Clinical diagnosis is inherently a structured reasoning process, yet existing deep learning models often bypass this structure by mapping image features directly to disease labels without explicitly interrogating the morphological and textural criteria that clinicians systematically evaluate. This limits diagnostic tra...
Asim Khan, Samee Ullah Khan, Dwarikanath Mahapatra· 0 citations
Ultrasound imaging relies on high-frequency sound waves to visualize internal anatomy in a non-invasive manner, yet automated analysis remains difficult due to low signal-to-noise ratios, speckle artifacts, and inconsistencies across scanners. These challenges are compounded by the scarcity of annotated datasets, limit...
Satyam Dubey, Tanushree Meena, B. Bozorgtabar et al.· IEEE Access· 0 citations
Endo-C6, a compact corruption benchmark of six endoscopy-realistic perturbations evaluated at a fixed high severity, is introduced and RobustEndoCLIP, obtained by few-shot parameter-efficient tuning with VeRA, outperforming existing TVLM baselines are presented.
Darakshan Rashid, Raza Imam, Ufaq Khan et al.· 0 citations
Imbalanced gradient magnitudes between the visual and language pathways of a vision-language model are often treated as a defect to be corrected. We test that premise for one family of correction, deliberately excluding adaptive, signal-driven schemes (e.g. BalGrad, OGM, PMR, CGGM), which are a mechanistically distinct...
Findings indicate that gradient-level and graph-based analysis can enhance the effectiveness, transparency, and clinical applicability of quality control in multi-label medical image classification.
Shelley Zixin Shu, Aurélie Pahud de Mortanges, A. Poellinger et al.· Journal of imaging informati...· 0 citations
Multi-Resolution Pyramid Transformer (MRPT) is introduced, a model that hierarchically aggregates multi-resolution information from cellular to tissue and WSI levels and surpasses recent foundation models and Multimodal Large Language Models in cancer subtype classification, tissue phenotyping, and Visual Question Answ...
B. Alawode, Moshira Abdalla, Dwarikanath Mahapatra et al.· 0 citations
MoBE is proposed: a fully optimization-free framework that performs dynamic expert selection and adaptation at test time that combines entropy-guided dynamic routing in MoE settings with expert-wise Bayesian adaptation, enabling experts to update their confidence and adapt online without gradient updates.
