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MULTI-MODAL DATA INTEGRATION AND ANALYSIS FOR CANCER PROGNOSIS USING MACHINE LEARNING MODEL

MULTI-MODAL DATA INTEGRATION AND ANALYSIS FOR CANCER PROGNOSIS USING MACHINE LEARNING MODEL

Speaker: Sripana Saha – Bihta Patna District, India
Topic(s): Artificial Intelligence, Machine Learning, Computer Vision, Natural language processing

Abstract

Breast cancer is a concerning disease due to its high incidence and mortality. In recent decades, the incidence and mortality have continuously increased. Its early-stage detection and the correct prognosis is the only effective way to eradicate fatalities caused. The prognosis and diagnosis of cancer primarily rely on clinical, genomic, and histopathological tissue image modalities. To have a better understanding of the situation and proceed with the correct treatment, it is required to explore all the possible modalities. The complex and heterogeneous nature of these modalities along with the variations of clinical outcomes in this disease poses a serious challenge in the prognosis. So, the situation demands some artificial-intelligence automated system for more accurate and reliable prognosis prediction of cancer patients.

In recent years, we have exploited gene expression, DNA methylation, copy number variation/alteration, and microRNA expression with regard to genomics, clinical profiles for previous conditions, history, lifestyle, and severity of the disease, and histopathological tissue image for cell-level visualization of cancer patients. We further utilized this information to design multi-modal machine-learning architectures for the prognostication of breast cancer. Our extensive experiments and comparative analysis support the superiority of proposed architectures over many other state-of-the-art methods. My talk will discuss the architectures and algorithms that we have developed for multimodal breast cancer prognosis prediction.

The corresponding publications are listed below:

1. Nikhilanand Arya and Sriparna Saha. Deviation-Support based Fuzzy Ensemble of Multimodal Deep Learning classifiers for Breast Cancer Prognosis Prediction. Scientific Reports. Nature Publishing Group, 2023.(Impact Factor:4.99, h5-index: 206 ).

2. Nikhilanand Arya, Sriparna Saha, Archana Mathur and Snehanshu Saha. Improving the Robustness and Stability of a Machine Learning Model for Breast Cancer Prognosis through the use of Multi-Modal Classifiers. Scientific Reports, volume 13, pages 4079. Nature Publishing Group, 2023.(Impact Factor: 4.99, h5-index: 206 ).

3. Nikhilanand Arya, Sriparna Saha, Archana Mathur, and Snehanshu Saha. Proposal of SVM Utility Kernel for Breast Cancer Survival Estimation. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2022.(Impact Factor: 3.702).

4. Nikhilanand Arya and Sriparna Saha. Multi-modal advanced deep learning architectures for breast cancer survival prediction. Knowledge-Based Systems, volume 221, pages 106965. Elsevier, 2021.(Impact Factor: 8.139).

5. Nikhilanand Arya and Sriparna Saha. Generative incomplete multi-view prognosis predictor for breast cancer: GIMPP. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2021.(Impact Factor: 3.702).

6. Nikhilanand Arya and Sriparna Saha. Multi-Modal Classification for Human Breast Cancer Prognosis Prediction: Proposal of Deep-Learning Based Stacked Ensemble Model. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2020.(Impact Factor:3.702).

About this Lecture

Number of Slides: 60 - 70
Duration: 90 minutes
Languages Available: English
Last Updated:

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