesion segmentation is a key task in computer-aided dermatological diagnosis, where accuracy directly impacts downstream analysis and disease cla...
Developing robust artificial intelligence models for 4D (3D + time) medical imaging is constrained by limited annotated data, inter-device domain shif...
This study introduces Blasto-Net, a multi-task deep learning model for comprehensive blastocyst analysis. The proposed model performs three tasks simu...
Ultrasound is a non-invasive, real-time, and cost-effective imaging technique widely used in clinical diagnosis. However, its diagnostic efficacy is o...
ABACUS is a unified vision-language model that handles object counting, crowd counting, referring-expression counting, and count-faithful image genera...
Lesion segmentation in breast ultrasound involves two related challenges. In images with lesions, speckle noise, low tissue contrast, and posterior ac...
Chromatin is organized into self-interacting topologically associating domains partitioned by boundary elements that insulate adjacent domains and res...
Intravascular ultrasound (IVUS) lumen and external elastic membrane (EEM) segmentation is important for quantitative coronary plaque burden assessment...