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This repository contains code for the MOTHER-DB.org specifically related to image segmentation and annotation work flows. MOTHER-DB is a database, meta-data archive and set of programs for annotating and storing ovary histology images from a wide range of species.
Using Convolutional Neural Network and transfer learning to create an accurate classification model of Invasive Ductal Carcinoma in Histology Images. Then deploying this model into a simple front end.
Deep-learning based classification pipeline for subtyping lung tumors from histology. Study design and codebase to analyze the impact of nucleus segmentation on subtyping.
HistoSeg is an Encoder-Decoder DCNN which utilizes the novel Quick Attention Modules and Multi Loss function to generate segmentation masks from histopathological images with greater accuracy. This repo contains the code to Test and Train the HistoSeg