Texture CNN for Histopathological Image Classification

Abstract

Biopsies are the gold standard for breast cancer diagnosis. This task can be improved by the use of Computer Aided Diagnosis (CAD) systems, reducing the time of diagnosis and reducing the inter and intra-observer variability. The advances in computing have brought this type of system closer to reality. However, datasets of Histopathological Images (HI) from biopsies are quite small and unbalanced what makes difficult to use modern machine learning techniques such as deep learning. In this paper we propose a compact architecture based on texture filters that has fewer parameters than traditional deep models but is able to capture the difference between malignant and benign tissues with relative accuracy. The experimental results on the BreakHis dataset have show that the proposed texture CNN achieves almost 90% of accuracy for classifying benign and malignant tissues.

Machine Learning Methods for Histopathological Image Analysis: A Review

“…Ataky et al [124] proposed a novel approach for augmenting an HI dataset considering the inter-patient variability through image blending using the Gaussian-Laplacian pyramid. Experimental results on the BreakHis dataset with a texture CNN [123] have shown promising gains vis-à-vis the majority of DA techniques presented in the literature. The research carried out by Gecer et al [158] presented a method for breast diagnosis based on WSIs.…”

“…Still, most of the HI datasets have only a few patients and hundreds of images, which can limit the use of DL. Data augmentation [123,124] and transfer learning [125] are two possible approaches to circumvent the lack of data in HI datasets. For instance, ImageNet, which has more than 14 million images, is one of the most common datasets used for training CNNs for object recognition.…”

“…Experimental results on the ICIAR 2018 challenge dataset showed that a 14-layer CNN achieved the best results: patch-wise classification accuracy of 77.4% and 84.7% for four and two classes, respectively; image-wise classification accuracy of 90.0% and 92.5% for four and two classes, respectively. de Matos et al [123] proposed a 7-layer CNN architecture based on texture filters that have fewer parameters than traditional CNNs but can capture the difference between malignant and benign tissues with relative accuracy. The experimental results on the BreakHis dataset showed that the proposed texture CNN achieves 85% of accuracy for classifying benign and malignant tissues.…”

Colour and Texture Descriptors for Visual Recognition: A Historical Overview

“…Second, public datasets for colour textures are by orders of magnitude smaller than available for other task (again, object classification is a striking example). As a result, end-toend fully-trained convolutional networks for colour textures are mostly confined to domainspecific tasks, as for instance classification of histology and microscopy images [129,130], materials recognition [131], defect detection [132] and land-cover classification [133].…”

Texture CNN for Thermoelectric Metal Pipe Image Classification

“…With this in mind, in this paper, our hypothesis is that the use of automatic extraction of deep features may improve the classification accuracy of our previous method for visual inspection of corroded thermoelectric metal pipes [5]. To such an aim, deep features extracted using a Texture Convolutional Neural Network (TCNN) [8], [9] are used to replace wellknown and efficient handcrafted features. The experimental results have confirmed our hypothesis, since the accuracy was improved from 98.71% to 99.20% in the task of identifying different levels of corrosion in metallic pipes.…”