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

TL;DR

This work investigates how different self-supervised pre-training paradigms (adversarial, contrastive, diffusion denoising, and reconstruction) interact with various fine-tuning strategies to optimize a multi-task PCS segmentation/classification problem in IID neuroimaging. Using 3D transformer CNNs on the TOP-OSLO dataset (n=596), the authors compare four pre-training methods across four fine-tuning schemes (Top, Decoder, Full, LoRA) and assess performance in terms of accuracy, time, and memory. They find reconstruction and adversarial pre-training to balance accuracy with efficiency, while diffusion is more resource-intensive; top-tuning delivers strong segmentation performance with favorable computational cost, and LoRA is less effective for this small-region task. The results provide practical guidance for selecting pre-training and fine-tuning configurations to improve IID generalization in medical image segmentation and highlight potential for extension to OOD cohorts. The study also emphasizes the importance of efficient fine-tuning to enable practical deployment in resource-constrained clinical settings.

Abstract

In the last decade, computer vision has witnessed the establishment of various training and learning approaches. 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. The exploration of fine-tuning approaches has emerged as a current focal point, addressing the need for efficient model tuning with reduced GPU memory usage and time costs while enhancing overall performance, as exemplified by methodologies like low-rank adaptation (LoRA). Key questions arise: which pre-training technique yields optimal results - adversarial, contrastive, reconstruction, or diffusion denoising? How does the performance of these approaches vary as the complexity of fine-tuning is adjusted? This study aims to elucidate the advantages of pre-training techniques and fine-tuning strategies to enhance the learning process of neural networks in independent identical distribution (IID) cohorts. We underscore the significance of fine-tuning by examining various cases, including full tuning, decoder tuning, top-level tuning, and fine-tuning of linear parameters using LoRA. Systematic summaries of model performance and efficiency are presented, leveraging metrics such as accuracy, time cost, and memory efficiency. To empirically demonstrate our findings, we focus on a multi-task segmentation-classification challenge involving the paracingulate sulcus (PCS) using different 3D Convolutional Neural Network (CNN) architectures by using the TOP-OSLO cohort comprising 596 subjects.

Figures (2)

• Figure 1: The proposed strategy for complex multi-task learning computer vision tasks.
• Figure 2: Results of pre-training and fine-tuning simulations.