In the dynamic landscape of medical artificial intelligence, this study explores the vulnerabilities of the Pathology Language-Image Pretraining (PLIP) model, a Vision Language Foundation model, under targeted attacks like PGD adversarial attack.

BIBM2023 regular paper for "Addressing Sparse Annotation: a Novel Semantic Energy Loss for Tumor Cell Detection from Histopathologic Images"

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

Our solution for BreastPathQ cancer cellularity challenge

Deep learning analysis of the PCAM dataset

KBSMC_colon_tma_cancer_grading_1024_dataset

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

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

Redes Neurais - Detecção de Câncer em Imagens

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.

Awesome List of Digital and Computational Pathology Resources

Classification of Breast Cancer using Histopathological Images

Tensorflow 2.0 implementation of the M-LSA method.

Data Exploration , visualization and deep learning detection as well classification of Histopathology image of cancers by CNN-VGG16 on tcga data set

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

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.

Curation of resources of McMedHacks Workshop