Multi-source Domain Adaptation Techniques for Mitigating Batch Effects: A Comparative Study

Rohan Panda, Sunil Vasu Kalmady, Russell Greiner

Keywords: Resting-state fMRI, multi-source domain adaptation, batch effects, deep learning, ADHD, ASD

TL;DR: A study on comparing various state-of-the-art multi-source domain adaptation techniques and their performance on public rs-fMRI datasets.

Abstract: The past decade has seen an increasing number of applications of deep learning (DL) techniques to biomedical fields, especially in neuroimaging-based analysis. Such DL-based methods are generally data-intensive and require large number of training instances, which might be infeasible to acquire from a single acquisition site, especially for data such as fMRI scans, due to the time and costs that they demand. We can attempt to address this issue by combining fMRI data from various sites, thereby creating a bigger heterogeneous dataset. Unfortunately, the inherent differences in the combined data, known as batch effects, often hampers learning a model. To mitigate this issue, techniqes such as multi-source domain adaptation (MSDA) aim at learning an effective classification function that uses (learned) domain-invariant latent features. This paper analyzes and compares the performance of various popular MSDA methods (MDAN, DARN, MDMN, M$^3$SDA) at predicting different labels (illness, age and sex) of images from several public rs-fMRI datasets: ABIDE I and ADHD-200. It also evaluates the impact of various conditions such as: class imbalance, number of sites along with a comparison of the degree of adaptation of each of the methods, thereby presenting the effectiveness of MSDA models in neuroimaging based applications.

Registration: I acknowledge that publication of this at MIDL and in the proceedings requires at least one of the authors to register and present the work during the conference.

Authorship: I confirm that I am the author of this work and that it has not been submitted to another publication before.

Paper Type: validation/application paper

Primary Subject Area: Transfer Learning and Domain Adaptation

Secondary Subject Area: Unsupervised Learning and Representation Learning