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

An explainable three dimension framework to uncover learning patterns: A unified look in variable sulci recognition

1 code implementation • 2 Sep 2023 • Michail Mamalakis, Heloise de Vareilles, Atheer AI-Manea, Samantha C. Mitchell, Ingrid Arartz, Lynn Egeland Morch-Johnsen, Jane Garrison, Jon Simons, Pietro Lio, John Suckling, Graham Murray

With respect to this mathematical formulation, we propose a 3D explainability framework aimed at validating the outputs of deep learning networks in detecting the paracingulate sulcus an essential brain anatomical feature.

Anatomy Dimensionality Reduction +1