Department of Computer Science & Engineering

Availability of slide scanners and electronic medical record systems have led to increased digitization of pathology images. With digitization, Computer-Aided Diagnosis (CAD) tools can be built to reduce pathologist's fatigue and improve diagnosis workflow. As a first step towards building such tools, we developed a cancer detector for prostate needle core biopsy images. The detector is trained using boosting. We use color and texture features. We also built new structural features that express visual cues not captured by color and texture features. These structural features inform the classifier whether cancer has deformed the glands, the main functional unit of prostate. On the test images we scored, we got an Area Under the ROC Curve (AUC) of 0.94.

Pre-2018 CSE ID: CS2012-0987