Code supporting our MICCAI 2019 paper: Generalizable Feature Learning in the Presence of Data Bias and Domain Class Imbalance: Application to Skin Lesion Classification
(Chris Yoon with Prof. Garbi)
Super-Resolution NETwork Analysis, SuperResNET, is an integrated software for the analysis of 3D single molecule localization microscopy (SMLM) point cloud data. It consists of computational modules to read, pre-process, post-process, quantify, and visualize 3D SMLM data. SuperResNET allows the user to extract, visualize, and analyze biological clusters. SuperResNET is largely based on the original Scientific Reports 2018 and Scientific Reports 2019 works.
(Ismail Khater with Prof. Nabi)
Generating transformations based on illumination information and color imaging of the skin for RGB skin lesion images.
(Kumar Abhishek with Prof. Drew)
An oversampling method for synthetic data generation or similar tasks. It is designed for the challenging case of high-dimensional, non-gaussian data with low sample size