Dataset of 9536 H&E-stained patches for colorectal polyps classification and adenomas grading | ICIP21 https://doi.org/10.1109/ICIP42928.2021.9506198

adaptive color deconvolution for paper "Zheng et al., CMPB, 2019"

MIA-DDTNet: A Dense Dual-Task Network for Tumor-infiltrating Lymphocyte Detection and Segmentation in Histopathological Images of Breast Cancer

Codes and Data for CVSM Group: 1. IAST: Instance Adaptive Self-training for Unsupervised Domain Adaptation (ECCV 2020); 2.

Histopathological image processing and segmentation

This is my second ML project. The primary challenge in this competition is to create a model that can accurately classify microscopic images of lymph node sections as containing metastatic tissue or not. This repository includes the code for building and training a CNN to tackle this problem. The model is developed in Python using TensorFlow/Keras

Automatic Annotation of FTIR tissue images

In this project, we have implemented various deep learning algorithms like Transfer Learning, CNN and MLP, and some other classification algorithms like Random Forest, LightGBM etc. to classify histopathological images of lymph nodes and reduce the human intervention yet providing accurate results.

cancer detection from histopathological images

Histopathological Metastatic Cancer Detection, using Image Processing and Machine Learning