ArticleDiagnostics (Basel, Switzerland)2021
Colon Tissues Classification and Localization in Whole Slide Images Using Deep Learning.
Article in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- A comparative analysis of deep learning models for disease classification in multi-organ histopathological images.Scientific reports · 2026Article
- Choroidal imaging biomarkers as predictors of recurrent central serous chorioretinopathy: A multicenter retrospective study - MICRoN report number five.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Article
- Pediatric Inflammatory Bowel Disease Tissue Classification From Pathology Slide Images: Detecting Phenotypes Using Computer Vision.Gastro hep advances · 2026Article
- Review
- Deep learning-based differentiation of benign and malignant thyroid follicular neoplasms on multiscale intraoperative frozen pathological images: A multicenter diagnostic study.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2025Article
- Segmentation of ADPKD Computed Tomography Images with Deep Learning Approach for Predicting Total Kidney Volume.Biomedicines · 2025Article
- Computational pathology: A survey review and the way forward.Journal of pathology informatics · 2024Review
- Lung and Colon Cancer Detection Using a Deep AI Model.Cancers · 2024Article
- Association of the pathomics-collagen signature with lymph node metastasis in colorectal cancer: a retrospective multicenter study.Journal of translational medicine · 2024Article
- Use of artificial intelligence in the management of T1 colorectal cancer: a new tool in the arsenal or is deep learning out of its depth?Clinical endoscopy · 2024Review
- Article
- Localization of Colorectal Cancer Lesions in Contrast-Computed Tomography Images via a Deep Learning Approach.Bioengineering (Basel, Switzerland) · 2023Article
- Diagnostic Assessment of Deep Learning Algorithms for Frozen Tissue Section Analysis in Women with Breast Cancer.Cancer research and treatment · 2023Article
- Tissue classification and diagnosis of colorectal cancer histopathology images using deep learning algorithms. Is the time ripe for clinical practice implementation?Przeglad gastroenterologiczny · 2023Review
- The role of deep learning in diagnosing colorectal cancer.Przeglad gastroenterologiczny · 2023Review
- Review
- Special Issue: "Machine Learning for Computer-Aided Diagnosis in Biomedical Imaging".Diagnostics (Basel, Switzerland) · 2022Article
- Deep Learning on Histopathological Images for Colorectal Cancer Diagnosis: A Systematic Review.Diagnostics (Basel, Switzerland) · 2022Review
- Accurate Prediction of Metachronous Liver Metastasis in Stage I-III Colorectal Cancer Patients Using Deep Learning With Digital Pathological Images.Frontiers in oncology · 2022Article
Corrections and comments
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11 authors.
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Abstract
Colorectal cancer is one of the leading causes of cancer-related death worldwide. The early diagnosis of colon cancer not only reduces mortality but also reduces the burden related to the treatment strategies such as chemotherapy and/or radiotherapy. However, when the microscopic examination of the suspected colon tissue sample is carried out, it becomes a tedious and time-consuming job for the pathologists to find the abnormality in the tissue. In addition, there may be interobserver variability that might lead to conflict in the final diagnosis. As a result, there is a crucial need of developing an intelligent automated method that can learn from the patterns themselves and assist the pathologist in making a faster, accurate, and consistent decision for determining the normal and abnormal region in the colorectal tissues. Moreover, the intelligent method should be able to localize the abnormal region in the whole slide image (WSI), which will make it easier for the pathologists to focus on only the region of interest making the task of tissue examination faster and lesser time-consuming. As a result, artificial intelligence (AI)-based classification and localization models are proposed for determining and localizing the abnormal regions in WSI. The proposed models achieved F-score of 0.97, area under curve (AUC) 0.97 with pretrained Inception-v3 model, and F-score of 0.99 and AUC 0.99 with customized Inception-ResNet-v2 Type 5 (IR-v2 Type 5) model.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.