Divide-and-Attention Network for HE-Stained Pathological Image Classification

Abstract

Since pathological images have some distinct characteristics that are different from natural images, the direct application of a general convolutional neural network cannot achieve good classification performance, especially for fine-grained classification problems (such as pathological image grading). Inspired by the clinical experience that decomposing a pathological image into different components is beneficial for diagnosis, in this paper, we propose a Divide-and-Attention Network (DANet) for Hematoxylin-and-Eosin (HE)-stained pathological image classification. The DANet utilizes a deep-learning method to decompose a pathological image into nuclei and non-nuclei parts. With such decomposed pathological images, the DANet first performs feature learning independently in each branch, and then focuses on the most important feature representation through the branch selection attention module. In this way, the DANet can learn representative features with respect to different tissue structures and adaptively focus on the most important ones, thereby improving classification performance. In addition, we introduce deep canonical correlation analysis (DCCA) constraints in the feature fusion process of different branches. The DCCA constraints play the role of branch fusion attention, so as to maximize the correlation of different branches and ensure that the fused branches emphasize specific tissue structures. The experimental results of three datasets demonstrate the superiority of the DANet, with an average classification accuracy of 92.5% on breast cancer classification, 95.33% on colorectal cancer grading, and 91.6% on breast cancer grading tasks.

Tissue classification and diagnosis of colorectal cancer histopathology images using deep learning algorithms. Is the time ripe for clinical practice implementation?

“…To tackle the CRC grading task (normal, low, and high grade of dysplasia, according to the WHO histopathological classification) Awan et al ., Shaban et al ., and Yan et al . [ 17 – 19 ] developed custom CNN models with reported accuracy of 91%, 95.7%, and 95.3%. The latter proposed CNN was also based on majority-voting (MV) technique, and it could also be used in different tissues like breast cancer WSI samples, proving its superiority.…”

“…Due to its recent rise in popularity within classification tasks, we separately discuss the performance of attention learning within tissue classification in CRC. Within the included studies, there were 2 reports of attention modelling techniques employed in pathological image analysis [ 20 , 33 ]. Dabass et al .…”

“…Another example of attention modelling in pathological images of CRC is the one developed by Yan et al . [ 33 ]. The ultimate goal of their classification model was to classify cellular nucleic structures from histological slides as belonging to malignant tissue or not, following image decomposition into nucleic and non-nucleic structures.…”

“…A subsection of models used for tissue classification employed attention learning algorithms. Although these instances were scarce in the current literature on CRC pathological classification [ 20 , 33 ], they provide some insights that could enhance the model outcomes already predicted by other CNNs. There is, however, some outstanding criticism regarding attention modelling.…”