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20182026
most citedCan Artificial Intelligence Reliably Report Chest X-Rays?: Radiologist Validation of an Algorithm trained on 2.3 Million X-Rays

11 citations · 11 across the 5 of their papers we have counts for

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Showing 2026Show all

5 papers · 1 filter

cs.AI2026

QureRadEmbed: Structuring Radiological Similarity through Attribute and Reasoning Supervision

Janhavi Prabhu, Sahil, Shivam Ashok Shukla +1

Radiological similarity depends on disease relationships and on fine details such as laterality, lobe, severity, size, and certainty. Broad biomedical similarity can overlook these…

cs.CV2026

Med-AR: Autoregressive Vision-Language Pretraining for Long-Tailed Chest X-Ray Classification and Uncertainty-Aware Evaluation

Janhavi Prabhu, Sahil, Akshay V +3

Long-tailed chest X-ray classification requires visual representations that capture both common abnormalities and subtle, infrequent findings. We propose Med-AR-8B and Med-AR-2B, t…

cs.CV2026

Anatomy-Decomposed Chest Computed Tomography (CT) Projections as Scalable Supervision for Bone Suppression in Chest Radiographs

Mrunmay Angaitkar, Piyush Kumar, Aarjav Satia +4

Bone overlap can obscure abnormalities in chest radiographs, while scarce paired training data limit supervised bone suppression. We address this challenge with a digitally reconst…

cs.CV2026

DiffusionXRay: A Diffusion and GAN-Based Approach for Enhancing Digitally Reconstructed Chest Radiographs

Aryan Goyal, Ashish Mittal, Pranav Rao +2

Deep learning-based automated diagnosis of lung cancer has emerged as a crucial advancement that enables healthcare professionals to detect and initiate treatment earlier. However,…

cs.CV2026

A Diffusion-Driven Fine-Grained Nodule Synthesis Framework for Enhanced Lung Nodule Detection from Chest Radiographs

Aryan Goyal, Shreshtha Singh, Ashish Mittal +3

Early detection of lung cancer in chest radiographs (CXRs) is crucial for improving patient outcomes, yet nodule detection remains challenging due to their subtle appearance and va…