1 citations · 3 across the 13 of their papers we have counts for
11 papers · 1 filter
PANDA - Prototype-Anchored Alignment for Partially Unpaired Multimodal Learning, with Applications to Alzheimers MRI and TCGA Pathology
Sheethal Bhat, Mahfuzur Rahman Chowdhury, Paula Andrea Perez-Toro +4
Multimodal medical prediction often faces incomplete pairing: auxiliary modalities with complementary signal are available for only a subset of subjects (or none) and cannot be ass…
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…
Pseudo-Text-Conditioned 3D Grounding DINO for Organ Localization in Abdominal CT
Siqi Chen, Han Gong, Keyi Hou +3
Reliable organ localization in abdominal CT can provide spatial priors for downstream trauma analysis. We propose CT-3GDINO, a lightweight 3D detector that adapts a Grounding-DINO-…
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…
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…
Exemplar Med-DETR: Toward Generalized and Robust Lesion Detection in Mammogram Images and beyond
Sheethal Bhat, Bogdan Georgescu, Adarsh Bhandary Panambur +8
Detecting abnormalities in medical images poses unique challenges due to differences in feature representations and the intricate relationship between anatomical structures and abn…
CXR-CML: Improved zero-shot classification of long-tailed multi-label diseases in Chest X-Rays
Rajesh Madhipati, Sheethal Bhat, Lukas Buess +1
Chest radiography (CXR) plays a crucial role in the diagnosis of various diseases. However, the inherent class imbalance in the distribution of clinical findings presents a signifi…
Enhancing zero-shot learning in medical imaging: integrating clip with advanced techniques for improved chest x-ray analysis
Prakhar Bhardwaj, Sheethal Bhat, Andreas Maier
Due to the large volume of medical imaging data, advanced AI methodologies are needed to assist radiologists in diagnosing thoracic diseases from chest X-rays (CXRs). Existing deep…
Review of Zero-Shot and Few-Shot AI Algorithms in The Medical Domain
Maged Badawi, Mohammedyahia Abushanab, Sheethal Bhat +1
In this paper, different techniques of few-shot, zero-shot, and regular object detection have been investigated. The need for few-shot learning and zero-shot learning techniques is…