Pathologists find tedious to examine the status of the sentinel lymph node on a large number of pathological scans. The examination process of such lymph node which encompasses metastasized cancer cells is histopathologically organized. However, the task of finding metastatic tissues is gradual which is often challenging. In this work, we present our deep convolutional neural network based model validated on PatchCamelyon (PCam) benchmark dataset for fundamental machine learning research in histopathology diagnosis. We find that our proposed model trained with a semi-supervised learning approach by using pseudo labels on PCam-level significantly leads to better performances to strong CNN baseline on the AUC metric.
@cite_29 conducted a study utilizing results from deep learning algorithms for the detection of breast cancer metastasis in lymph nodes. The study involved reviewing 70 slides by six pathologists in two modes assisted, and unassisted wherein the deep learning mode was used to outline interesting regions in assisted mode. The study found that algorithm-assisted pathologists demonstrated higher accuracy than either the algorithm or the pathologist alone. @cite_7 proposed multiple magnification feature embedding (MMFE) as image tile prediction encoder and slice feature extractor. The method considered inputs image tiles in three resolution 256, 1024, and 4096 and scales to 256. The authors reported 78.1
@cite_29 @cite_7
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Advances in the quality of whole-slide images have set the stage for the clinical use of digital images in anatomic pathology. Along with advances in computer image analysis, this raises the possibility for computer-assisted diagnostics in pathology to improve histopathologic interpretation and clin In recent years, the use of convolutional neural networks has made great success in the analysis of digital pathological images. However, due to the slower running speed of the model and the large amount of data in the single image, the model based on full sampling runs very slowly. It is of great significance to optimize the speed of the model. This paper proposes a method to complete the breast cancer detection by incomplete sampling of the features of the transfer learning output without network training. This method verified on Camelyon16 dataset. The experimental results show that while ensuring the accuracy of the model, it can greatly reduce the time for model construction and use.