5 papers
Radiologist-Guided Causal Concept Bottleneck Models for Chest X-Ray Interpretation
Amy Rafferty, Rishi Ramaesh, Ajitha Rajan
Concept Bottleneck Models (CBMs) in medical imaging aim to improve model interpretability by predicting intermediate clinical concepts before final diagnoses. However, most existin…
Clinically Aware Synthetic Image Generation for Concept Coverage in Chest X-ray Models
Amy Rafferty, Rishi Ramaesh, Ajitha Rajan
Deep learning models for chest X-ray diagnosis are constrained by limited coverage of clinically meaningful concept combinations in publicly available training datasets. While synt…
Limitations of Public Chest Radiography Datasets for Artificial Intelligence: Label Quality, Domain Shift, Bias and Evaluation Challenges
Amy Rafferty, Ajitha Rajan
Artificial intelligence has shown significant promise in chest radiography, where deep learning models can approach radiologist-level diagnostic performance. Progress has been acce…
Explainability Through Human-Centric Design for XAI in Lung Cancer Detection
Amy Rafferty, Rishi Ramaesh, Ajitha Rajan
Deep learning models have shown promise in lung pathology detection from chest X-rays, but widespread clinical adoption remains limited due to opaque model decision-making. In prio…
CoRPA: Adversarial Image Generation for Chest X-rays Using Concept Vector Perturbations and Generative Models
Amy Rafferty, Rishi Ramaesh, Ajitha Rajan
Deep learning models for medical image classification tasks are becoming widely implemented in AI-assisted diagnostic tools, aiming to enhance diagnostic accuracy, reduce clinician…