Breast Histopathology Image Segmentation
Using Tensorflow/Keras for Image Classifications
- Part 1: Data Inspection and Pre-processing
- Part 2: Weights, Data Augmentations and Generators
- Part 3: Model creation based on a pre-trained and a custom model
- Part 4: Train our model to fit the dataset
- Part 5: Evaluate the performance of your trained model
- Part 6: Running Predictions
Based on Breast Histopathology Images by Paul Mooney.
Invasive Ductal Carcinoma (IDC) is the most common subtype of all breast cancers. To assign an aggressiveness grade to a whole mount sample, pathologists typically focus on the regions which contain the IDC. As a result, one of the common pre-processing steps for automatic aggressiveness grading is to delineate the exact regions of IDC inside of a whole mount slide.Can recurring breast cancer be spotted with AI tech? - BBC News
- Citation: Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases
- Dataset: 198,738 IDC(negative) image patches; 78,786 IDC(positive) image patches