🧬☣️ An up-to-date and curated list of awesome Machine Learning in Biology and Medical Imaging project ideas, papers, datasets and repositories.
- MRNet: A Dataset of Knee MRIs and Competition for Automated Knee MRI Interpretation: https://stanfordmlgroup.github.io/competitions/mrnet/
Alzheimer's Disease
- OASIS Brains - Open Access Series of Imaging Studies (oasis-brains.org)
- 0. Classification of Alzheimer’s disease diagnosis — Deep Learning for Medical Imaging (DL4MI 2022) (inria.fr)
- openfmri.org: http://openfmri.org/data-organization/
- ADNI Database
Brain Tumor:
- Brain Tumor Dataset e brain tumor dataset
- Brain Tumor MRI Dataset Dataset | Papers With Code
- RSNA-MICCAI Brain Tumor Radiogenomic Classification
- BraTS 2013-2021 Papers With Code
- MURA Dataset: Towards Radiologist-Level Abnormality Detection in Musculoskeletal Radiographs: https://stanfordmlgroup.github.io/competitions/mura/
- CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning: https://stanfordmlgroup.github.io/projects/chexnet/
- DeepLesion, a dataset with 32735 lesions in 32120 CT slices from 10594 studies of 4427 unique patients: https://nihcc.app.box.com/v/DeepLesion
Lung Cancer:
- The Cancer Imaging Archive (TCIA): https://imaging.cancer.gov/informatics/cancer_imaging_archive.htm
- A Large-Scale CT and PET/CT Dataset for Lung Cancer Diagnosis (Lung-PET-CT-Dx) - The Cancer Imaging Archive (TCIA) Public Access - Cancer Imaging Archive Wiki: https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=70224216#7022421621c64ff049c44f03bb442ec5eb88bdf2
- Chest CT-Scan images Dataset: https://www.kaggle.com/datasets/mohamedhanyyy/chest-ctscan-images
- Luna16 Lung Cancer Dataset: https://www.kaggle.com/datasets/fanbyprinciple/luna-lung-cancer-dataset?select=candidates_V2
- CT Scan Images for Lung Cancer: https://www.kaggle.com/datasets/dishantrathi20/ct-scan-images-for-lung-cancer
- IQ-OTH/NCCD - Lung Cancer Dataset: https://www.kaggle.com/datasets/adityamahimkar/iqothnccd-lung-cancer-dataset
Breast Cancer:
- Breast Cancer CT (Fully Preprocessed); https://www.kaggle.com/datasets/sabermalek/bcfpp
- Duke Breast Cancer MRI Dataset: https://paperswithcode.com/dataset/duke-breast-cancer-mri
COVID/Pneumonia:
- https://www.cancerimagingarchive.net/access-data/
- Large COVID-19 CT scan slice dataset: https://www.kaggle.com/datasets/maedemaftouni/large-covid19-ct-slice-dataset
- Curated Covid CT: https://github.com/maftouni/Curated_Covid_CT
- COVID-CT Dataset: https://paperswithcode.com/dataset/covid-ct
- COVIDx CT-3A: https://www.kaggle.com/datasets/hgunraj/covidxct?select=3A_images
- COVIDNet-CT: https://github.com/haydengunraj/COVIDNet-CT/blob/master/docs/dataset.md#data-distribution
- COVID-19 Lung CT Scans: https://www.kaggle.com/datasets/luisblanche/covidct?select=CT_COVID
COVID/Pneumonia
- COVID-19-Affected-Lung-CT-Image-Generative-Network/Modeling_COVID_19_Using_GANs_Paper.pdf at master · brendon-ng/COVID-19-Affected-Lung-CT-Image-Generative-Network · GitHub
- https://github.com/Arminkhayati/CovidCT_CNN-ACGAN
- brendon-ng/COVID-19-Affected-Lung-CT-Image-Generative-Network: A Generative Adversarial Network (GAN) trained to generate artificial CT scan images of lungs infected with the COVID-19 virus. (github.com)
- https://github.com/ParisanSH/Training-AC-GAN-in-order-to-generate-more-images-in-COVID-Non-COVID-CT-IMAGES
- COVID-19-Affected-Lung-CT-Image-Generative-Network/Modeling_COVID_19_Using_GANs_Paper.pdf at master · brendon-ng/COVID-19-Affected-Lung-CT-Image-Generative-Network · GitHub
- COVID-19 Chest CT image Augmentation GAN Dataset | Kaggle
- COVID‐19 diagnosis on CT scan images using a generative adversarial network and concatenated feature pyramid network with an attention mechanism - PMC (nih.gov)
- CCS-GAN: COVID-19 CT Scan Generation and Classification with Very Few Positive Training Images - PMC (nih.gov)
- Prostate Cancer Segmentation + Grade Assessment: https://www.kaggle.com/competitions/prostate-cancer-grade-assessment/data
- 3D Teeth Scan Segmentation + Labeling: https://3dteethseg.grand-challenge.org/
- Carotid Vessel Wall Segementation + Atheosclerosos Diagnosis: https://vessel-wall-segmentation-2022.grand-challenge.org/data/
- MELA Challenge (Lung CT Lesions Detection): https://mela.grand-challenge.org/
- Immunofluorescence for Immunohistochemical from Image: https://github.com/nadeemlab/DeepLIIF
- BIDCell: Biologically-informed deep learning for cell segmentation of subcelluar spatial transcriptomics data: https://github.com/SydneyBioX/BIDCell
- Mitochondria-EM-Image-Segmentation: https://github.com/wangyiranamy/Mitochondria-EM-Image-Segmentation
- Retinal Layers Segmenters: https://github.com/Beknaizer/OCT-Retinal-Layer-Segmenter
- Lung and Colon Cancer Histopathological Images: https://www.kaggle.com/datasets/andrewmvd/lung-and-colon-cancer-histopathological-images
Generative Models
- Generative Adversarial Networks in Digital Pathology and Histopathological Image Processing: A Review - PMC (nih.gov): https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8609288/
- Generative Augmentation Methods for Histological Image Analysis in Limited Data Conditions: https://link.springer.com/article/10.1007/s10598-023-09578-1
- Binary classification of Chemical Structure / not chemical structure: https://github.com/jorgeso/biof509-final-project
- Biological Named Entity Recognition in text: https://github.com/hhkkxxx133/Biological-Named-Entity-Recognition-NER-System
- Multiple Myeloma DREAM Challenge: A crowd-sourced challenge to improve identification of high-risk patients: https://www.synapse.org/#!Synapse:syn6187098/files/
- A biologically interpretable integrative deep learning model that integrates PAthological images and GEnomic data
- DeepConv-DTI: Prediction of drug-target interactions via deep learning with convolution on protein sequences: https://github.com/Rye-Catcher-ZCH/HITSZ-2020-Bioinformatics-Course-Project-Two-Team-zchnb