5 papers
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…
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…
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…