ArticleNPJ precision oncology2024
An interpretable machine learning system for colorectal cancer diagnosis from pathology slides.
Article in NPJ precision oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed, 47 citations in OpenAlex.
- Ensemble learning of pathology foundation models for precision oncology.Cancer cell · 2026Article
- Intraoperative margin assessment in colorectal cancer via AI-enhanced multiphoton microscopy.Journal of biomedical optics · 2026Article
- Bridging the Precancerous Gap in Colorectal Cancer Through AI-Enhanced Liquid Biopsy, Endoscopy, and Digital Pathology.Cancers · 2026Review
- Assessing Quality Gaps and Clinician Perspectives on AI Integration in Colorectal Cancer NGS Pathways.Current oncology (Toronto, Ont.) · 2026Article
- End-to-end multimodal pathology foundation model with clinical dialogue.Nature medicine · 2026Article
- nnMIL: a generalizable multiple instance learning framework for computational pathology.Nature biomedical engineering · 2026Article
- The impact of tissue detection on diagnostic artificial intelligence algorithms in prostate digital pathology.Scientific reports · 2026Article
- Integrating artificial intelligence (AI) into colorectal cancer reporting.The Journal of pathology · 2026Review
- Reliable classification of polyps based on artificial intelligence: a development and validation study.EClinicalMedicine · 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
- Early detection of colorectal cancer using a hybrid model with enhanced image quality and optimized classification.Physical and engineering sciences in medicine · 2025Article
- A Robust Framework for Domain-Generalized Classification of Ovarian Cancer Histology Images.Diagnostics (Basel, Switzerland) · 2025Article
- Quantification-based explainable artificial intelligence for deep learning decisions: clustering and visualization of quantitative morphometric features in hepatocellular carcinoma discrimination.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- A multimodal whole-slide foundation model for pathology.Nature medicine · 2025Article
- Histopathological classification of colorectal cancer based on domain-specific transfer learning and multi-model feature fusion.Scientific reports · 2025Article
- Bridging Domain Gaps in Computational Pathology: A Comparative Study of Adaptation Strategies.Sensors (Basel, Switzerland) · 2025Article
- Advanced deep learning for multi-class colorectal cancer histopathology: integrating transfer learning and ensemble methods.Quantitative imaging in medicine and surgery · 2025Article
- Artificial intelligence in gastrointestinal cancers: Diagnostic, prognostic, and surgical strategies.Cancer letters · 2025Review
- Gamma-Glutamyl Transferase Plus Carcinoembryonic Antigen Ratio Index: A Promising Biomarker Associated with Treatment Response to Neoadjuvant Chemotherapy for Patients with Colorectal Cancer Liver Metastases.Current oncology (Toronto, Ont.) · 2025Article
Corrections and comments
- Erratum issued
Authors and funding
13 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Considering the profound transformation affecting pathology practice, we aimed to develop a scalable artificial intelligence (AI) system to diagnose colorectal cancer from whole-slide images (WSI). For this, we propose a deep learning (DL) system that learns from weak labels, a sampling strategy that reduces the number of training samples by a factor of six without compromising performance, an approach to leverage a small subset of fully annotated samples, and a prototype with explainable predictions, active learning features and parallelisation. Noting some problems in the literature, this study is conducted with one of the largest WSI colorectal samples dataset with approximately 10,500 WSIs. Of these samples, 900 are testing samples. Furthermore, the robustness of the proposed method is assessed with two additional external datasets (TCGA and PAIP) and a dataset of samples collected directly from the proposed prototype. Our proposed method predicts, for the patch-based tiles, a class based on the severity of the dysplasia and uses that information to classify the whole slide. It is trained with an interpretable mixed-supervision scheme to leverage the domain knowledge introduced by pathologists through spatial annotations. The mixed-supervision scheme allowed for an intelligent sampling strategy effectively evaluated in several different scenarios without compromising the performance. On the internal dataset, the method shows an accuracy of 93.44% and a sensitivity between positive (low-grade and high-grade dysplasia) and non-neoplastic samples of 0.996. On the external test samples varied with TCGA being the most challenging dataset with an overall accuracy of 84.91% and a sensitivity of 0.996.
Identifiers
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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.