4 papers
Beyond Discrimination: Evaluating Probability Calibration in Radiomics-Image Fusion for Renal Cell Carcinoma Classification
Yuan Liang, Morgan May, Abraham Campbell +3
Reliable probability estimates are important for the clinical interpretation of machine-learning classifiers, yet radiomics--image fusion models for renal cell carcinoma (RCC) are…
Impact of Data Augmentation on Confidence Calibration in Melanoma Classification
Morgan May, Simon Caton, Pierpaolo Dondio
Accurately quantifying the predictive uncertainty or improving model calibration plays an important role in medical image classification, in particular in melanoma diagnosis, where…
On Impact of Loss Function on the Performance of Neural Networks in Melanoma Diagnosis
Morgan May, Pierpaolo Dondio, Simon Caton
Melanoma is the deadliest type of skin cancer, whose early diagnosis is crucial for patients' survival. Image classification using deep learning models has shown promising results…
A Case For Noisy Shallow Gate-Based Circuits In Quantum Machine Learning
Patrick Selig, Niall Murphy, Ashwin Sundareswaran R +2
There is increasing interest in the development of gate-based quantum circuits for the training of machine learning models. Yet, little is understood concerning the parameters of c…