Multiple Instance Learning for Breast Cancer Magnetic Resonance Imaging

Abstract

In this thesis we evaluate the efficacy of multiple instance learning (MIL) as a 'pure' machine learning approach for the diagnosis of breast cancer in magnetic resonance images (MRI). The traditional approach for the diagnosis of breast cancer is based on region-of-interest (ROI) based single instance learning (SIL). In the ROI-based SIL, the classification of benign and malignant lesions depends on the features, which are extracted from segmented ROIs. But, an accurate segmentation of a ROI is a challenging task due to poor signal-to-noise-ratio and faint edges due to partial volume effects. Therefore, variations in the segmentation of ROIs can affect the diagnostic outcome. MIL is a relatively new method in the supervised learning, where each sample is represented as a bag of instances. An image in the context of MIL can be considered as a bag of pixels, tiles, or ROIs, which correspond to instances. Here we apply tile-based MIL, where the diagnosis of breast cancer is based on tile-based features which do not require the segmentation of ROIs. Therefore, tile-based MIL has the potential to provide the classification of breast cancer without segmenting the ROIs. This is the motivation for the main objective of this thesis: to estimate the efficacy of tile-based MIL in the detection and the diagnosis of breast cancer MRI.We initially evaluate the potential of MIL for the detection and the diagnosis of breast cancer in anatomical T2-weighted MRI. In particular, we compare the performance of a MIL-based learner, i.e., citation-kNN (CkNN) against conventional kNN and a Random Forest classifier. We utilise both (generic) tile-based spatial features and (domain specific) ROI-based features. We perform experiments on two datasets consisting of 77 mass-like lesions and 129 both mass-like and non-mass-like lesions. The performance of CkNN as both a diagnostic and screening tool is evaluated using the area under the receiver operating characteristic curve (AUC), estimated over 10-fold cross validation. Results demonstrate that the tile-based CkNN has equivalent performance to the ROI-based classification. However, tile-based MIL has an advantage that it does not require the domain specific ROI-based features typically used in breast MRI. This not only has the potential to make the tile-based classification robust to inaccuracies in the delineation of suspicious lesions, but also makes it suitable for the detection of suspicious lesions prior to segmentation.Next, we investigate the performance of CkNN for the diagnosis of breast cancer using dynamic MRI. Specifically we use generic tile-based spatio-temporal features derived from T2-weighted MRI and T1-weighted dynamic contrast enhanced MRI. We utilise a discrete cosine transform and contrast enhancement models as feature extraction techniques. We compare the III performance of CkNN and kNN against a traditional approach based on bespoke ROI-based features using the 77 mass-like lesions. Empirical results show the equivalent classification performance of both ti...

Not-so-supervised: A survey of semi-supervised, multi-instance, and transfer learning in medical image analysis

“…MIL category Method Brain Tong et al (2014) AD classification global excl bag Chen et al (2015b) cerebral small vessel disease detection global instance Dubost et al (2017) enlarged perivascular space detection local instance Eye Venkatesan et al (2015) diabetic retinopathy classification global excl bag Quellec et al (2012) diabetic retinopathy classification global, local instance Schlegl et al (2015) fluid segmentation local instance Manivannan et al (2016) retinal nerve fiber layer visibility classification global, local instance Lu et al (2017) fluid detection global instance Breast Maken et al (2014) breast cancer detection global multiple Sanchez de la Rosa et al (2015) breast cancer detection global, local excl bag Shin et al (2017) mass localization, classification global, local instance Lung Dundar et al (2007) pulmonary embolism detection false positive instance Bi and Liang (2007) pulmonary embolism detection false positive instance Liang and Bi (2007) pulmonary embolism detection false positive instance Cheplygina et al (2014) COPD classification global multiple Melendez et al (2014) tuberculosis detection global, local instance Stainvas et al (2014) lung cancer lesion classification false positive instance Melendez et al (2016) tuberculosis detection global, local instance Kim and Hwang (2016) tuberculosis detection global, local instance Shen et al (2016) lung cancer malignancy prediction global, local instance Cheplygina et al (2017) COPD classification global instance Li et al (2017b) abnormality detection (14 classes) global, local instance Abdomen Dundar et al (2007) polyp detection false positive instance Wu et al (2009) polyp detection false positive instance Lu et al (2011) polyp detection, size estimation false positive instance Wang et al (2012) polyp detection false positive instance Wang et al (2015a) lesion detection global prim bag Wang et al (2015b) lesion detection global prim bag Histology/Microscopy Dundar et al (2010) brea...…”