Machine Learning for Image Processing in Healthcare

摘要

Many aspects of healthcare are undergoing rapid evolution and facing many challenges. Computer vision and image processing methods have progressed tremendously within the last few years. One of the reasons is the excellent performance that machine learning algorithms are achieving in many the fields of image processing, especially through deep learning techniques. There exists various application areas where computer-based image classification and object detection methods can make meaningful contributions. Yet, these data-intensive methods encounter a unique set of challenges in the medical domain - which often suffer from a scarcity of large public datasets and still require reliable analysis with high precision. This talk will present some recent work in the area of image analytics for cervical cancer screening in the context of low resource settings. The work is in collaboration with Dr. Pamela Tan from Singapore's KK Hospital and MobileODT, a medical device and software-enabled services company. In this joint project, our group's work focuses on machine learning algorithms for the medical analysis of cervix images acquired via unconventional consumer imaging devices like smart-phones, based on their appearance and for the purpose of screening cervical cancer precursor lesions. The talk will present our methodology and some preliminary results.