Biomedical Image Classification using Gradient Boosting Algorithms

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Meeting Agenda:

6:00 – 6:40 pm: Pizza, water and networking.
6:40 – 6:45 pm: Welcome message by Ernest Bonat, Ph.D.
6:45 – 8:15 pm: Presentation and open discussions. “Biomedical Image Classification using Gradient Boosting Algorithms (XGBoost, LightGBM and CatBoost)” by Ernest Bonat, Ph.D., Senior Software Engineer and Senior Data Scientist.

Abstract:

Biomedical image classification using Machine Learning algorithms is a very hot topic in the Medical Industry right now. There are specific requirements that make this job challenging. One example is the algorithm selection and tuning hyperparameters, image semantic segmentation, image features extraction, image class imbalanced, etc. Melanoma is a cancer that develops in melanocytes, the pigment cells present in our skin. It can be more serious than other forms of skin cancer because of it's tendency to spread to other parts of the body (metastasize) and cause serious illness and death. The incidences of death from Melanoma has risen worldwide in past two decades. In this meetup Ernest will present best practices of using Machine Learning Gradient Boosting algorithms for Melanoma image binary classification. We’ll be covering XGBoost, LightGBM and CatBoost algorithms. Let’s find out why Gradient Boosting algorithms is one of the best options for this type of biomedical image classification.

8.15 pm – 9.00 pm: Coding and learning session. Bring your Python development laptop!