shivani-shrivastava / h-vae

Implementation of "Synthetic Data Generation and Classification of Histopathological Images"

Synthetic Data Generation and Classification of Histopathological Images

This repository contains the implementation for the work "Synthetic Data Generation Of Histopathological Images" presented at the XXII International Conference on Mechanics in Medicine and Biology, in Bologna (2022).
The data set used in the study is publicly available for download at: Kaggle. In order to launch the training scripts, it is assumed that the data is available in the directory: 'datasets/breast-histopathology/IDC_regular_ps50_idx5'.
The data set in tfds format is available here: Google Drive

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Overview

Training

Please refer to the scripts provided in the table corresponding to some training tasks possible using the code.

Task	Reference
Training VAE (1st stage)	training/vae_train.py
Training DDPM (2nd stage)	training/ddpm_train.py
Classifier (Diagnostic)	training/classifier.py

Generation

Task	Reference
Generation from VAE	generation/vae_generate.py
Generation from VAE + DDPM	generation/ddpm_generate.py

Training

Generation

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Install

conda config --add channels conda-forge

conda create --name H-VAE --file requirements.txt

conda activate H-VAE

conda install -c anaconda conda-build

conda develop PATH-TO-DIRECTORY/H-VAE/

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Since our model uses diffusion models please consider citing the original DiffuseVAE Diffusion model, DDPM and VAE papers.