Towards machine learned quality control: A benchmark for sharpness quantification in digital pathology.

ABSTRACT: Pathology is on the verge of a profound change from an analog and qualitative to a digital and quantitative discipline. This change is mostly driven by the high-throughput scanning of microscope slides in modern pathology departments, reaching tens of thousands of digital slides per month. The resulting vast digital archives form the basis of clinical use in digital pathology and allow large scale machine learning in computational pathology. One of the most crucial bottlenecks of high-throughput scanning is quality control (QC). Currently, digital slides are screened manually to detected out-of-focus regions, to compensate for the limitations of scanner software. We present a solution to this problem by introducing a benchmark dataset for blur detection, an in-depth comparison of state-of-

SUBMITTER: Campanella G 

PROVIDER: S-EPMC9113532 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature