DataCurator (coming soon)
Code supporting our two papers: MedIA - Guidelines and evaluation of clinical explainable AI in medical image analysis; and AAAI: Evaluating Explainable AI on a Multi-Modal Medical Imaging Task: Can Existing Algorithms Fulfill Clinical Requirements?
Code supporting our CMIG paper: Active Deep Learning from a Noisy Teacher for Semi-supervised 3D Image Segmentation: Application to COVID-19 Pneumonia Infection in CT
Code supporting our Medical Image Analysis paper: Skin3D: Detection and Longitudinal Tracking of Pigmented Skin Lesions in 3D Total-Body Textured Meshes
(Mengliu Zhao, Jeremy Kawahara, Kumar Abhishek, and Sajjad Shamanian)
Code supporting our paper Computers in Biology and Medicine 2021 paper: Learning-to-Augment Strategy using Noisy and Denoised Data: Improving Generalizability of Deep CNN for the Detection of COVID-19 in X-ray Images
Code supporting our MICCAI 2019 paper: Generalizable Feature Learning in the Presence of Data Bias and Domain Class Imbalance: Application to Skin Lesion Classification
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.
3D point clouds of super resolution molecule localization microscopy (SMLM) of Cav1 protein in prostate cancer (PC3) and CAVIN1/PTRF transfected PC3 (PC3-PTRF) cells
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