Cancer Metastasis Detection Using Local Features and Random Forests

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Presentation transcript:

Cancer Metastasis Detection Using Local Features and Random Forests Kaisa Liimatainen, MSc, Machine learning Mira Valkonen, BSc Feature engineering, machine learning Kimmo Kartasalo, MSc 3D reconstructions Pekka Ruusuvuori, Doc. Bioimage informatics Leena Latonen, PhD Cancer biologist

Feature Extraction Feature Extraction: The features extracted from the whole slide images include several local descriptors related to image morphology, texture, and spatial distribution of nuclei within a 200x200 pixel block in full resolution. Features such as LBP, HOG, SIFT, MSER, GLCM descriptors were extracted.

Random Forest Model Training Data: 200 000 positive and 200 000 negative training samples were randomly selected from tissue area of the training images. 214 features extracted from each sample block. Model: Ensemble of 50 decision trees. Bootstrap aggregation to improve stability and accuracy

Results Results: The confidence values returned by the classifier were further processed using morphological operations and thresholded into binary detections. Advantages: Can be extended with new features Easy to interpret

Thank you!