collaborators

10 papers

cs.LG2026

Re-thinking Mammography Transfer Learning: The Dataset-Informed Transfer Learning (DITL) Framework for Breast Cancer Screening and Lesion Diagnosis

Adarsh Bhandary Panambur, Siming Bayer, Andreas Maier

Enhancing classification performance in mammography remains a persistent challenge across both small curated datasets and large-scale clinical cohorts. Conventional transfer learni…

cs.CV2026

Safety-oriented sidewalk and road segmentation for smartphone-based assistive navigation

Hakan Calim, Anamaria Dumitrescu, Adarsh Bhandary Panambur +2

Independent sidewalk mobility is essential for blind and visually impaired pedestrians (BVIPs), yet smartphone-based assistive navigation requires perception models that distinguis…

cs.CV2026

LoGSAM: Parameter-Efficient Cross-Modal Grounding for MRI Segmentation

Mohammad Robaitul Islam Bhuiyan, Sheethal Bhat, Melika Qahqaie +4

Precise localization and delineation of brain tumors using magnetic resonance imaging (MRI) are essential for planning therapy and guiding surgical decisions. To address this, we p…

cs.CV2026

CT-VDETR: Semi-supervised 3D Trauma Detection in Computed Tomography (CT) scans using Dense Vertex Relative Position Encoding

Shivam Chaudhary, Sheethal Bhat, Andreas Maier

Accurate detection and localization of traumatic injuries in abdominal CT remain challenging because voxel-level annotations are limited and expensive to obtain. We present a label…

cs.CV2026

Retina-RAG: Retrieval-Augmented Vision-Language Modeling for Joint Retinal Diagnosis and Clinical Report Generation

Abdelrahman Zaian, Sheethal Bhat, Mohamed Abdalkader +1

Diabetic Retinopathy (DR) is a leading cause of preventable blindness among working-age adults worldwide, yet most automated screening systems are limited to image-level classifica…

cs.LG2026

ProtoCLIP: Prototype-Aligned Latent Refinement for Robust Zero-Shot Chest X-Ray Classification

Florian Kittler, Sheethal Bhat, Andreas Maier

Zero-shot vision-language models (VLMs) have shown promise for chest radiograph classification, but their performance is often limited by confounding label co-occurrence, long-tail…