Search Results for author:
Found 4 papers, 1 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.
Deep learning-based lung segmentation and automatic regional template in chest X-ray images for pediatric tuberculosis
In clinical practice, experienced physicians assess TB by examining chest X-rays (CXR).
Computer Aided Detection for Pulmonary Embolism Challenge (CAD-PE)
Objective: To generate a database of annotated computed tomography pulmonary angiographies, use it to compare the sensitivity and false positive rate of current algorithms and to develop new methods that improve such metrics.
Pulmonary Artery–Vein Classification in CT Images Using Deep Learning
In this paper, we present a novel, fully automatic approach to classify vessels from chest CT images into arteries and veins.
Ranked #3 on
Pulmonary Artery–Vein Classification
on SunYs
