eshasadia/drca_net

This Repository is project directory of the Distance Regression and Classification Network (DRCA-Net).DRCA-net is convolutional neural network developed using tensorflow and tensorpack. In this work i perform nuclei instance segmentation and type classification in parallel using a end-to-end trainable convolutional neural network.

DRCA-Net: Nuclei Instance Segmentation and Type Classification in Digitized Histology Images

DRCA-net(distance regression and classification with attention) is a convolutional neural network developed using tensorflow and tensorpack. In this work i perform nuclei instance segmentation and type classification in parallel using an end-to-end trainable multi-branch convolutional neural network which uses attention mechanism for feature refinment and higher accuracy.We have experimented our network with four diffenet H&E stained multi-tissue datasets.

DRCA-Net model ; 3 branch acrchitecture along with attention units design and placement.

Overall architecture of the model showcasing base shared feature encoder and three different attention incorporated branches

Visualization of network predictions overlayed on respective images

Classification results on PanNuke dataset

Instance Segmentation results on the four datasets that we have used

Steps to run this model

Install all dependecies mentioned in the requirements text file

pip install -r requirements.txt

Dataset download links

The project is configured to run PanNuke dataset

CoNSeP (https://warwick.ac.uk/fac/sci/dcs/research/tia/data/hovernet/)

Kumar,TNBC,CPM-15 and CPM-17 (https://drive.google.com/open?id=1l55cv3DuY-f7-JotDN7N5nbNnjbLWchK).

PanNuke dataset (https://warwick.ac.uk/fac/sci/dcs/research/tia/data/pannuke/)

Preprocessing steps before training

Use the python scripts in the panNukePrecprocess/ folder to process panNuke dataset. make necessary directory configurations.
Create patches by running the file extract_patches/ , provide the directory paths in the script file.

Training

Change hypermeters in the opt/hover.py file, select batch size according to your available GPU memmory.

In config.py file , enter paths of extracted training and validation patches.

run file train.py using command python train.py --gpu="0,1" select gpus according to your specific scnenerio.

Inference and performance calculation

run file infer.py after training , post process your model output by running script process.py

finally setup directories in the compute_stats.py file and run this file by providing mode to --mode flag either instance or type

For PanNuke classification calculations use file convert.py to create .mat format ground-truth masks necessary for calculation prediction performance.