Pan-Cancer Integrative Histology-Genomic Analysis via Multimodal Deep Learning - Cancer Cell
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Updated
Feb 7, 2023 - Jupyter Notebook
Pan-Cancer Integrative Histology-Genomic Analysis via Multimodal Deep Learning - Cancer Cell
AI-based pathology predicts origins for cancers of unknown primary - Nature
Context-Aware Survival Prediction using Patch-based Graph Convolutional Networks - MICCAI 2021
Fast and scalable search of whole-slide images via self-supervised deep learning - Nature Biomedical Engineering
Predicting Axillary Lymph Node Metastasis in Early Breast Cancer Using Deep Learning on Primary Tumor Biopsy Slides, BCNB Dataset
MICCAI2020. "Multiple Instance Learning with Center Embeddings for Histopathology Image Classification"
DeeperHistReg - Source Code
The code for Kernel attention transformer (KAT)
[ESWA 2023] Implementation of DSCA: A Dual-stream Network with Cross-attention on Whole-Slide Image Pyramids for Cancer Prognosis
This is a viewer app that provides using to zoom and pan tools Deep Zoom Image (dzi) or Whole Slide Image (wsi) using Electron JS and OpenSeaDragon .
Breast Cancer Image Classification On WSI With Spatial Correlations https://teacher.bupt.edu.cn/zhuchuang/en/index.htm
Integrative Spatial Analysis of H&E and IHC Images Identifies Prognostic Immune Subtypes correlated with Progression Free Survival in HPV-Related Oropharyngeal Squamous Cell Carcinoma
Diagnosis of histologic growth patterns of lung cancer in digital slides using deep learning.
A plugin for TensorBoard to show WSI images. The plugin is based on Deep Zoom Image (DZI).
Given an image of cells from a WSI, identify the mitotic figures and return a mitotic index.
Python 3 Package for optimally sampling big images with texture-aware patchification based on SLIC superpixels. So Sleek !
Automated batch processing for tissue microscopy image data
Algorithm for the Whole Slide Images registration.
PathoPatcher is a Python project designed for accelerating Whole Slide Image Preprocessing, employing AI-based preprocessing techniques with features like annotation handling, color normalization, and configurable parameters
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