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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.
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Support vector machine guided reproducing kernel particle method for image-based modeling of microstructures
Abstract This work presents an approach for automating the discretization and approximation procedures in constructing digital representations of composites from micro-CT images featuring intricate microstructures. The proposed method is guided by the Support Vector Machine (SVM) classification, offering an effective approach for discretizing microstructural images. An SVM soft margin training process is introduced as a classification of heterogeneous material points, and image segmentation is accomplished by identifying support vectors through a local regularized optimization problem. In addition, an Interface-Modified Reproducing Kernel Particle Method (IM-RKPM) is proposed for appropriate approximations of weak discontinuities across material interfaces. The proposed method modifies the smooth kernel functions with a regularized Heaviside function concerning the material interfaces to alleviate Gibb's oscillations. This IM-RKPM is formulated without introducing duplicated degrees of freedom associated with the interface nodes commonly needed in the conventional treatments of weak discontinuities in the meshfree methods. Moreover, IM-RKPM can be implemented with various domain integration techniques, such as Stabilized Conforming Nodal Integration (SCNI). The extension of the proposed method to 3-dimension is straightforward, and the effectiveness of the proposed method is validated through the image-based modeling of polymer-ceramic composite microstructures.
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- Award ID(s):
- 1826221
- PAR ID:
- 10469710
- Publisher / Repository:
- Springer Science + Business Media
- Date Published:
- Journal Name:
- Computational Mechanics
- Volume:
- 73
- Issue:
- 4
- ISSN:
- 0178-7675
- Format(s):
- Medium: X Size: p. 907-942
- Size(s):
- p. 907-942
- Sponsoring Org:
- National Science Foundation
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