Open Source Codes

We are trying our best to make our codes publicly available as possible for reproducibility and benchmarking purposes. However, there are many constraints including patenting, and collaboration with industry partners, where we can not simply share the code.

Federated Learning

Our recent algorithms in FL with Medical Imaging

Anomaly Detection

Implemntation of our comparative study on anomaly detection

Capsule Networks

MICCAI2018 Workshop: Capsule Networks against Medical Imaging Data Challenges

Polyp and Artifact Detector

Our Paper: A learning without forgetting approach to incorporate artifact knowledge in polyp localization tasks


Implemntation of our recent paper on Whole Brain Segmentation and COVID-19 CT Lung Segmentation using RandOm lAyer Mixup in Semi-Supervised Learning

Stain Normlization

ISBI2019: StainGAN: Stain Style Transfer for Digital Histological Images