create an algorithm to identify metastatic cancer in small image patches taken from larger digital pathology scans.
The data for this competition is a slightly modified version of the PatchCamelyon (PCam) benchmark dataset
The dataset used for the research is a slightly modified version of the PatchCamelyon (PCam). The original PCam dataset contains duplicate images due to its probabilistic sampling, however, this version does not contain duplicates. The dataset is open-source and can be downloaded from (https://www.kaggle.com/c/histopathologic-cancer-detection/data). The dataset has more than 220K RGB images with a dimension of 96x96x3. The given problem is the binary classification problem where the associated label has two class labels i.e. tumor and non-tumor tissues. A positive label indicates that the center 32x32px region of a patch contains at least one pixel of tumor tissue.
- Python
- Pytorch
- PytorchVision
- opencv
- sklearn
- numpy
- pandas
- matplotlib