ArticleNPJ digital medicine2022
Unleashing the potential of digital pathology data by training computer-aided diagnosis models without human annotations.
Article in NPJ digital medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed.
- Weakly Supervised Learning of Oral Cavity Images for Separating Precancer from Cancer.IEEE International Conference on Omni-layer Intelligent Systems : COINS. IEEE International Conference on Omni-layer Intelligent Systems · 2026Article
- A benchmark study of vision and pathology foundation models for computational pathology.Nature communications · 2026Article
- Applications and challenges of utilizing digital pathology and AI-enabled workflows in clinical trials.Journal of pathology informatics · 2026Review
- Assessing skin cancer histopathology reporting against minimum dataset standards in a low-resource setting.BMC health services research · 2025Article
- The role of artificial intelligence-based foundation models and "copilots" in cancer pathology: potential and challenges.Journal of experimental & clinical cancer research : CR · 2025Review
- Multimodal prediction of tyrosine kinase inhibitors therapy outcomes in advanced EGFR-mutated NSCLC patients.Journal of translational medicine · 2025Article
- Automatic labels are as effective as manual labels in digital pathology images classification with deep learning.Journal of pathology informatics · 2025Article
- Systematic review and meta-analysis of artificial intelligence in classifying HER2 status in breast cancer immunohistochemistry.NPJ digital medicine · 2025Article
- Diagnostic classification in toxicologic pathology using attention-guided weak supervision and whole slide image features: a pilot study in rat livers.Scientific reports · 2025Article
- AI-powered 3D pathology protocol enhances enteric nervous system visualization and quantification for clinical diagnostics.Theranostics · 2025Article
- From Images to Genes: Radiogenomics Based on Artificial Intelligence to Achieve Non-Invasive Precision Medicine in Cancer Patients.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Computational pathology in the identification of HER2-low breast cancer: Opportunities and challenges.Journal of pathology informatics · 2024Review
- Leveraging weak complementary labels enhances semantic segmentation of hepatocellular carcinoma and intrahepatic cholangiocarcinoma.Scientific reports · 2024Article
- A Genomics-Driven Artificial Intelligence-Based Model Classifies Breast Invasive Lobular Carcinoma and Discovers CDH1 Inactivating Mechanisms.Cancer research · 2024Article
- Image Analysis in Histopathology and Cytopathology: From Early Days to Current Perspectives.Journal of imaging · 2024Review
- The 1 million words pathology report or the challenge of a reproducible and meaningful message.ESMO real world data and digital oncology · 2024Review
- An AI based, open access screening tool for early diagnosis of Burkitt lymphoma.Frontiers in medicine · 2024Article
- Artificial Intelligence in Bone Marrow Histological Diagnostics: Potential Applications and Challenges.Pathobiology : journal of immunopathology, molecular and cellular biology · 2024Review
- Quilt-1M: One Million Image-Text Pairs for Histopathology.Advances in neural information processing systems · 2023Article
- Revolutionizing Cancer Research: The Impact of Artificial Intelligence in Digital Biobanking.Journal of personalized medicine · 2023Review
Corrections and comments
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Authors and funding
20 authors.
Funding
Abstract
The digitalization of clinical workflows and the increasing performance of deep learning algorithms are paving the way towards new methods for tackling cancer diagnosis. However, the availability of medical specialists to annotate digitized images and free-text diagnostic reports does not scale with the need for large datasets required to train robust computer-aided diagnosis methods that can target the high variability of clinical cases and data produced. This work proposes and evaluates an approach to eliminate the need for manual annotations to train computer-aided diagnosis tools in digital pathology. The approach includes two components, to automatically extract semantically meaningful concepts from diagnostic reports and use them as weak labels to train convolutional neural networks (CNNs) for histopathology diagnosis. The approach is trained (through 10-fold cross-validation) on 3'769 clinical images and reports, provided by two hospitals and tested on over 11'000 images from private and publicly available datasets. The CNN, trained with automatically generated labels, is compared with the same architecture trained with manual labels. Results show that combining text analysis and end-to-end deep neural networks allows building computer-aided diagnosis tools that reach solid performance (micro-accuracy = 0.908 at image-level) based only on existing clinical data without the need for manual annotations.
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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.