ArticleOncology letters2019
Deep learning techniques for detecting preneoplastic and neoplastic lesions in human colorectal histological images.
Article in Oncology letters, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
24 citing papers in PubMed, 44 citations in OpenAlex.
- Two-Step Ensemble Convolutional Neural Networks for Colonoscopic Biopsy Classification Resembling Pathologists' Process.Journal of Korean medical science · 2026Article
- Lung and Colon Cancer Detection Using a Deep AI Model.Cancers · 2024Article
- Recognition of Colon Polyps (Tubular Adenoma, Villous Adenoma) and Normal Colon Epithelium Histomorphology with Transfer Learning.The Eurasian journal of medicine · 2024Article
- An efficient computational framework for gastrointestinal disorder prediction using attention-based transfer learning.PeerJ. Computer science · 2024Article
- Classification of Multiple H&E Images via an Ensemble Computational Scheme.Entropy (Basel, Switzerland) · 2023Article
- Applying Deep Transfer Learning to Assess the Impact of Imaging Modalities on Colon Cancer Detection.Diagnostics (Basel, Switzerland) · 2023Article
- Application of artificial neural network algorithm in pathological diagnosis and prognosis prediction of digestive tract malignant tumors.Zhejiang da xue xue bao. Yi xue ban = Journal of Zhejiang University. Medical sciences · 2023Review
- Histopathology image classification: highlighting the gap between manual analysis and AI automation.Frontiers in oncology · 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
- Role of artificial intelligence in risk prediction, prognostication, and therapy response assessment in colorectal cancer: current state and future directions.Frontiers in oncology · 2023Review
- Colon Cancer Diagnosis Based on Machine Learning and Deep Learning: Modalities and Analysis Techniques.Sensors (Basel, Switzerland) · 2022Review
- Deep Neural Network Models for Colon Cancer Screening.Cancers · 2022Review
- HunCRC: annotated pathological slides to enhance deep learning applications in colorectal cancer screening.Scientific data · 2022Article
- Deep Learning on Histopathological Images for Colorectal Cancer Diagnosis: A Systematic Review.Diagnostics (Basel, Switzerland) · 2022Review
- Applications of Artificial Intelligence in Screening, Diagnosis, Treatment, and Prognosis of Colorectal Cancer.Current oncology (Toronto, Ont.) · 2022Review
- Review
- Automated Detection and Characterization of Colon Cancer with Deep Convolutional Neural Networks.Journal of healthcare engineering · 2022Article
- CAD systems for colorectal cancer from WSI are still not ready for clinical acceptance.Scientific reports · 2021Review
- Status quo and future prospects of artificial neural network from the perspective of gastroenterologists.World journal of gastroenterology · 2021Review
- Requirements for implementation of artificial intelligence in the practice of gastrointestinal pathology.World journal of gastroenterology · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Trained pathologists base colorectal cancer identification on the visual interpretation of microscope images. However, image labeling is not always straightforward and this repetitive task is prone to mistakes due to human distraction. Significant efforts are underway to develop informative tools to assist pathologists and decrease the burden and frequency of errors. The present study proposes a deep learning approach to recognize four different stages of cancerous tissue development, including normal mucosa, early preneoplastic lesion, adenoma and cancer. A dataset of human colon tissue images collected and labeled over a 10-year period by a team of pathologists was partitioned into three sets. These were used to train, validate and test the neural network, comprising several convolutional and a few linear layers. The approach used in the present study is 'direct'; it labels raw images and bypasses the segmentation step. An overall accuracy of >95% was achieved, with the majority of mislabeling referring to a near category. Tests on an external dataset with a different resolution yielded accuracies >80%. The present study demonstrated that the neural network, when properly trained, can provide fast, accurate and reproducible labeling for colon cancer images, with the potential to significantly improve the quality and speed of medical diagnoses.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.