Deep learning-based histopathological segmentation for whole slide images of colorectal cancer in a compressed domain.

ABSTRACT: Automatic pattern recognition using deep learning techniques has become increasingly important. Unfortunately, due to limited system memory, general preprocessing methods for high-resolution images in the spatial domain can lose important data information such as high-frequency information and the region of interest. To overcome these limitations, we propose an image segmentation approach in the compressed domain based on principal component analysis (PCA) and discrete wavelet transform (DWT). After inference for each tile using neural networks, a whole prediction image was reconstructed by wavelet weighted ensemble (WWE) based on inverse discrete wavelet transform (IDWT). The training and validation were performed using 351 colorectal biopsy specimens, which were pathologically confirmed

SUBMITTER: Kim H 

PROVIDER: S-EPMC8602325 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature