Bag of Bags: Nested Multi Instance Classification for Prostate Cancer Detection

Abstract

Computer-aided detection (CAD) algorithms have been proposed for auto-detection of different types of cancer. CAD algorithms rely on machine learning methods to classify regions of interest in images into cancerous and healthy regions. In cancer screening, the foremost problem to solve is whether a patient has cancer, regardless of the location of cancerous regions in the organ. This allows early detection of the disease leading to a right course of action in terms of treatment to be taken. In machine learning, this problem has been formulated as multi-instance learning (MIL) where bags of instances are classified rather than the individual instances. In this paper, we propose a bag of bags (BoB) nested MIL algorithm where high-level bags (or parent bags), each contains multiple smaller bags of instances. We applied the proposed BoB MIL algorithm to prostate cancer detection problem using magnetic resonance imaging data to first detect which patients have cancer and consequently, to detect which slices in the 3D volume imaging data of the detected patients contain cancerous regions. Experimental results obtained from the imaging data of 30 patients with ground-truth data based on biopsy results show that the proposed algorithm is not only capable of detecting prostate cancer at patient level, it is also able to detect the cancerous regions at slice level of imaging data with high accuracy.