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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…
A Selective Quantization Tuner for ONNX Models
Nikolaos Louloudakis, Ajitha Rajan
Quantization reduces the precision of deep neural networks to lower model size and computational demands, but often at the expense of accuracy. Fully quantized models can suffer si…
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…
DiTOX: Fault Detection and Localization in the ONNX Optimizer
Nikolaos Louloudakis, Ajitha Rajan
The ONNX Optimizer, part of the official ONNX repository and widely adopted for graph-level model optimizations, is used by default to optimize ONNX models. Despite its popularity,…
Metric-Guided Synthesis of Class Activation Mapping
Alejandro Luque-Cerpa, Elizabeth Polgreen, Ajitha Rajan +1
Class activation mapping (CAM) is a widely adopted class of saliency methods used to explain the behavior of convolutional neural networks (CNNs). These methods generate heatmaps t…
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…