10 papers · 1 filter
Example-based Robust Abnormality Detection with Minimal Annotations using Exemplar Med-DETR
Sheethal Bhat, Bogdan Georgescu, Awais Mansoor +5
Reducing annotation requirements remains a key challenge in developing robust medical object detectors. To address this, Vision-Language (VL) object detection methods leverage grou…
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…
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…
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…
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…
Benchmarking CNN-based Models against Transformer-based Models for Abdominal Multi-Organ Segmentation on the RATIC Dataset
Lukas Bayer, Sheethal Bhat, Andreas Maier
Accurate multi-organ segmentation in abdominal CT scans is essential for computer-aided diagnosis and treatment. While convolutional neural networks (CNNs) have long been the stand…