ArticleCancers2022
iMIL4PATH: A Semi-Supervised Interpretable Approach for Colorectal Whole-Slide Images.
Article in Cancers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
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Who cites it
20 citing papers in PubMed, 36 citations in OpenAlex.
- Ensemble learning of pathology foundation models for precision oncology.Cancer cell · 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
- 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
- Diagnostic accuracy of deep learning models for colorectal cancer detection in histopathological whole-slide images: a systematic review and diagnostic test accuracy meta-analysis.Frontiers in oncology · 2026Review
- Aligning computational pathology with clinical practice for colorectal cancer.NPJ precision oncology · 2025Review
- 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
- ColoViT: a synergistic integration of EfficientNet and vision transformers for advanced colon cancer detection.Journal of cancer research and clinical oncology · 2025Article
- Accurate colorectal cancer detection using a random hinge exponential distribution coupled attention network on pathological images.Abdominal radiology (New York) · 2025Article
- Bridging Domain Gaps in Computational Pathology: A Comparative Study of Adaptation Strategies.Sensors (Basel, Switzerland) · 2025Article
- A semi-supervised convolutional neural network for diagnosis of pancreatic ductal adenocarcinoma based on EUS-FNA cytological images.BMC cancer · 2025Article
- A pathology-attention multi-instance learning framework for multimodal classification of colorectal lesions.Frontiers in pharmacology · 2025Article
- An interpretable machine learning system for colorectal cancer diagnosis from pathology slides.NPJ precision oncology · 2024Article
- Evaluating AI in medicine: a comparative analysis of expert and ChatGPT responses to colorectal cancer questions.Scientific reports · 2024Article
- One label is all you need: Interpretable AI-enhanced histopathology for oncology.Seminars in cancer biology · 2023Review
- A CAD system for automatic dysplasia grading on H&E cervical whole-slide images.Scientific reports · 2023Article
- Using an anomaly detection approach for the segmentation of colorectal cancer tumors in whole slide images.Journal of pathology informatics · 2023Article
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
Abstract
Colorectal cancer (CRC) diagnosis is based on samples obtained from biopsies, assessed in pathology laboratories. Due to population growth and ageing, as well as better screening programs, the CRC incidence rate has been increasing, leading to a higher workload for pathologists. In this sense, the application of AI for automatic CRC diagnosis, particularly on whole-slide images (WSI), is of utmost relevance, in order to assist professionals in case triage and case review. In this work, we propose an interpretable semi-supervised approach to detect lesions in colorectal biopsies with high sensitivity, based on multiple-instance learning and feature aggregation methods. The model was developed on an extended version of the recent, publicly available CRC dataset (the CRC+ dataset with 4433 WSI), using 3424 slides for training and 1009 slides for evaluation. The proposed method attained 90.19% classification ACC, 98.8% sensitivity, 85.7% specificity, and a quadratic weighted kappa of 0.888 at slide-based evaluation. Its generalisation capabilities are also studied on two publicly available external datasets.
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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.