27 citations · 181 across the 39 of their papers we have counts for
41 papers
Guidelines For The Choice Of The Baseline in XAI Attribution Methods
Cristian Morasso, Giorgio Dolci, Ilaria Boscolo Galazzo +2
Given the broad adoption of artificial intelligence, it is essential to provide evidence that AI models are reliable, trustable, and fair. To this end, the emerging field of eXplai…
State-of-the-Art Stroke Lesion Segmentation at 1/1000th of Parameters
Alex Fedorov, Yutong Bu, Xiao Hu +2
Efficient and accurate whole-brain lesion segmentation remains a challenge in medical image analysis. In this work, we revisit MeshNet, a parameter-efficient segmentation model, an…
Spectral Introspection Identifies Group Training Dynamics in Deep Neural Networks for Neuroimaging
Bradley T. Baker, Vince D. Calhoun, Sergey M. Plis
Neural networks, whice have had a profound effect on how researchers study complex phenomena, do so through a complex, nonlinear mathematical structure which can be difficult for h…
Low-Rank Learning by Design: the Role of Network Architecture and Activation Linearity in Gradient Rank Collapse
Bradley T. Baker, Barak A. Pearlmutter, Robyn Miller +2
Our understanding of learning dynamics of deep neural networks (DNNs) remains incomplete. Recent research has begun to uncover the mathematical principles underlying these networks…
Looking deeper into interpretable deep learning in neuroimaging: a comprehensive survey
Md. Mahfuzur Rahman, Vince D. Calhoun, Sergey M. Plis
Deep learning (DL) models have been popular due to their ability to learn directly from the raw data in an end-to-end paradigm, alleviating the concern of a separate error-prone fe…
Self-supervised multimodal neuroimaging yields predictive representations for a spectrum of Alzheimer's phenotypes
Alex Fedorov, Eloy Geenjaar, Lei Wu +7
Recent neuroimaging studies that focus on predicting brain disorders via modern machine learning approaches commonly include a single modality and rely on supervised over-parameter…