ArticlePeerJ2017
Segmentation and classification of colon glands with deep convolutional neural networks and total variation regularization.
Article in PeerJ, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 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
41 citing papers in PubMed, 137 citations in OpenAlex.
- The Use of Artificial Intelligence in the Liver Histopathology Field: A Systematic Review.Diagnostics (Basel, Switzerland) · 2024Review
- Immunohistochemical Stain-Aided Annotation Accelerates Machine Learning and Deep Learning Model Development in the Pathologic Diagnosis of Nasopharyngeal Carcinoma.Diagnostics (Basel, Switzerland) · 2023Article
- Segmentation of human functional tissue units in support of a Human Reference Atlas.Communications biology · 2023Article
- Artificial intelligence and digital biomarker in precision pathology guiding immune therapy selection and precision oncology.Cancer reports (Hoboken, N.J.) · 2023Review
- Deep learning image analysis quantifies tumor heterogeneity and identifies microsatellite instability in colon cancer.Journal of surgical 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
- Colon Cancer Diagnosis Based on Machine Learning and Deep Learning: Modalities and Analysis Techniques.Sensors (Basel, Switzerland) · 2022Review
- Deep Learning Approaches in Histopathology.Cancers · 2022Review
- Deep Neural Network Models for Colon Cancer Screening.Cancers · 2022Review
- Article
- Colon Cancer Grading Using Infrared Spectroscopic Imaging-Based Deep Learning.Applied spectroscopy · 2022Article
- Deep Learning on Histopathological Images for Colorectal Cancer Diagnosis: A Systematic Review.Diagnostics (Basel, Switzerland) · 2022Review
- A promising deep learning-assistive algorithm for histopathological screening of colorectal cancer.Scientific reports · 2022Article
- A review on deep learning in medical image analysis.International journal of multimedia information retrieval · 2022Article
- Artificial intelligence assists precision medicine in cancer treatment.Frontiers in oncology · 2022Review
- CVD-HNet: Classifying Pneumonia and COVID-19 in Chest X-ray Images Using Deep Network.Wireless personal communications · 2022Article
- Deep learning-based histopathological segmentation for whole slide images of colorectal cancer in a compressed domain.Scientific reports · 2021Article
- Accurate recognition of colorectal cancer with semi-supervised deep learning on pathological images.Nature communications · 2021Article
- Automatic Detection of Colorectal Polyps Using Transfer Learning.Sensors (Basel, Switzerland) · 2021Article
- Quantitative Image Analysis for Tissue Biomarker Use: A White Paper From the Digital Pathology Association.Applied immunohistochemistry & molecular morphology : AIMM · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Segmentation of histopathology sections is a necessary preprocessing step for digital pathology. Due to the large variability of biological tissue, machine learning techniques have shown superior performance over conventional image processing methods. Here we present our deep neural network-based approach for segmentation and classification of glands in tissue of benign and malignant colorectal cancer, which was developed to participate in the
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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.