An educational game to familiarize yourself with digital slides of tissue stained with H&E

Curation of resources of McMedHacks Workshop

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

Breast Cell Nuclei Segmentation, project work done as a part of Internship at Machine Vision Lab, IIT Roorkee.

TCNN: A Transformer Convolutional Neural Network for artifact classification in whole slide images

Our solution for BreastPathQ cancer cellularity challenge

KBSMC_colon_tma_cancer_grading_1024_dataset

Different networks, 2D CNN, 1X1 filter, VGG16 and Inception network is used for predicting breast cancer from histopathological images. Performance of different models are compared and found out that InceptionNet performed the best.

Code for "Difficulty Translation in Histopathology Images" full paper at Artificial Intelligence in Medicine (AIME) 2020.

Redes Neurais - Detecção de Câncer em Imagens

Awesome List of Digital and Computational Pathology Resources

Breast Cancer Histopathological Image Classification

Deep Learning Model to Classify Histopathological Cancer Tissues

Code for "Difficulty Translation in Histopathology Images" full paper at Artificial Intelligence in Medicine (AIME) 2020.

Pathology Atlas (English version of Patoloji Atlası)

Following repository demonstrates machine learning architectures that can correctly classify lesions between LM and AMH. Overall, our methods showcase the potential for computer-aided diagnosis in dermatology, which, in conjunction with remote acquisition can expand the range of diagnostic tools in the community. This code is implemented using K…

This file contains the code for my work. "Reference5" is the image used for normalization. The "stained_dataset_test" is the final result of the model. The "lc25000_dataset_test" is the result of my model on the Augmentor dataset. Lastly, the "vahadane_stain_normalization" is my normalization code.

Deep learning based method called hist2RNA to predict the expression of genes using digital images of stained tissue samples

An automated CAD (Computer aided diagnosis) system for the classification of the breast histopathology images. It contains machine learning (feature extraction) and deep learning (transfer learning) pipelines.