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Issue DateTitleAuthor(s)
27-Mar-2020Estimating Uncertainty and Interpretability in Deep Learning for CoronavirusGhoshal, B; Tucker, A
15-Jul-2020Using topological data analysis and pseudo time series to infer temporal phenotypes from electronic health recordsDagliati, A; Geifman, N; Peek, N; Holmes, JH; Sacchi, L; Bellazzi, R; Sajjadi, SE; Tucker, A
17-Apr-2020Using the Lexicon from Source Code to Determine Application DomainsCapiluppi, A; Ajienka, N; Ali, N; Arzoky, M; Counsell, S; Destefanis, G; Miron, A; Nagaria, B; Neykova, R; Shepperd, M; Swift, S; Tucker, A
22-Oct-2020Estimating Uncertainty in Deep Learning for Reporting Confidence to Clinicians in Medical Image Segmentation and Diseases DetectionGhoshal, B; Tucker, A; Sanghera, B; Wong, WL
2-Feb-2021Reasoning About Neural Network Activations: An Application in Spatial Animal Behaviour from Camera Trap ClassificationsEvans, BC; Tucker, A; Wearn, OR; Carbone, C
29-Mar-2020Opening the black box: Personalizing type 2 diabetes patients based on their latent phenotype and temporal associated complication rulesYousefi, L; Swift, S; Arzoky, M; Saachi, L; Chiovato, L; Tucker, A
21-Aug-2021DeepHistoClass: A novel strategy for confident classification of immunohistochemistry images using Deep LearningGhoshal, B; Hikmet, F; Pineau, C; Tucker, A; Lindskog, C
13-Jan-2022Harnessing Large-Scale Herbarium Image Datasets Through Representation LearningWalker, BE; Tucker, A; Nicolson, N
9-Nov-2020Generating High-Fidelity Synthetic Patient Data for Assessing Machine Learning Healthcare SoftwareTucker, A; Wang, Z; Rotalinti, Y; Myles, P
14-Mar-2022Identifying Latent Variables in Dynamic Bayesian Networks with Bootstrapping Applied to Type 2 Diabetes Complication PredictionLeila, Y; Tucker, A