3 citations · 10 across the 11 of their papers we have counts for
13 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…
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…
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…
Explainable Attribute-Based Speaker Verification
Xiaoliang Wu, Chau Luu, Peter Bell +1
This paper proposes a fully explainable approach to speaker verification (SV), a task that fundamentally relies on individual speaker characteristics. The opaque use of speaker att…
Leveraging Expert Input for Robust and Explainable AI-Assisted Lung Cancer Detection in Chest X-rays
Amy Rafferty, Rishi Ramaesh, Ajitha Rajan
Deep learning models show significant potential for advancing AI-assisted medical diagnostics, particularly in detecting lung cancer through medical image modalities such as chest…
FetaFix: Automatic Fault Localization and Repair of Deep Learning Model Conversions
Nikolaos Louloudakis, Perry Gibson, José Cano +1
Converting deep learning models between frameworks is a common step to maximize model compatibility across devices and leverage optimization features that may be exclusively provid…
Hawkeye: Change-targeted Testing for Android Apps based on Deep Reinforcement Learning
Chao Peng, Zhengwei Lv, Jiarong Fu +4
Android Apps are frequently updated to keep up with changing user, hardware, and business demands. Ensuring the correctness of App updates through extensive testing is crucial to a…