Automatic detection of invasive ductal carcinoma in whole slide images with convolutional neural networks

Abstract

This paper presents a deep learning approach for automatic detection and visual analysis of invasive ductal carcinoma (IDC) tissue regions in whole slide images (WSI) of breast cancer (BCa). Deep learning approaches are learn-from-data methods involving computational modeling of the learning process. This approach is similar to how human brain works using different interpretation levels or layers of most representative and useful features resulting into a hierarchical learned representation. These methods have been shown to outpace traditional approaches of most challenging problems in several areas such as speech recognition and object detection. Invasive breast cancer detection is a time consuming and challenging task primarily because it involves a pathologist scanning large swathes of benign regions to ultimately identify the areas of malignancy. Precise delineation of IDC in WSI is crucial to the subsequent estimation of grading tumor aggressiveness and predicting patient outcome. DL approaches are particularly adept at handling these types of problems, especially if a large number of samples are available for training, which would also ensure the generalizability of the learned features and classifier. The DL framework in this paper extends a number of convolutional neural networks (CNN) for visual semantic analysis of tumor regions for diagnosis support. The CNN is trained over a large amount of image patches (tissue regions) from WSI to learn a hierarchical part-based representation. The method was evaluated over a WSI dataset from 162 patients diagnosed with IDC. 113 slides were selected for training and 49 slides were held out for independent testing. Ground truth for quantitative evaluation was provided via expert delineation of the region of cancer by an expert pathologist on the digitized slides. The experimental evaluation was designed to measure classifier accuracy in detecting IDC tissue regions in WSI. Our method yielded the best quantitative results for automatic detection of IDC regions in WSI in terms of F-measure and balanced accuracy (71.80%, 84.23%), in comparison with an approach using handcrafted image features (color, texture and edges, nuclear textural and architecture), and a machine learning classifier for invasive tumor classification using a Random Forest. The best performing handcrafted features were fuzzy color histogram (67.53%, 78.74%) and RGB histogram (66.64%, 77.24%). Our results also suggest that at least some of the tissue classification mistakes (false positives and false negatives) were less due to any fundamental problems associated with the approach, than the inherent limitations in obtaining a very highly granular annotation of the diseased area of interest by an expert pathologist.

Automated tubule nuclei quantification and correlation with oncotype DX risk categories in ER+ breast cancer whole slide images

“…In supervised classification settings, a DNN uses the backpropagation algorithm to update its internal weights according to the label of input exemplars 12 . Some applications of the DNNs in histological image analysis include the mitosis identification task 13 and the localization of regions of interest in histological images 14 . With the recent emergence of whole slide tissue scanning and digital pathology [15][16][17] there has been substantial interest in developing automated computerized histologic predictors of tumor grade and outcome for several diseases including oropharyngeal squamous cell carcinoma 18 , prostate cancer 19,20 and glioblastoma 21 .…”

Invasive ductal breast carcinoma detector that is robust to image magnification in whole digital slides

“…2, that is, automatically finding areas expressing IDBC within whole digital slides. Our approach most resembles a design recently proposed by Cruz-Roa et al 16 while additionally addressing two of the common weaknesses described in Sec. 1.2.…”

“…To further lower computational time, methods either sample a subset of the rectangular areas 12 or reduce the magnification level during analysis. 15,16 The former may be undesirable for clinical applications since the omitted areas may contain information that would ultimately affect tumor classification. Similarly, the latter technique may lead to a misinterpretation of the data due to the reduced magnification.…”

Microscopic medical image classification framework via deep learning and shearlet transform

“…13 Therefore, despite hand-crafted features, learned features do not need any preprocessing and can easily be transferred to different applications since they are data-driven. 15 These methods often outperform traditional approaches that use hand-crafted features. [16][17][18][19] Cruz-Roa et al 15 proposed a three-layer convolutional neural network (CNN) method for invasive ductal carcinoma detection in histopathology images of breast cancer and compared their method with hand-crafted features.…”

“…15 These methods often outperform traditional approaches that use hand-crafted features. [16][17][18][19] Cruz-Roa et al 15 proposed a three-layer convolutional neural network (CNN) method for invasive ductal carcinoma detection in histopathology images of breast cancer and compared their method with hand-crafted features. They reported 6% improvement in the classification accuracy when using their CNN.…”