zzl2022's starred repositories
Histopathology-Datasets
Ressources of histopathology datasets
KBSMC_colon_cancer_grading_dataset
KBSMC colon cancer grading dataset repository
JCO_Learning-pytorch
Medical Image Analysis (MEDIA) paper: Joint Categorical and Ordinal Learning for Cancer Grading in Pathology Images
Cell_Normalization_Sample_Results
I included 7 sample lung pathology images (Lung Adenocarcinoma, Squamos Cell Carcinoma, and Mesothelioma). Attached are folders containing each respective samples and specific normalization results.
TCIA-CPTAC-lung-histology-download
Unofficial Instructions for downloading TCIA-CPTAC Pathology Images Lung Cohorts: LUAD, LSCC (aka LUSC)
NSCLC_ResNet
Haowen Zhou, Mark Watson, Cory T. Bernadt, Steven (Siyu) Lin, Chieh-yu Lin, Jon. H. Ritter, Alexander Wein, Simon Mahler, Sid Rawal, Ramaswamy Govindan, Changhuei Yang*, and Richard J. Cote*. "AI-guided histopathology predicts brain metastasis in lung cancer patients." Journal of Pathology (In review).
multimodal-lung-cancer-prediction
Model to predict Lung Cancer using multimodal tabular data - MICCAI Hackathon 2022
multimodal-recurrence
Code accompanying the paper "Multimodal fusion of imaging and genomics for lung cancer recurrence prediction" - Vaishnavi Subramanian, Minh N. Do, Tanveer Syeda-Mahmood (ISBI 2020)
HistoBistro
Weakly-supervised learning pipeline for histopathology images. Publications: Biomarker prediction in colorectal cancer (CRC)
ViT_Attention_Map_Visualization
ViT Attention map visualization (using Custom ViT and Pytorch timm module)
visualizing-attention-maps
Visualizing ViT attention maps
Epistroma_LC25000-Classification
Classification of histopathological images using triplet net and k-nearest neighbours classifier. The method was tested on Epistroma and LC25000 datasets.
LungColonNet
Classification of lung and colon cancer from histopathological images (LC25000) using EfficientNetV2.
Lung-Cancer-Detection-Model
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.
Semantic-Segmentation-of-Pathological-Images
病理图像分割，Semantic Segmentation of Pathological Images
High_Precision_Aerial_Image_Segmentation
Road Image Segmentation with UNet and PyTorch
TransMF_AD
Transformer-based Multimodal Fusion for Early Diagnosis of Alzheimer’s Disease Using Structural MRI and PET
TransformerMultimodal
复现论文：TRANSFORMER-BASED MULTIMODAL FUSION FOR EARLY DIAGNOSIS OF ALZHEIMER’S DISEASE USING STRUCTURAL MRI AND PET
LungTumorSegmentation-UNet-PyTorch
Medical Imaging Segmentation