4.8 Article

Swarm learning for decentralized artificial intelligence in cancer histopathology

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article Oncology

Weakly supervised annotation-free cancer detection and prediction of genotype in routine histopathology

Peter Leonard Schrammen et al.

Summary: SLAM is a simple yet powerful computational pathology method that utilizes a neural network to predict tumor presence and genetic alterations directly from histopathology slides, without the need for complex manual annotations. It demonstrates high reliability and accuracy in clinically relevant tasks, making it a valuable tool for disease analysis.

JOURNAL OF PATHOLOGY (2022)

Article Computer Science, Artificial Intelligence

Federated learning for computational pathology on gigapixel whole slide images

Ming Y. Lu et al.

Summary: Deep learning-based computational pathology algorithms show great potential in various tasks, but require large high-quality training data. Collaborative integration of medical data from multiple institutions can enhance model performance, although privacy concerns and data sharing complexities remain challenging.

MEDICAL IMAGE ANALYSIS (2022)

Article Urology & Nephrology

Artificial Intelligence-based Detection of FGFR3 Mutational Status Directly from Routine Histology in Bladder Cancer: A Possible Preselection for Molecular Testing?

Chiara Maria Lavinia Loeffler et al.

Summary: In this study, an artificial intelligence system was used to predict mutations of the FGFR3 gene in bladder cancer from histological slides, and it was found to detect these mutations with high accuracy. In the future, this system could be used to preselect patients for further molecular testing.

EUROPEAN UROLOGY FOCUS (2022)

Review Oncology

Harnessing multimodal data integration to advance precision oncology

Kevin M. Boehm et al.

Summary: Advancements in quantitative biomarker development have accelerated insights for cancer patients, but integrated approaches across modalities remain underdeveloped. To succeed, efforts in data engineering, computational methods for heterogeneous data analysis, and instantiation of synergistic data models in biomedical research are necessary.

NATURE REVIEWS CANCER (2022)

Article Gastroenterology & Hepatology

Image-based consensus molecular subtype (imCMS) classification of colorectal cancer using deep learning

Korsuk Sirinukunwattana et al.

Summary: This study utilized deep learning to analyze histological images of colorectal cancer tissue samples and successfully predicted consensus molecular subtypes, addressing the issue of molecular classification. The image-based classification method accurately identified samples that were unclassifiable by RNA expression analysis, showing potential for biological stratification.
Article Cell Biology

Identifying mismatch repair-deficient colon cancer: near-perfect concordance between immunohistochemistry and microsatellite instability testing in a large, population-based series

Maurice B. Loughrey et al.

Summary: This study compared PCR-based microsatellite instability (MSI) testing and MMR protein immunohistochemistry (IHC) in colorectal cancer cases and found that both methods are equally proficient in establishing MMR/MSI status, but awareness of potential pitfalls is important. The choice of methodology may depend on available services and expertise.

HISTOPATHOLOGY (2021)

Review Oncology

Deep learning in cancer pathology: a new generation of clinical biomarkers

Amelie Echle et al.

Summary: Clinical workflows in oncology rely on molecular biomarkers for prediction and prognosis. Deep learning technology can extract biomarkers directly from routine histology images, potentially enhancing clinical decision-making, but require rigorous external validation in clinical settings.

BRITISH JOURNAL OF CANCER (2021)

Article Cell Biology

Lynch syndrome screening in colorectal cancer: results of a prospective 2-year regional programme validating the NICE diagnostics guidance pathway throughout a 5.2-million population

Nicholas P. West et al.

Summary: This study validates the NICE screening pathway for colorectal cancer patients in the Yorkshire and Humber region of the UK. The results demonstrate that the LS screening pathway is deliverable at scale and can identify significant numbers of patients with dMMR.

HISTOPATHOLOGY (2021)

Article Multidisciplinary Sciences

AI-based pathology predicts origins for cancers of unknown primary

Ming Y. Lu et al.

Summary: Cancer of unknown primary (CUP) is a difficult diagnosis as the primary site of tumor origin cannot be determined. The deep-learning-based algorithm TOAD provides a differential diagnosis for the origin of the primary tumor, achieving high accuracy on test sets and reducing the occurrence of CUP by assisting in assigning differential diagnoses for complicated cases.

NATURE (2021)

Article Multidisciplinary Sciences

Swarm Learning for decentralized and confidential clinical machine learning

Stefanie Warnat-Herresthal et al.

Summary: The study introduces Swarm Learning, a decentralized machine-learning approach that integrates medical data globally while complying with local privacy regulations. Using over 16,400 blood transcriptomes and more than 95,000 chest X-ray images, the research shows that Swarm Learning classifiers outperform those developed at individual sites.

NATURE (2021)

Editorial Material Biochemistry & Molecular Biology

AI in medicine must be explainable

Shinjini Kundu

NATURE MEDICINE (2021)

Article Multidisciplinary Sciences

The impact of site-specific digital histology signatures on deep learning model accuracy and bias

Frederick M. Howard et al.

Summary: Deep learning models trained on TCGA can predict various features directly from histology, but site-specific histologic signatures may introduce bias into the accuracy estimates of these models.

NATURE COMMUNICATIONS (2021)

Article Oncology

Artificial Intelligence Can Cut Costs While Maintaining Accuracy in Colorectal Cancer Genotyping

Alec J. Kacew et al.

Summary: A study found that in first-line treatment of metastatic colorectal cancer, using high-sensitivity artificial intelligence followed by confirmatory high-specificity polymerase chain reaction or immunohistochemistry panel testing strategy resulted in the greatest project cost savings, while the strategy of using high-specificity artificial intelligence alone had the most favorable clinical impact in guiding genotype-directed treatment.

FRONTIERS IN ONCOLOGY (2021)

Editorial Material Engineering, Biomedical

Synthetic data in machine learning for medicine and healthcare

Richard J. Chen et al.

Summary: The proliferation of synthetic data in artificial intelligence for medicine and healthcare has raised concerns about the vulnerabilities of the software and the challenges faced by current policies.

NATURE BIOMEDICAL ENGINEERING (2021)

Article Medical Informatics

Development and validation of a weakly supervised deep learning framework to predict the status of molecular pathways and key mutations in colorectal cancer from routine histology images: a retrospective study

Mohsin Bilal et al.

Summary: This study developed a novel deep learning pipeline to predict the status of key molecular pathways and mutations in colorectal cancer from tissue slides, and the results showed better performance compared to previous methods.

LANCET DIGITAL HEALTH (2021)

Article Medical Informatics

Correspondence to: Dr Jakob Nikolas Kather, @jnkath For the Genomic Data Commons data portal see https://portal.gdc.cancer.gov See Online for appendix

Hannah Sophie Muti et al.

Summary: This study aimed to develop and validate deep learning-based classifiers to detect microsatellite instability and EBV status in gastric cancer tissue samples. The results showed that the deep learning-based classifier had high accuracy in detecting microsatellite instability and moderate effectiveness in detecting EBV status.

LANCET DIGITAL HEALTH (2021)

Article Computer Science, Hardware & Architecture

A Blockchain-Based Decentralized Federated Learning Framework with Committee Consensus

Yuzheng Li et al.

Summary: Federated learning allows users to collaboratively train a shared global model without exposing private data, but security concerns arise due to potential attacks from malicious clients or central servers. A decentralized federated learning framework based on blockchain, called BFLC, is proposed to address these security issues. It utilizes a Committee consensus mechanism to enhance security and scalability, as demonstrated in experiments using real-world datasets.

IEEE NETWORK (2021)

Article Computer Science, Artificial Intelligence

Morphological and molecular breast cancer profiling through explainable machine learning

Alexander Binder et al.

Summary: The study introduces an explainable machine-learning approach for integrated profiling of morphological, molecular, and clinical features from breast cancer histology. By detecting cancer cells, predicting molecular features, and assessing the link between morphological and molecular properties, the approach aims to promote basic cancer research and precision medicine.

NATURE MACHINE INTELLIGENCE (2021)

Review Oncology

Designing deep learning studies in cancer diagnostics

Andreas Kleppe et al.

Summary: The number of publications on deep learning for cancer diagnostics is increasing rapidly, but clinical translation progress is slow. It is advocated to estimate performance in external cohorts, define a primary analysis in a standardized protocol stored online, and establish recommended protocol items in the field to facilitate transition to the clinic.

NATURE REVIEWS CANCER (2021)

Article Gastroenterology & Hepatology

Clinical-Grade Detection of Microsatellite Instability in Colorectal Tumors by Deep Learning

Amelie Echle et al.

GASTROENTEROLOGY (2020)

Article Gastroenterology & Hepatology

Colonoscopy and Reduction of Colorectal Cancer Risk by Molecular Tumor Subtypes: A Population-Based Case-Control Study

Michael Hoffmeister et al.

AMERICAN JOURNAL OF GASTROENTEROLOGY (2020)

Editorial Material Gastroenterology & Hepatology

Development of AI-based pathology biomarkers in gastrointestinal and liver cancer

Jakob N. Kather et al.

NATURE REVIEWS GASTROENTEROLOGY & HEPATOLOGY (2020)

Article Multidisciplinary Sciences

A deep learning model to predict RNA-Seq expression of tumours from whole slide images

Benoit Schmauch et al.

NATURE COMMUNICATIONS (2020)

Editorial Material Biochemistry & Molecular Biology

Minimum information about clinical artificial intelligence modeling: the MI-CLAIM checklist

Beau Norgeot et al.

NATURE MEDICINE (2020)

Article Health Care Sciences & Services

The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database

Stan Benjamens et al.

NPJ DIGITAL MEDICINE (2020)

Review Computer Science, Artificial Intelligence

Secure, privacy-preserving and federated machine learning in medical imaging

Georgios A. Kaissis et al.

NATURE MACHINE INTELLIGENCE (2020)

Article Oncology

Pan-cancer image-based detection of clinically actionable genetic alterations

Jakob Nikolas Kather et al.

NATURE CANCER (2020)

Article Biochemistry & Molecular Biology

Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer

Jakob Nikolas Kather et al.

NATURE MEDICINE (2019)

Article Biochemistry & Molecular Biology

Clinical-grade computational pathology using weakly supervised deep learning on whole slide images

Gabriele Campanella et al.

NATURE MEDICINE (2019)

Article Biochemistry & Molecular Biology

Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning

Nicolas Coudray et al.

NATURE MEDICINE (2018)

Article Pathology

Pathologic Predictors of Microsatellite Instability in Colorectal Cancer

Joel K. Greenson et al.

AMERICAN JOURNAL OF SURGICAL PATHOLOGY (2009)