Example data
Classification​
- Dog breed images: Stanford Dog Dataset[1][2] for classifying (~20k) images into one of 120 breeds using MobileNet [Download CSV]
- Bone Marrow cell types: Bone Marrow Cell Classification[3][4] ~170k cells from the bone marrow smears of 945 patients using AlexNet
- Eye fundus images: RFMiD[5] and AGAR300[6][7][8] for the classification of Diabetic Retinopathy [Download CSV]
Object detection​
- Wheat ear detection: Global Wheat Detection Challenge[9] using PyTorch (Ultralytics) YOLOv5 [Download CSV]
Self-supervised learning​
- Breast histpathology images: Kaggle Challenge[10][11] using VISSL [Download CSV]
Dataset References​
[1] Aditya Khosla, Nityananda Jayadevaprakash, Bangpeng Yao and Li Fei-Fei. Novel dataset for Fine-Grained Image Categorization. First Workshop on Fine-Grained Visual Categorization (FGVC), IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011.
[2] J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li and L. Fei-Fei, ImageNet: A Large-Scale Hierarchical Image Database. IEEE Computer Vision and Pattern Recognition (CVPR), 2009.
[3] Matek, C., Krappe, S., MĂĽnzenmayer, C., Haferlach, T., & Marr, C. (2021). An Expert-Annotated Dataset of Bone Marrow Cytology in Hematologic Malignancies [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/TCIA.AXH3-T579
[4] Matek, C., Krappe, S., MĂĽnzenmayer, C., Haferlach, T., and Marr, C. (2021). Highly accurate differentiation of bone marrow cell morphologies using deep neural networks on a large image dataset. https://doi.org/10.1182/blood.2020010568
[5] Samiksha Pachade, Prasanna Porwal, Dhanshree Thulkar, Manesh Kokare, Girish Deshmukh, Vivek Sahasrabuddhe, Luca Giancardo, Gwenolé Quellec, Fabrice Mériaudeau, November 25, 2020, "Retinal Fundus Multi-disease Image Dataset (RFMiD)", IEEE Dataport, doi: https://dx.doi.org/10.21227/s3g7-st65.
[6] Derwin, D.J., Selvi, S.T. & Singh, O.J. Secondary Observer System for Detection of Microaneurysms in Fundus Images Using Texture Descriptors. J Digit Imaging 33, 159–167 (2020). https://doi.org/10.1007/s10278-019-00225-z
[7] Derwin, D.J., Selvi, S.T. & Singh, O.J. Discrimination of microaneurysm in color retinal images using texture descriptors. SIViP 14, 369–376 (2020). https://doi.org/10.1007/s11760-019-01566-6
[8] Jeba Derwin,D.,Tamil Selvi,S.,Jeba Singh,O.,Priestly Shan,B.,” A novel automated system of discriminating Microaneurysms in fundus images”, Biomedical Signal Processing and Control, Elsevier, Vol.58, 2020
[9] David E, Madec S, Sadeghi-Tehran P, Aasen H, Zheng B, Liu S, Kirchgessner N, Ishikawa G, Nagasawa K, Badhon MA, Pozniak C. Global Wheat Head Detection (GWHD) dataset: a large and diverse dataset of high-resolution RGB-labelled images to develop and benchmark wheat head detection methods. Plant Phenomics. 2020 Aug 20;2020.
[10] Angel Cruz-Roa; Ajay Basavanhally; Fabio González; Hannah Gilmore; Michael Feldman; Shridar Ganesan; Natalie Shih; John Tomaszewski; Anant Madabhushi. Automatic detection of invasive ductal carcinoma in whole slide images with convolutional neural networks. Proc. SPIE 9041, Medical Imaging 2014: Digital Pathology, 904103 (20 March 2014);
[11] Janowczyk A, Madabhushi A. Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases. J Pathol Inform. 2016 Jul 26;7:29. doi: 10.4103/2153-3539.186902. PMID: 27563488; PMCID: PMC4977982.