2022 HPCC Systems Community Summit: Analysis on Medical Images: Colorectal Cancer Diagnosis...

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Analysis on Medical Images: Colorectal Cancer Diagnosis / Implementation of Local Outlier factor Algorithm for Anomaly Detection in HPCC Systems - Sarvesh Prabhu, Lambert High School / Arya Adesh, RVCE

If you are a machine learning finatic, you won't want to miss this session including two very interesting talks from Sarvesh Prabhu and Arya Adesh who interned with HPCC Systems this summer. 

Excerpt from Sarvesh Prabhu:
The advancement of AI & ML in the medical field, particularly cancer diagnosis, has long been held back on the basis of accuracy limitations and a lack of trust from the practitioners. Since the diagnosis changes the course of action taken on a patient, any error, whether a false positive or negative, leads to loss of life or potentially unnecessary treatment; As a result, ML has not played the role of a primary predictor, acting as an optional aid. This talk will cover the scope of my research to get to a consistently accurate diagnosis, possible by highlighting the areas of interest to the Physician (whether it’s a polyp, ulcer, etc.), allowing conclusion several hours faster than a traditional procedure while being non-invasive. The accuracy is realized by comparing the two dominant ML models: Neural Networks and Random Forest. They will conclude that the best training and inference approaches for the pragmatic business use cases.

Excerpt from Arya Adesh:
Anomaly detection is the process of finding unexpected abnormalities in the dataset. Anomalies are rare occurrences that differ from the norm. An anomaly in a real-time dataset may indicate critical incidents like bank frauds, data compromise, infrastructure failure, and other deviations. Hence it is critical to identify such anomalies for further action. Local Outlier Factor(LOF) is an unsupervised anomaly detection method that identifies anomalies without training. It is a density-based anomaly detection algorithm that assigns a degree of outlier-ness (called Local outlier factor) to each point in the dataset. LOF can find both global and Local Outliers. Local anomalies are points that are outlying with respect to their neighbours. Other anomaly detection algorithms accurately find global anomalies, however, they fail to identify local outliers as they assume the dataset to exhibit uniform data distribution. LOF is most suitable for uneven distribution datasets as it doesn’t make assumptions about the distribution. It can identify both global outliers (outlying with respect to all the points in the dataset) and local outliers. The determination of outliers is based on the density between each data point and its neighbour points.

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