2019
DOI: 10.1101/820563
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A pilot exploratory study of the potentials of deep learning methods in cancer image segmentation and classification

Abstract: Background: Tumor classification and feature quantification from H&E histology images are critical tasks for cancer diagnosis, cancer research, and treatment. However, both tasks involve tedious and time-consuming manual examination of histology images. We explored the possibilities of using deep learning methods to perform segmentation and classification of histology images of cancer tissue for their potential in computeraided tumor diagnosis and other clinical and research applications. Specifically, we test… Show more

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“…Recently, deep learning-based semantic segmentation is providing state-of-the-art performance in multiple applications, including medical imaging applications [8], [12], [13]. DeepLabV3+ has been a favorite segmentation model in the field of medical imaging [14]. DeepLabV3+ has beendeveloped by Google's DeepLab as discussed in L.C.…”
Section: Introductionmentioning
confidence: 99%
“…Recently, deep learning-based semantic segmentation is providing state-of-the-art performance in multiple applications, including medical imaging applications [8], [12], [13]. DeepLabV3+ has been a favorite segmentation model in the field of medical imaging [14]. DeepLabV3+ has beendeveloped by Google's DeepLab as discussed in L.C.…”
Section: Introductionmentioning
confidence: 99%