Research-pages | MINDS LAB

AI Foundations & Translational Imaging Science

Tue, 28 Jul 2026 00:00:00 +0000

This pillar spans representation learning, AI algorithms for image analysis, and AI-enabled platforms that support translational imaging research - from foundational methods through to tools deployed on real clinical data.

Representation learning

Abdalla Bani - Scalable Orthonormal Projective NMF via Diversified Stochastic Optimization (Information Processing in Medical Imaging (IPMI), 2023); code released as dsopNMF.
Pan Xiao (alum) - A generative-discriminative deep learning approach to classify radiology reports based on the presence of follow up recommendations (Medical Imaging 2023: Imaging Informatics for Healthcare, Research, and Applications); implementation released as SC-VAE.
Peijie Qiu - VAE framework based on Wasserstein barycenters (AAAI Conference on Artificial Intelligence, 2025, oral).

Image analysis algorithms

Satrajit Chakrabarty (alum) - MRI-based classification of IDH mutation and 1p/19q codeletion status of gliomas using a 2.5 D hybrid multi-task convolutional neural network (Neuro-Oncology Advances, 2023).
Satrajit Chakrabarty (alum) - Non-invasive classification of IDH mutation status of gliomas from multi-modal MRI using a 3D convolutional neural network (Medical Imaging 2023: Computer-Aided Diagnosis).
Jin Yang (alum) - Abdominal CT pancreas segmentation using multi-scale convolution with aggregated transformations (Medical Imaging 2023: Computer-Aided Diagnosis), and volumetric segmentation via Dynamic Large Kernel / Dynamic Feature Fusion, with Peijie Qiu; released as DLK (D-Net).
Peijie Qiu - QCResUNet: Joint Subject-Level and Voxel-Level Prediction of Segmentation Quality (Medical Image Computing and Computer-Assisted Intervention, 2023); released as QCResUNet.

Translational platforms

Shawn Fan (alum) - AI tool to interpret amyloid PET scans (Radiology, 2024); released as AmyloidPETNet, an end-to-end pipeline classifying amyloid positivity directly from minimally processed PET scans without a companion structural MRI.
Pan Xiao and collaborators - AI tool to analyze radiology reports for follow-up recommendations (preliminary version: Medical Imaging 2023: Imaging Informatics for Healthcare, Research, and Applications; journal extension: Radiology, 2025); released as Follow-up-recommendations. (External collaborator, not a current lab member.)
Satrajit Chakrabarty (alum) - modular, AI-driven pipelines for large-scale, reproducible imaging research, contributed as part of the NIH-funded I3CR center (JCO Clinical Cancer Informatics, 2023).

2023 Python projects

dsopNMF

This repository accompanies the IPMI 2023 work on scalable orthonormal projective NMF via diversified stochastic optimization.

It provides opNMF/sopNMF implementations with stochastic and DPP-based sampling strategies to improve scalability for large neuroimaging datasets.

2024 Python projects

SC-VAE

SC-VAE is the official implementation of Sparse Coding-based Variational Autoencoder with learned ISTA for representation learning.

The repository contains model training code, configuration-based experiments, and associated resources for reproducing results from the published work.

2023 Python projects

QCResUNet

QCResUNet is a deep learning framework for joint subject-level and voxel-level segmentation quality prediction in medical imaging.

The repository includes model architectures, training scripts, and quality control workflows to support robust segmentation assessment.

2024 Python projects

DLK (D-Net)

This repository provides the official implementation of D-Net, introducing Dynamic Large Kernel (DLK) and Dynamic Feature Fusion (DFF) modules for volumetric medical image segmentation.

The framework combines hierarchical transformer representations with adaptive multi-scale convolutional components to improve segmentation performance while keeping computational complexity practical.

2024 Python / R projects

AmyloidPETNet

AmyloidPETNet is an end-to-end deep learning pipeline for classifying amyloid positivity directly from minimally processed brain PET scans, without requiring companion structural MRI.

The repository includes pretrained weights for inference, a training pipeline for adapting the model to new datasets, and preprocessing utilities for NIfTI PET frames and optional visualization outputs.

2025 Python projects

Follow-up recommendations

This repository provides code for large-scale machine learning evaluation of follow-up recommendation identification in radiology reports.

It includes dataset handling, model training, and benchmarking workflows supporting the Radiology 2025 study on report-level recommendation extraction.

Back to Research

Clinical Applications

Tue, 28 Jul 2026 00:00:00 +0000

We bring the methods above to bear on specific disease areas - Alzheimer’s disease, brain tumors, and psychiatric disorders - with the goal of personalized diagnostics and improved disease understanding.

Alzheimer’s Disease & Aging - amyloid/tau staging and biomarker modeling (Tom Earnest), AD heterogeneity subtyping in clinical (Gordon An) and research (Sayantan Kumar) cohorts, amyloid PET classification (Shawn Fan, AmyloidPETNet), and preclinical AD prediction from multimodal amyloid PET and MRI (yang_preclinical_AD_prediction, Jin Yang/Braden Yang† - Neurobiology of Aging, 2026).
Neuro-Oncology - molecular subtype prediction for gliomas (Satrajit Chakrabarty) and organ/tumor segmentation frameworks (Jin Yang, Peijie Qiu).
Psychiatric Disorders - risk factors for psychopathology across development (Robert J. Jirsaraie).
Brain Development - white matter maturation in neonates (Nazeri et al., external collaboration).
Clinical Imaging Informatics - automated extraction of follow-up recommendations from radiology reports (Pan et al., external collaboration) and segmentation quality control deployed at scale (Peijie Qiu).

2024 Python / R projects

AmyloidPETNet

AmyloidPETNet is an end-to-end deep learning pipeline for classifying amyloid positivity directly from minimally processed brain PET scans, without requiring companion structural MRI.

2026 Python projects

yang_preclinical_AD_prediction

This repository supports preclinical Alzheimer's disease prediction from multimodal amyloid PET and MRI.

The work combines imaging biomarkers and machine learning models for risk stratification in the preclinical AD setting.

2025 Python projects

Follow-up-recommendations

This repository provides code for large-scale machine learning evaluation of follow-up recommendation identification in radiology reports.

It includes dataset handling, model training, and benchmarking workflows supporting the report-level recommendation extraction studies referenced on this page.

2023 Python projects

QCResUNet

QCResUNet is a deep learning framework for joint subject-level and voxel-level segmentation quality prediction in medical imaging.

The repository includes model architectures, training scripts, and quality control workflows to support robust segmentation assessment at scale.

Back to Research

Machine Learning for Brain Health Across the Lifespan

Tue, 28 Jul 2026 00:00:00 +0000

The lab builds machine learning frameworks to model brain structure in health and disease across the lifespan, drawing on large-scale, multimodal neuroimaging datasets to capture heterogeneity in structure, function, and disease progression. A central emphasis is on unsupervised and semi-supervised methods — normative modeling, non-negative matrix factorization (NMF), and deep generative models — to uncover latent patterns and subtypes tied to specific outcomes or disease states.

People and Publications

Tom Earnest — biological staging of tau pathology in Alzheimer’s disease (Alzheimer’s & Dementia, 2024).
Gordon An — characterization of Alzheimer’s disease heterogeneity in clinical cohorts (Alzheimer’s Research & Therapy, 2025).
Sayantan Kumar (alum) — Improving Normative Modeling for Multi-modal Neuroimaging Data using mixture-of-product-of-experts variational autoencoders and Normative Modeling using Multimodal Variational Autoencoders to Identify Abnormal Brain Structural Patterns in Alzheimer Disease.
Robert J. Jirsaraie (alum) — A systematic review of multimodal brain age studies: Uncovering a divergence between model accuracy and utility.
Nazeri et al. — white matter development in neonates (Neuron, 2022; external collaborator, not a current lab member).

2026 Python / R projects

Amyloid and tau staging

This repository develops data-driven biological staging models for Alzheimer's disease by combining amyloid and tau PET analyses with non-negative matrix factorization (NMF) to identify reproducible spatial pathology factors.

It includes preprocessing workflows, shared NMF factors, projection utilities for new datasets, and staging code to derive amyloid and tau severity labels for external cohorts.

2026 R / Python projects

Preclinical AD prediction

This repository contains code for predicting future cognitive impairment in preclinical Alzheimer's disease using multimodal amyloid PET and MRI features across multiple cohorts.

It includes data processing, subject selection, machine learning training and evaluation workflows, and figure/table generation for the Neurobiology of Aging 2026 study.

2023 Python / R projects

AD biomarker modeling

This repository evaluates variability in AT(N) biomarker operationalizations for predicting cognition in Alzheimer's disease using multimodal imaging and clinical data.

It provides reproducible modeling scripts, experiment pipelines, and figure-generation utilities for cross-validated regression and SVM analyses.

Back to Research

Research-pages | MINDS LAB

AI Foundations & Translational Imaging Science

Related GitHub Projects

Clinical Applications

Related GitHub Projects

Machine Learning for Brain Health Across the Lifespan

People and Publications

Related GitHub Projects