Breast Cancer Histopathological Image Classification Based on High-Order Modeling and Multi-Branch Receptive Fields
Source abstract
Abstract
Existing convolutional neural network (CNN) methods primarily depend on first-order feature modeling, which makes it challenging to effectively capture higher-order features in breast cancer histopathological images. Additionally, due to the limitations of the receptive field, CNNs have difficulty capturing long-range dependencies, thereby limiting the integration of global information. To address this, inspired by the strengths of high-order statistical features and extended receptive fields in visual tasks, this study proposes a novel high-order receptive field network (HoRFNet). Specifically, HoRFNet expands the receptive field and improves the model’s contextual awareness of pathological tissue structures by introducing a multi-branch convolutional structure with convolution kernels of varying sizes, along with dilated convolution layers. Additionally, HoRFNet integrates a matrix power normalization strategy in the covariance pooling module to model the global interactions between convolutional features, thereby improving the higher-order representation of complex textures and structural relationships in tissue images. The BreakHis dataset shows that HoRFNet achieves an image level classification accuracy of 99.50% and a patient level classification accuracy of 99.23%, significantly outperforming existing methods and demonstrating its effectiveness.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: AI in cancer detection · Radiomics and Machine Learning in Medical Imaging · Digital Imaging for Blood Diseases
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Computer Science and Engineering, Dalian Minzu University, Dalian 116650, China
Research Center of Multimodal Information Perception and Intelligent Processing, Dalian Minzu University, Dalian 116650, China
Funding
Applied Basic Research Project of Liaoning Province · Major Open Project of Key Laboratory for Advanced Design and Intelligent Computing of the Ministry of Education
References (55)
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