| [122494] |
| Title: Deep transfer learning methods for colon cancer classification in confocal laser microscopy images. |
| Written by: N. Gessert and M. Bengs and L. Wittig and D. Drömann and T. Keck and A. Schlaefer and D. B. Ellebrecht |
| in: <em>International Journal of Computer Assisted Radiology and Surgery</em>. May (2019). |
| Volume: Number: |
| on pages: |
| Chapter: |
| Editor: |
| Publisher: |
| Series: |
| Address: |
| Edition: |
| ISBN: |
| how published: |
| Organization: |
| School: |
| Institution: |
| Type: |
| DOI: 10.1007/s11548-019-02004-1 |
| URL: https://doi.org/10.1007/s11548-019-02004-1 |
| ARXIVID: |
| PMID: |
Note:
Abstract: The gold standard for colorectal cancer metastases detection in the peritoneum is histological evaluation of a removed tissue sample. For feedback during interventions, real-time in vivo imaging with confocal laser microscopy has been proposed for differentiation of benign and malignant tissue by manual expert evaluation. Automatic image classification could improve the surgical workflow further by providing immediate feedback.