@inproceedings{04353ce03197415996d83d7eb9a1b477,
title = "Whole MILC: Generalizing Learned Dynamics Across Tasks, Datasets, and Populations",
abstract = "Behavioral changes are the earliest signs of a mental disorder, but arguably, the dynamics of brain function gets affected even earlier. Subsequently, spatio-temporal structure of disorder-specific dynamics is crucial for early diagnosis and understanding the disorder mechanism. A common way of learning discriminatory features relies on training a classifier and evaluating feature importance. Classical classifiers, based on handcrafted features are quite powerful, but suffer the curse of dimensionality when applied to large input dimensions of spatio-temporal data. Deep learning algorithms could handle the problem and a model introspection could highlight discriminatory spatio-temporal regions but need way more samples to train. In this paper we present a novel self supervised training schema which reinforces whole sequence mutual information local to context (whole MILC). We pre-train the whole MILC model on unlabeled and unrelated healthy control data. We test our model on three different disorders (i) Schizophrenia (ii) Autism and (iii) Alzheimers and four different studies. Our algorithm outperforms existing self-supervised pre-training methods and provides competitive classification results to classical machine learning algorithms. Importantly, whole MILC enables attribution of subject diagnosis to specific spatio-temporal regions in the fMRI signal.",
keywords = "Deep learning, Resting state fMRI, Self-supervised, Transfer learning",
author = "Usman Mahmood and Rahman, {Md Mahfuzur} and Alex Fedorov and Noah Lewis and Zening Fu and Calhoun, {Vince D.} and Plis, {Sergey M.}",
note = "Funding Information: Acknowledgement. This study was in part supported by NIH grants 1R01AG063153 and 2R01EB006841. We{\textquoteright}d like to thank and acknowledge the open access data platforms and data sources that were used for this work, including: Human Connectome Project (HCP), Open Access Series of Imaging Studies (OASIS), Autism Brain Imaging Data Exchange (ABIDE I), Function Biomedical Informatics Research Network (FBIRN) and Centers of Biomedical Research Excellence (COBRE). Funding Information: This study was in part supported by NIH grants 1R01AG063153 and 2R01EB006841. We{\textquoteright}d like to thank and acknowledge the open access data platforms and data sources that were used for this work, including: Human Connectome Project (HCP), Open Access Series of Imaging Studies (OASIS), Autism Brain Imaging Data Exchange (ABIDE I), Function Biomedical Informatics Research Network (FBIRN) and Centers of Biomedical Research Excellence (COBRE). Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020 ; Conference date: 04-10-2020 Through 08-10-2020",
year = "2020",
doi = "10.1007/978-3-030-59728-3_40",
language = "English (US)",
isbn = "9783030597276",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "407--417",
editor = "Martel, {Anne L.} and Purang Abolmaesumi and Danail Stoyanov and Diana Mateus and Zuluaga, {Maria A.} and Zhou, {S. Kevin} and Daniel Racoceanu and Leo Joskowicz",
booktitle = "Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings",
address = "Germany",
}