Abstract
Invasive ductal carcinoma (IDC) is the most common type of breast cancer which is the leading cause of cancer-related deaths in middle-aged women. Pathological analysis of biopsy is the gold standard for diagnosis of breast cancer, and early detection, diagnosis, and treatment can significantly increase the survival rate. This paper proposes a method for the automatic detection of IDC based on the fusion of multi-scale residual convolutional neural network (MSRCNN) and SVM. First, the patches from whole slide images (WSI) were preprocessed by data enhancement and normalization and then input into the MSRCNN for features extraction. Second, the extracted features were input to the SVM and are classified into two categories: healthy and diseased patches. Finally, it is restored to the WSI according to the coordinate information of the patches, therefore the IDC and healthy tissue regions were built. Experimental results show that after 5-fold cross-validation, our method obtained an average accuracy of 87.45±0.81%, an average balance accuracy of 85.7±0.95%, and an average F1 score of 79.89±1.11%. Consequently, it has important practical value and scientific research significance.
Highlights
Breast cancer is one of the most common types of cancer among middle-aged women in the world, 70% of which are invasive ductal carcinoma (IDC), and the incidence has been significantly increasing in the past 20 years[1]
The IDC diseased and NOIDC healthy patches were drawn on WSI according to the coordinate information, so the IDC automatic detection task is solved through the two-classification problem
This paper proposes an multi-scale residual convolutional neural network (MSRCNN)-support vector machines (SVM) model for automatic IDC detection
Summary
Breast cancer is one of the most common types of cancer among middle-aged women in the world, 70% of which are invasive ductal carcinoma (IDC), and the incidence has been significantly increasing in the past 20 years[1]. If breast cancer can be diagnosed and treated early, the survival rate of patients can be increased to 80%[3]. Medical image technologies such as Color Doppler Ultrasound, X-ray, and MRI have developed rapidly in recent years, pathological analysis of a tissue biopsy is still the gold standard for final diagnosis[4,5]. In the pathological analysis of clinical breast cancer tissue regions, the tissue sample taken out by puncture or surgery is stained with hematoxylin and eosin(H&E). The results are affected by the subjective experience of pathologists and the difference in tissue samples. There are fewer pathologists in undeveloped countries and small hospitals in developed countries, which affects IDC early diagnosis and treatment, and resulting in higher mortality
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