Scalable Multi-Instance Multi-Shape Support Vector Machine for Whole Slide Breast Histopathology

Histopathological image analysis is critical in cancer diagnosis and treatment. Due to the huge size of histopathological images, most existing works analyze the whole slide pathological image (WSI) as a bag and its patches are considered as instances. However, these approaches are limited to analyzing the patches in a fixed shape, while the malignant lesions can form varied shapes. To address this challenge, we propose the Multi-Instance Multi-Shape Support Vector Machine (MIMSSVM) to analyze the multiple images (instances) jointly where each instance consists of multiple patches in varied shapes. In our approach, we can identify the varied morphologic abnormalities of nuclei shapes from the multiple images. In addition to the multi-instance multi-shape learning capability, we provide an efficient algorithm to optimize the proposed model which scales well to a large number of features. Our experimental results show the proposed MIMSSVM method outperforms the existing SVM and recent deep learning models in histopathological classification. The proposed model also identifies the tissue segments in an image exhibiting an indication of an abnormality which provides utility in the early detection of malignant tumors.