The official implementations of our BIBM'24 paper: Focus on Focus: Focus-oriented Representation Learning and Multi-view Cross-modal Alignment for Glioma Grading

Histopathology Image Classification via Supervised Contrastive Deep Learning

Code for the paper " PDL: Regularizing Multiple Instance Learning with Progressive Dropout Layers "

The code for Kernel attention transformer (KAT)

Code for the Nature Scientific Reports paper "Pathologist-level classification of histologic patterns on resected lung adenocarcinoma slides with deep neural networks." A sliding window framework for classification of high resolution whole-slide images, often microscopy or histopathology images.

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

Deep-learning based classification pipeline for subtyping lung tumors from histology. Study design and codebase to analyze the impact of nucleus segmentation on subtyping.

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

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

🌟 GPU-accelerated stain normalization command line tool

Hard Sample Aware Noise Robust Learning forHistopathology Image Classification

Magnification Prior: A Self-Supervised Method for Learning Representations on Breast Cancer Histopathological Images (WACV 2023)

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

1st to MICCAI DigestPath2019 challenge (https://digestpath2019.grand-challenge.org/Home/) on colonoscopy tissue segmentation and classification task. (MICCAI 2019) https://teacher.bupt.edu.cn/zhuchuang/en/index.htm

Image data augmentation on-the-fly by add new class on transforms in PyTorch and torchvision.

The repository contains a simple pipeline for training Nuclei Segmentation Datasets of Histopathology Images.

KBSMC_colon_tma_cancer_grading_1024_dataset

Official Repository for "Detection of tumour infiltrating lymphocytes in CD3 and CD8 stained histopathological images using a two-phase deep CNN"

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…

PyTorch implementation of Foveation for Segmentation of Ultra-High Resolution Images