ArticleScientific reports2021
Deep learning-based histopathological segmentation for whole slide images of colorectal cancer in a compressed domain.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Integration of Histologic Pattern Classifiers and Graph Convolutional Networks for Prognostic Prediction in Lung Adenocarcinoma.JTO clinical and research reports · 2026Article
- A Fixed-Ratio Hybrid ARO-ALO Algorithm for Multi-Level Thresholding of Histopathological Colon Cancer Images.Cancers · 2026Article
- Two-Step Ensemble Convolutional Neural Networks for Colonoscopic Biopsy Classification Resembling Pathologists' Process.Journal of Korean medical science · 2026Article
- Mixture-of-Skip-Connection Deep Learning Model to Classify Stroke Severity from Diffusion Weighted Imaging Based on NIHSS.Journal of imaging informatics in medicine · 2026Article
- ClinSegNet: Towards Reliable and Enhanced Histopathology Screening.Bioengineering (Basel, Switzerland) · 2025Article
- Added prognostic value of histogram features from preoperative multi-modal diffusion MRI in predicting Ki-67 proliferation for adult-type diffuse gliomas.Quantitative imaging in medicine and surgery · 2025Article
- Development and validation of a deep learning model for morphological assessment of myeloproliferative neoplasms using clinical data and digital pathology.British journal of haematology · 2025Article
- Deep learning model to diagnose cardiac amyloidosis from haematoxylin/eosin-stained myocardial tissue.European heart journal. Imaging methods and practice · 2025Article
- Computational pathology: A survey review and the way forward.Journal of pathology informatics · 2024Review
- Artificial-Intelligence-Assisted Detection of Metastatic Colorectal Cancer Cells in Ascitic Fluid.Cancers · 2024Article
- Deep Learning-Assisted Quantitative Measurement of Thoracolumbar Fracture Features on Lateral Radiographs.Neurospine · 2024Article
- Deep learning-based computed tomographic image super-resolution via wavelet embedding.Radiation physics and chemistry (Oxford, England : 1993) · 2023Article
- Validation of a Machine Learning Expert Supporting System, ImmunoGenius, Using Immunohistochemistry Results of 3000 Patients with Lymphoid Neoplasms.Diagnostics (Basel, Switzerland) · 2023Article
- Using an anomaly detection approach for the segmentation of colorectal cancer tumors in whole slide images.Journal of pathology informatics · 2023Article
- Application of artificial intelligence in diagnosis and treatment of colorectal cancer: A novel Prospect.Frontiers in medicine · 2023Review
- Tissue classification and diagnosis of colorectal cancer histopathology images using deep learning algorithms. Is the time ripe for clinical practice implementation?Przeglad gastroenterologiczny · 2023Review
- Development of quality assurance program for digital pathology by the Korean Society of Pathologists.Journal of pathology and translational medicine · 2022Article
- Recent Application of Artificial Intelligence in Non-Gynecological Cancer Cytopathology: A Systematic Review.Cancers · 2022Review
- Review
- Deep Learning on Histopathological Images for Colorectal Cancer Diagnosis: A Systematic Review.Diagnostics (Basel, Switzerland) · 2022Review
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7 authors.
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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 by two pathologists. For 39 test datasets, the average Dice score, the pixel accuracy, and the Jaccard score were 0.804 ± 0.125, 0.957 ± 0.025, and 0.690 ± 0.174, respectively. We can train the networks for the high-resolution image with the large region of interest compared to the result in the low-resolution and the small region of interest in the spatial domain. The average Dice score, pixel accuracy, and Jaccard score are significantly increased by 2.7%, 0.9%, and 2.7%, respectively. We believe that our approach has great potential for accurate diagnosis.
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