Automated Cancer Diagnostics via Analysis of Optical and Chemical Images by Deep and Shallow Learning.

ABSTRACT: Optical microscopy has long been the gold standard to analyse tissue samples for the diagnostics of various diseases, such as cancer. The current diagnostic workflow is time-consuming and labour-intensive, and manual annotation by a qualified pathologist is needed. With the ever-increasing number of tissue blocks and the complexity of molecular diagnostics, new approaches have been developed as complimentary or alternative solutions for the current workflow, such as digital pathology and mass spectrometry imaging (MSI). This study compares the performance of a digital pathology workflow using deep learning for tissue recognition and an MSI approach utilising shallow learning to annotate formalin-fixed and paraffin-embedded (FFPE) breast cancer tissue microarrays (TMAs). Results show that b

SUBMITTER: Isberg OG 

PROVIDER: S-EPMC9143055 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature