Analysis of histopathological images is critical in cancer diagnosis and treatment. Due to the huge size of histopathological images and the varied number of imaging records per patient, many existing works analyze the Whole Slide Image (WSI) as a bag in which its patches are 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, in this article we propose a Multi-Instance Multi-Shape Support Vector Machine (MIMSSVM) to analyze the multiple images (instances) jointly where each instance consists of multiple patches in various shapes. In our approach, we can identify the different morphologic abnormalities of nuclei shapes from the multiple images. In addition to the multi-instance multi-shape learning capability, we derive an efficient solution algorithm to optimize the proposed model that scales well to a large number of features. Our experimental results show our new method outperforms the existing SVMs and 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. All these promising experimental results have demonstrated the effectiveness of our new method. We anticipate that our new method is of interest to biomedical engineering communities beyond WSI research and have open sourced the code of our method online. The implementation of our proposed MIMSSVM model is publicly available at https://github.com/hoonseo0409/MIMSSVM .

Chest X-ray (CXR) analysis plays an important role in patient treatment. As such, a multitude of machine learning models have been applied to CXR datasets attempting automated analysis. However, each patient has a differing number of images per angle, and multi-modal learning should deal with the missing data for specific angles and times. Furthermore, the large dimensionality of multi-modal imaging data with the shapes inconsistent across the dataset introduces the challenges in training. In light of these issues, we propose the Fast Multi-Modal Support Vector Machine (FMMSVM) which incorporates modality-specific factorization to deal with missing CXRs in the specific angle. Our model is able to adjust the fine-grained details in feature extraction and we provide an efficient optimization algorithm scalable to a large number of features. In our experiments, FMMSVM shows clearly improved classification performance.

MOTIVATION: Breast cancer is a type of cancer that develops in breast tissues, and, after skin cancer, it is the most commonly diagnosed cancer in women in the United States. Given that an early diagnosis is imperative to prevent breast cancer progression, many machine learning models have been developed in recent years to automate the histopathological classification of the different types of carcinomas. However, many of them are not scalable to large-scale datasets. RESULTS: In this study, we propose the novel Primal-Dual Multi-Instance Support Vector Machine to determine which tissue segments in an image exhibit an indication of an abnormality. We derive an efficient optimization algorithm for the proposed objective by bypassing the quadratic programming and least-squares problems, which are commonly employed to optimize Support Vector Machine models. The proposed method is computationally efficient, thereby it is scalable to large-scale datasets. We applied our method to the public BreaKHis dataset and achieved promising prediction performance and scalability for histopathological classification. AVAILABILITY AND IMPLEMENTATION: Software is publicly available at: https://1drv.ms/u/s!AiFpD21bgf2wgRLbQq08ixD0SgRD?e=OpqEmY. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Alzheimer’s Disease (AD) is a chronic neurodegenerative disease that causes severe problems in patients’ thinking, memory, and behavior. An early diagnosis is crucial to prevent AD progression; to this end, many algorithmic approaches have recently been proposed to predict cognitive decline. However, these predictive models often fail to integrate heterogeneous genetic and neuroimaging biomarkers and struggle to handle missing data. In this work we propose a novel objective function and an associated optimization algorithm to identify cognitive decline related to AD. Our approach is designed to incorporate dynamic neuroimaging data by way of a participant-specific augmentation combined with multimodal data integration aligned via a regression task. Our approach, in order to incorporate additional side-information, utilizes structured regularization techniques popularized in recent AD literature. Armed with the fixed-length vector representation learned from the multimodal dynamic and static modalities, conventional machine learning methods can be used to predict the clinical outcomes associated with AD. Our experimental results show that the proposed augmentation model improves the prediction performance on cognitive assessment scores for a collection of popular machine learning algorithms. The results of our approach are interpreted to validate existing genetic and neuroimaging biomarkers that have been shown to be predictive of cognitive decline.

Alzheimer’s Disease (AD) is a progressive memory disorder that causes irreversible cognitive declines, therefore early diagnosis is imperative to prevent the progression of AD. To this end, many biomarker analysis models have been presented for early AD detection. However, these models may not realize the full data potential due to their failure to integrate longitudinal (dynamic) phenotypic data with (static) genetic data. Sometimes, they may not fully utilize both labeled and unlabeled samples either. To overcome these limitations, we propose a semi-supervised enrichment learning method to learn a fixed-length vectorial representation for each participant, by which the static data record can be integrated with the dynamic data records. We have applied our new method on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort and achieved 75% accuracy on multiclass AD progression prediction by one year in advance.

Alzheimer's Disease (AD) is a progressive memory disorder that causes irreversible cognitive decline. Recently, many statistical learning methods have been presented to predict cognitive declines by using longitudinal imaging data. However, missing records that broadly exist in the longitudinal neuroimaging data have posed a critical challenge for effectively using these data in machine learning models. To tackle this difficulty, in this paper we propose a novel approach to integrate longitudinal (dynamic) phenotypic data and static genetic data to learn a fixed-length biomarker representation using the enrichment learned from the temporal data in multiple imaging modalities. Armed with this enriched biomarker representation, as a fixed-length vector per participant, conventional machine learning models can be used to predict clinical outcomes associated with AD. We have applied our new method on the Alzheimer's Disease Neruoimaging Initiative (ADNI) cohort and achieved promising experimental results that validate its effectiveness.