abide

Fader Networks for domain adaptation on fMRI: ABIDE-II study

Automated blind MRI quality assessment using 3D CNN + FC deep learning. Trains on ABIDE-1 (15 sites), achieves SOTA transfer to novel sites and Glioblastoma MRI from TCIA.

Preparatory scripts for BIDS tabular phenotypic data in large neuroimaging datasets.

[MICCAIW 2025 Best Paper Award] official code of BrainNetMLP for functional brain network classification, which is accepted by the 1st Efficient Workshop of MICCAI 2025.

基于功能脑网络多模版学习的精神疾病辅助诊断算法研究

Multi-task learning of functional connectivity on the ABIDE dataset.

ASD classification from resting-state fMRI using functional brain connectivity using Random Forest and 3D brain visualisation on ABIDE I.

Replication code for 'The IQ-Motion Confound in Multi-Site Autism fMRI May Be Inflated by Site-Correlated Measurement Uncertainty' (Soliman, 2026). Errors-in-variables correction via Probability Cloud Regression on ABIDE-I.

Dual-pathway deep QC for brain MRI: DNN on IQMs + ResNet-18 visual artifact extraction. Validated on ABIDE-1 and DS030 with GradCAM artifact localization.

💀💭 Tool to visualise resting-state fMRI connectivity

Autism Prediciton model based on research paper

ASD detection system built on the ABIDE neuroimaging dataset — multi-atlas feature extraction, tangent connectivity, and an MLP classifier achieving 76% accuracy and 0.818 AUC. Active development.

Graph-based classification of Autism Spectrum Disorder from resting-state fMRI functional connectivity data (ABIDE dataset, n=303)

B.Tech 2023 capstone — CNN vs Vision Transformer for ASD detection from structural MRI (ABIDE-I, 1,067 subjects). Established CNN baseline (AUC 0.978) and documented ViT failure from scratch. Foundation for graduate Phase 2.

Interpretable Machine Learning framework for early detection of Autism Spectrum Disorder using T1-weighted MRI scans from the ABIDE II dataset with Random Forest, XGBoost, SHAP, and AAL Atlas.

Code for Bachelor Thesis "Unveiling Hidden Features: Multimodal Integration Using Cross-Modal Variational Autoencoders for the Identification of Stratification in ABIDE"

3D CNN classification of Autism Spectrum Disorder from resting-state fMRI (ABIDE I, ReHo maps)