11 papers
CONQuER: Hardware-Aware Mixed-Precision Quantisation with Online-Calibrated Surrogates
Aidan Dakhama, Ajitha Rajan
Deploying deep neural networks on resource-constrained hardware relies on mixed-precision quantisation (MPQ). current deployment toolchains severely fragment this process. Quantisa…
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…
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…
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…
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…