Archives of Clinical Microbiology

  • ISSN: 1989-8436
  • Journal h-index: 22
  • Journal CiteScore: 7.55
  • Journal Impact Factor: 6.38
  • Average acceptance to publication time (5-7 days)
  • Average article processing time (30-45 days) Less than 5 volumes 30 days
    8 - 9 volumes 40 days
    10 and more volumes 45 days
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Performance of a novel deep learning library in recognition of breast cancer from whole slide images of histopathological sections

4th International Conference on Digital Pathology
August 22-23, 2019 Zurich, Switzerland

Parikshit Sanyal

MH Jalandhar, India

Posters & Accepted Abstracts: Arch Clin Microbiol


Evaluation of histopathologic slides for recognition of foci of cancer is a labour intensive and time consuming process. We evaluated two different machine learning models, both deep convolutional neural networks, built on ResNet 50 architecture, in recognition of foci of breast cancer from histopathological images. The first model was pre-trained with the ImageNet dataset, the second was trained only on training set with an optimized learning rate. Whole mounted slide images (WSI) data set prepared by Cruz Roa, was used for this study. 198, 738 images of benign foci and 78, 786 malignant foci were extracted from the WSI images. We split the data set randomly in training and evaluation set. After completion of training, both models could detect foci of cancer with 88% sensitivity. We conclude that pretrained ResNet models have the potential to become an effective screening tool for histologic diagnosis of breast cancer.

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