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.

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