Contrastive-Adversarial and Diffusion: Exploring pre-training and fine-tuning strategies for sulcal identification

no code implementations • 29 May 2024 • Michail Mamalakis, Héloïse de Vareilles, Shun-Chin Jim Wu, Ingrid Agartz, Lynn Egeland Mørch-Johnsen, Jane Garrison, Jon Simons, Pietro Lio, John Suckling, Graham Murray

Techniques like adversarial learning, contrastive learning, diffusion denoising learning, and ordinary reconstruction learning have become standard, representing state-of-the-art methods extensively employed for fully training or pre-training networks across various vision tasks.

Solving the enigma: Deriving optimal explanations of deep networks

no code implementations • 16 May 2024 • Michail Mamalakis, Antonios Mamalakis, Ingrid Agartz, Lynn Egeland Mørch-Johnsen, Graham Murray, John Suckling, Pietro Lio

In this study, for the first time, we propose a novel framework designed to enhance the explainability of deep networks, by maximizing both the accuracy and the comprehensibility of the explanations.

Binary Classification Decision Making