Qualitative Histopathological Classification of Primary Bone Tumors Using Deep Learning: A Pilot Study.

ABSTRACT:

Background

Histopathological diagnosis of bone tumors is challenging for pathologists. We aim to classify bone tumors histopathologically in terms of aggressiveness using deep learning (DL) and compare performance with pathologists.

Methods

A total of 427 pathological slides of bone tumors were produced and scanned as whole slide imaging (WSI). Tumor area of WSI was annotated by pathologists and cropped into 716,838 image patches of 256 × 256 pixels for training. After six DL models were trained and validated in patch level, performance was evaluated on testing dataset for binary classification (benign vs. non-benign) and ternary classification (benign vs. intermediate vs. malignant) in patch-level and slide-level prediction. The performance of four path

SUBMITTER: Tao Y 

PROVIDER: S-EPMC8526973 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature