Author:
Zhong Ziliang,Zheng Muhang,Mai Huafeng,Zhao Jianan,Liu Xinyi
Abstract
Abstract
Computer-aided diagnosis establishes methods for robust assessment of medical image-based examination. Image processing introduced a promising strategy to facilitate disease classification and detection while diminishing unnecessary expenses. In this paper, we propose a novel metastatic cancer image classification model based on DenseNet Block, which can effectively identify metastatic cancer in small image patches taken from larger digital pathology scans. We evaluate the proposed approach to the slightly modified version of the PatchCamelyon (PCam) benchmark dataset. The dataset is the slightly modified version of the PatchCamelyon (PCam) benchmark dataset provided by Kaggle competition, which packs the clinically-relevant task of metastasis detection into a straight-forward binary image classification task. The experiments indicated that our model outperformed other classical methods like Resnet34, Vgg19. Moreover, we also conducted data augmentation experiment and study the relationship between Batches processed and loss value during the training and validation process.
Subject
General Physics and Astronomy
Reference15 articles.
1. Rotation Equivariant CNNs for Digital Pathology;Veeling
2. Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer;Bejnordi;JAMA: The Journal of the American Medical Association
3. Densely Connected Convolutional Networks [C];Huang,2017
4. Deep Residual Learning for Image Recognition;He,2016
5. Very Deep Convolutional Networks for Large-Scale Image Recognition [J];Simonyan,2014
Cited by
35 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献