Accurate diagnosis of colorectal cancer based on histopathology images using artificial intelligence.

ABSTRACT:

Background

Accurate and robust pathological image analysis for colorectal cancer (CRC) diagnosis is time-consuming and knowledge-intensive, but is essential for CRC patients' treatment. The current heavy workload of pathologists in clinics/hospitals may easily lead to unconscious misdiagnosis of CRC based on daily image analyses.

Methods

Based on a state-of-the-art transfer-learned deep convolutional neural network in artificial intelligence (AI), we proposed a novel patch aggregation strategy for clinic CRC diagnosis using weakly labeled pathological whole-slide image (WSI) patches. This approach was trained and validated using an unprecedented and enormously large number of 170,099 patches, > 14,680 WSIs, from > 9631 subjects that covered diverse and representative clinica

SUBMITTER: Wang KS 

PROVIDER: S-EPMC7986569 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature