Pink AI

According to the National Center for Biotechnology Information (NCBI), approximately one in eight women worldwide carry a lifelong risk of contracting breast cancer. Breast Cancer is the most common form of cancer and is the 2nd leading cause of death among women. Breast cancer detection is often a laborious process, requiring either the use of mammograms which requires expensive Radiology instruments, trained radiologists, and a clinic with supporting infrastructure, or the use of Fine Needle Aspirations (FNA’s) to perform a biopsy of breast tissue at regular intervals typically after the age of 30 in females. In the case of performing a biopsy, a trained pathologist then needs to evaluate multiple samples of breast tissue in order to detect instances of breast cancer by using a microscope. This is difficult as even the most skilled pathologists can only spot instances of breast cancer approximately 85% percent of the time requiring them to evaluate many samples. According to a study published in the paper Diagnostic Concordance Among Pathologists Interpreting Breast Biopsy Specimens by Elmore et. al., the accuracy of pathologists analyzing breast biopsies for four different types of breast cancer varies anywhere from 48% for atypical hyperplasia to 96% for invasive carcinoma. The entire process takes an average of five days and up to three weeks depending on the size of the backlog of samples a particular clinic has. The involvement of pathologist analysis of makes current biopsies expensive; each one around five hundred dollars. It also makes them slow; After sample collection, a clinic would have to transport the sample and wait for the pathologist to analyze it before finally receiving the pathology report. Finally, it makes them inaccurate, as humans have to simultaneously consider dozens of factors; skilled pathologists have been shown to have 12% false negative and 16% false positive rates. In the US alone there are approximately 1.6 million breast biopsies performed every year. As the number of occurrences of breast cancer continue to rise, the number of screenings worldwide will continue to grow, requiring either additional resources or tools to address. In many developing countries, the aforementioned problems are only further exacerbated. The cost and lack of skilled labor makes biopsy based breast cancer detection very difficult or impossible in many cases, resulting in women not getting regular preventative screening for breast cancer. In cases where breast cancer screening is available, This creates a situation where breast cancer can metastasize, or develop beyond the point of treatment. In fact, according to NCBI, approximately 60% of deaths due to breast cancer occur in the developing world. Given the problems associated with the requirement of a pathologist for breast tissue analysis, our team proposes to create a lightweight deep learning model hosted on a website where clinics can directly upload microscope images, eliminating the need for a pathologist altogether. Our platform, Pink AI, would enable a dramatic decrease in cost for breast cancer detection and provide a diagnosis in real time instead of taking days. Pink AI is designed to run on any device with a web browser including smart phones and laptops, making it ideal for clinics in developing countries without advanced infrastructure to use our platform for breast cancer prediction. Pink AI has a groundbreaking accuracy of 99%, making it more accurate at detecting four distinct types of breast cancer than even the most skilled of pathologists.

We have a team of two, Pranshu Chaturvedi and Abhi Upadhyay. We met in middle school while doing STEM related projects and competitions including science fair projects and competitions. We continued to work together on ideas related to computer science throughout high school, including participating in the USA Computing Olympiad and starting a computer club at our high school. Pranshu and Abhi are both freshmen in college now with interests in exploring novel applications for Artificial Intelligence technologies and using software engineering to turn our ideas into a reality, which led us to participate in this competition. Pranshu is a current freshman at the University of Illinois Urbana-Champaign studying Computer Science and Statistics. His interests lie in High Performance Computing (HPC) and in AI/Deep Learning. Recently he attended the Super Computing 19 conference where he represented his school as a member of the University of Illinois Student Cluster Competition team; the team won 3rd place in the nation. The competition requires students to assemble the fastest possible HPC cluster within a given power limit and run a series of simulations and benchmarks during the 3 day competition window. Pranshu recently also won 2nd place at the National Center for Supercomputing Applications Deep Learning Hackathon. At the Technology Student Association’s national conference in June 2019, his team won 1st place out of over 75 teams in a research presentation competition on exploring a novel application of artificial intelligence in a domain field. Abhi is a current freshman at the University of California Berkeley, pursuing a dual-degree in Electrical Engineering & Computer Science + Business Administration as part of the M.E.T. program (Management, Entrepreneurship, & Technology). He has worked in areas including full-stack development, embedded systems, computer vision, and cryptocurrency. His full-stack development work includes projects for Saks Fifth Avenue affiliates, an app for his high school amassing over 1.2K active users, and more. He is well-versed in Node.js, React, PHP, and Ruby on Rails. Abhi has also conducted research and developed a prototype of an adapter that could alleviate 61% of household standby power use. Additionally, he researched methods to compress standard video by converting objects to vectors, using computer vision. Currently, he is interning at a startup, working towards mass market adoption of cryptocurrency.