About cookies on this site Our websites require some cookies to function properly (required). In addition, other cookies may be used with your consent to analyze site usage, improve the user experience and for advertising. For more information, please review your options. By visiting our website, you agree to our processing of information as described in IBM’sprivacy statement. To provide a smooth navigation, your cookie preferences will be shared across the IBM web domains listed here.
Publication
ICML 2020
Workshop poster
Machine Learning to Assess the Association of H. Pylori Infection and Gastric and Oesophageal Cancer by Detecting Western Blot Protein Bands
Abstract
The links between H. pylori infection and gastric and oesophageal cancer are either inconsistent or still poorly understood. In this paper, we present a machine learning approach that detects immuno- reactive protein bands from HelicoBlot assays of human serum samples related to these diseases. Feature encoding is applied via transfer learning by utilising existing computer vision networks on scanned HelicoBlo strips. XGBoost classifier is employed to predict the existence of relevant protein bands, and a 95% average detection AU- ROC is achieved. The proposed approach helps to reduce the amount of time required for man- ual annotation and the subjective bias associated with it. Moreover, the approach can be applied to large-scale epidemiological studies investigating the relationship between infectious disease and risk of cancer.