fuscc-deep-path / sc_MTOP
sc-MTOP is an analysis framework based on deep learning and computational pathology. This framework aims to characterize the tumor ecosystem diversity at the single-cell level. This code provide 1) Hover-Net-based nuclear segmentation and classification; 2) Nuclear morphological and texture feature extraction; 3) Multi-level pairwise nuclear gra…
☆29Updated 3 months ago
Alternatives and similar repositories for sc_MTOP:
Users that are interested in sc_MTOP are comparing it to the libraries listed below
- Multimodal deep learning to predict distant recurrence-free probability from digitized H&E tumour slide and tumour stage.☆34Updated 2 months ago
- Exploring prognostic indicators in pathological images of hepatocellular carcinoma based on deep learning☆41Updated 4 years ago
- CEll spatial Organization-based graph convolutional network☆24Updated last year
- Code for the im4MEC model described in the paper 'Interpretable deep learning model to predict the molecular classification of endometria…☆38Updated last year
- A pan-cancer platform for mutation prediction from routine histology☆68Updated 3 years ago
- Code from Foersch et al. (Under Construction / Development)☆36Updated last year
- Scripts for data processing☆40Updated 8 months ago
- Weakly-supervised learning pipeline for histopathology images. Publications: Biomarker prediction in colorectal cancer (CRC)☆68Updated last year
- GrandQC tool for tissue detection and quality control in pathology☆25Updated last month
- PyTorch implementation of HisToGene☆33Updated 3 years ago
- Artificial Intelligence predicts immune and inflammatory gene signatures directly from hepatocellular carcinoma histology images☆20Updated 7 months ago
- kfb转svs小软件☆15Updated 2 years ago
- Tools to build deep learning pipelines.☆92Updated 3 months ago
- ☆49Updated last year
- ☆26Updated 2 years ago
- HCC_Deep_learning