Search Results for author:
Found 4 papers, 0 papers with code
DiCoM -- Diverse Concept Modeling towards Enhancing Generalizability in Chest X-Ray Studies
In this context, we introduce Diverse Concept Modeling (DiCoM), a novel self-supervised training paradigm that leverages a student teacher framework for learning diverse concepts and hence effective representation of the CXR data.
Zero-Shot Pediatric Tuberculosis Detection in Chest X-Rays using Self-Supervised Learning
We demonstrate improvements in TB detection performance ($\sim$12. 7% and $\sim$13. 4% top AUC/AUPR gains in adults and children, respectively) when conducting self-supervised pre-training when compared to fully-supervised (i. e., non pre-trained) ViT models, achieving top performances of 0. 959 AUC and 0. 962 AUPR in adult TB detection, and 0. 697 AUC and 0. 607 AUPR in zero-shot pediatric TB detection.
Quantitative Metrics for Benchmarking Medical Image Harmonization
However, benchmarking the effectiveness of harmonization techniques has been a challenge due to the lack of widely available standardized datasets with ground truths.
Partitioned Shape Modeling with On-the-Fly Sparse Appearance Learning for Anterior Visual Pathway Segmentation
In this work, we propose a partitioned joint statistical shape model approach with sparse appearance learning for the segmentation of healthy and pathological AVP.
