128 citations · 178 across the 9 of their papers we have counts for
10 papers
Multimodal normative modeling in Alzheimers Disease with introspective variational autoencoders
Sayantan Kumar, Peijie Qiu, Aristeidis Sotiras
Normative modeling learns a healthy reference distribution and quantifies subject-specific deviations to capture heterogeneous disease effects. In Alzheimers disease (AD), multimod…
Multimodal Variational Autoencoder: a Barycentric View
Peijie Qiu, Wenhui Zhu, Sayantan Kumar +6
Multiple signal modalities, such as vision and sounds, are naturally present in real-world phenomena. Recently, there has been growing interest in learning generative models, in pa…
HiMAL: A Multimodal Hierarchical Multi-task Auxiliary Learning framework for predicting and explaining Alzheimer disease progression
Sayantan Kumar, Sean Yu, Andrew Michelson +2
Objective: We aimed to develop and validate a novel multimodal framework HiMAL (Hierarchical, Multi-task Auxiliary Learning) framework, for predicting cognitive composite functions…
Analyzing heterogeneity in Alzheimer Disease using multimodal normative modeling on imaging-based ATN biomarkers
Sayantan Kumar, Tom Earnest, Braden Yang +10
INTRODUCTION: Previous studies have applied normative modeling on a single neuroimaging modality to investigate Alzheimer Disease (AD) heterogeneity. We employed a deep learning-ba…
AgileFormer: Spatially Agile Transformer UNet for Medical Image Segmentation
Peijie Qiu, Jin Yang, Sayantan Kumar +2
In the past decades, deep neural networks, particularly convolutional neural networks, have achieved state-of-the-art performance in a variety of medical image segmentation tasks.…
Improving Normative Modeling for Multi-modal Neuroimaging Data using mixture-of-product-of-experts variational autoencoders
Sayantan Kumar, Philip Payne, Aristeidis Sotiras
Normative models in neuroimaging learn the brain patterns of healthy population distribution and estimate how disease subjects like Alzheimer's Disease (AD) deviate from the norm.…
Identifying Dementia Subtypes with Electronic Health Records
Sayantan Kumar, Zachary Abrams, Suzanne Schindler +2
Dementia is characterized by a decline in memory and thinking that is significant enough to impair function in activities of daily living. Patients seen in dementia specialty clini…
Normative Modeling using Multimodal Variational Autoencoders to Identify Abnormal Brain Structural Patterns in Alzheimer Disease
Sayantan Kumar, Philip Payne, Aristeidis Sotiras
Normative modelling is an emerging method for understanding the underlying heterogeneity within brain disorders like Alzheimer Disease (AD) by quantifying how each patient deviates…
Self-explaining Neural Network with Concept-based Explanations for ICU Mortality Prediction
Sayantan Kumar, Sean C. Yu, Thomas Kannampallil +3
Complex deep learning models show high prediction tasks in various clinical prediction tasks but their inherent complexity makes it more challenging to explain model predictions fo…
Machine learning for modeling the progression of Alzheimer disease dementia using clinical data: a systematic literature review
Sayantan Kumar, Inez Oh, Suzanne Schindler +3
Objective Alzheimer disease (AD) is the most common cause of dementia, a syndrome characterized by cognitive impairment severe enough to interfere with activities of daily life. We…