ReviewScientific reports2021
CAD systems for colorectal cancer from WSI are still not ready for clinical acceptance.
Review in Scientific reports, 2021. 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
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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.
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Who cites it
24 citing papers in PubMed.
- End-to-end multimodal pathology foundation model with clinical dialogue.Nature medicine · 2026Article
- A generalizable pathology foundation model using a unified knowledge distillation pretraining framework.Nature biomedical engineering · 2026Article
- Weakly supervised colorectal gland segmentation through self-supervised learning and attention-based pseudo-labeling.Scientific reports · 2026Article
- Colorectal cancer organoids: a developmental trajectory from Matrigel embedding to AI-empowered precision medicine.Frontiers in oncology · 2026Review
- A multimodal whole-slide foundation model for pathology.Nature medicine · 2025Article
- Narrative review of the application of artificial intelligence-related technologies in the diagnosis of pulmonary nodules with recommendations for clinical practice and future research.Journal of thoracic disease · 2025Review
- Bridging Domain Gaps in Computational Pathology: A Comparative Study of Adaptation Strategies.Sensors (Basel, Switzerland) · 2025Article
- Article
- A pathology-attention multi-instance learning framework for multimodal classification of colorectal lesions.Frontiers in pharmacology · 2025Article
- Computational pathology: A survey review and the way forward.Journal of pathology informatics · 2024Review
- Artificial intelligence assisted diagnosis of early tc markers and its application.Discover oncology · 2024Review
- An interpretable machine learning system for colorectal cancer diagnosis from pathology slides.NPJ precision oncology · 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
- Efficacy of Artificial Intelligence in the Categorisation of Paediatric Pneumonia on Chest Radiographs: A Systematic Review.Children (Basel, Switzerland) · 2023Review
- A CAD system for automatic dysplasia grading on H&E cervical whole-slide images.Scientific reports · 2023Article
- Detection of Colorectal Adenocarcinoma and Grading Dysplasia on Histopathologic Slides Using Deep Learning.The American journal of pathology · 2023Article
- Article
- EBHI-Seg: A novel enteroscope biopsy histopathological hematoxylin and eosin image dataset for image segmentation tasks.Frontiers in medicine · 2023Article
- Data-driven color augmentation for H&E stained images in computational pathology.Journal of pathology informatics · 2023Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Most oncological cases can be detected by imaging techniques, but diagnosis is based on pathological assessment of tissue samples. In recent years, the pathology field has evolved to a digital era where tissue samples are digitised and evaluated on screen. As a result, digital pathology opened up many research opportunities, allowing the development of more advanced image processing techniques, as well as artificial intelligence (AI) methodologies. Nevertheless, despite colorectal cancer (CRC) being the second deadliest cancer type worldwide, with increasing incidence rates, the application of AI for CRC diagnosis, particularly on whole-slide images (WSI), is still a young field. In this review, we analyse some relevant works published on this particular task and highlight the limitations that hinder the application of these works in clinical practice. We also empirically investigate the feasibility of using weakly annotated datasets to support the development of computer-aided diagnosis systems for CRC from WSI. Our study underscores the need for large datasets in this field and the use of an appropriate learning methodology to gain the most benefit from partially annotated datasets. The CRC WSI dataset used in this study, containing 1,133 colorectal biopsy and polypectomy samples, is available upon reasonable request.
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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.