Anirudh Attaluri
Pronouns: He/Him
Research Mentor(s): Arvind Rao
Co-Presenter:
Research Mentor School/College/Department: Computational Medicine and Bioniformatics / Medicine
Presentation Date: April 20
Presentation Type: Oral5
Session: Session 3 – 1:40pm – 2:30 pm
Room: Breakout room 4
Authors: Anirudh Attaluri, Avery Maddox, Arvind Rao
Presenter: 5
Abstract
With the ever improving ability of computing today, it’s important to consider potential benefits in the healthcare industry, specifically with patient diagnosis. Machine Learning algorithms can potentially be trained to discover the early stages of disease, and be a tremendous help to individuals who do not have access to professionals with the necessary specializations. For example, Bladder Cancer has been seen to be extremely heterogenous, with drastic differences in mutation, making with very useful to identify via Machine Learning technology. This project tests various existing frameworks in order to determine efficient practices when developing such tools. Through the use of publicly available datasets, specifically the GDC Data Portal, the previously mentioned frameworks will be analyzed to gain a better understanding of specific features that lead to specific benefits. We hope to create weight based spatial maps of individuals portions of a WSI (Whole Slide Image), which can then be analyzed to understand prominent mutations and features in existing tumors. In the future, the data gained from this study will ideally be able to paint a clear picture in terms of the role of such tools in the healthcare industry. By optimizing upon tools that already exist, an overall better product will be created. Ultimately, this can prove instrumental to the overall state of the healthcare industry, especially in regions where specialized professionals are significantly harder to gain access too. Additionally, such tools can serve as aids which can go a long way in terms of making the correct diagnosis.
Biomedical Sciences, Engineering
