Deep Multi-Magnification Networks for multi-class breast cancer image segmentation

Ho, David Joon; Yarlagadda, Dig Vijay Kumar; D’Alfonso, Timothy M.; Hanna, Matthew G.; Grabenstetter, Anne; Ntiamoah, Peter; Brogi, Edi; Tan, Lee K.; Fuchs, Thomas J.

doi:10.1016/j.compmedimag.2021.101866

Cited by 94 publications

(77 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Multi-magnification models are an emergent technology in the field of computational pathology and have so far been applied to multiclass image segmentation of breast cancer slides. Ho and colleagues 24 developed a tissue segmentation architecture that processes a set of patches from multiple magnifications for the analysis of breast cancer histology images. They demonstrated far more accurate predictions using this method in comparison to standard DL approaches.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the Use of Single- and Multi-Magnification Convolutional Neural Networks for the Determination and Quantitation of Lesions in Nonclinical Pathology Studies

et al. 2021

View full text Add to dashboard Cite

Digital pathology platforms with integrated artificial intelligence have the potential to increase the efficiency of the nonclinical pathologist’s workflow through screening and prioritizing slides with lesions and highlighting areas with specific lesions for review. Herein, we describe the comparison of various single- and multi-magnification convolutional neural network (CNN) architectures to accelerate the detection of lesions in tissues. Different models were evaluated for defining performance characteristics and efficiency in accurately identifying lesions in 5 key rat organs (liver, kidney, heart, lung, and brain). Cohorts for liver and kidney were collected from TG-GATEs open-source repository, and heart, lung, and brain from internally selected R&D studies. Annotations were performed, and models were trained on each of the available lesion classes in the available organs. Various class-consolidation approaches were evaluated from generalized lesion detection to individual lesion detections. The relationship between the amount of annotated lesions and the precision/accuracy of model performance is elucidated. The utility of multi-magnification CNN implementations in specific tissue subtypes is also demonstrated. The use of these CNN-based models offers users the ability to apply generalized lesion detection to whole-slide images, with the potential to generate novel quantitative data that would not be possible with conventional image analysis techniques.

show abstract

Section: Introductionmentioning

confidence: 99%

Evaluation of the Use of Single- and Multi-Magnification Convolutional Neural Networks for the Determination and Quantitation of Lesions in Nonclinical Pathology Studies

et al. 2021

View full text Add to dashboard Cite

show abstract

“…The presented workflow using HoBBIT to systematically filter and compile pathology datasets and using the viewer to review and annotate WSI and evaluate and discuss model outputs has been used in numerous peer-reviewed published research studies for the development of advanced computational models, including clinical-grade cancer detection models for prostate biopsies, 45 deep interactive learning for efficient WSI labeling, 57 saliency annotation and prediction for pathologist at the microscope, 58 cancer subtyping approaches, 59 , 60 evaluation of frozen section accuracy after neoadjuvant chemotherapy for breast carcinoma, 61 quality control and pen annotation extraction of WSI, 62 , 63 cell nucleus detection, 64 and deep multimagnification networks for breast cancer segmentation. 65 …”

Section: Resultsmentioning

confidence: 99%

Integrated digital pathology at scale: A solution for clinical diagnostics and cancer research at a large academic medical center

Schüffler

Geneslaw

Yarlagadda

et al. 2021

Journal of the American Medical Informatics Association

Self Cite

View full text Add to dashboard Cite

Objective Broad adoption of digital pathology (DP) is still lacking, and examples for DP connecting diagnostic, research, and educational use cases are missing. We blueprint a holistic DP solution at a large academic medical center ubiquitously integrated into clinical workflows; researchapplications including molecular, genetic, and tissue databases; and educational processes. Materials and Methods We built a vendor-agnostic, integrated viewer for reviewing, annotating, sharing, and quality assurance of digital slides in a clinical or research context. It is the first homegrown viewer cleared by New York State provisional approval in 2020 for primary diagnosis and remote sign-out during the COVID-19 (coronavirus disease 2019) pandemic. We further introduce an interconnected Honest Broker for BioInformatics Technology (HoBBIT) to systematically compile and share large-scale DP research datasets including anonymized images, redacted pathology reports, and clinical data of patients with consent. Results The solution has been operationally used over 3 years by 926 pathologists and researchers evaluating 288 903 digital slides. A total of 51% of these were reviewed within 1 month after scanning. Seamless integration of the viewer into 4 hospital systems clearly increases the adoption of DP. HoBBIT directly impacts the translation of knowledge in pathology into effective new health measures, including artificial intelligence–driven detection models for prostate cancer, basal cell carcinoma, and breast cancer metastases, developed and validated on thousands of cases. Conclusions We highlight major challenges and lessons learned when going digital to provide orientation for other pathologists. Building interconnected solutions will not only increase adoption of DP, but also facilitate next-generation computational pathology at scale for enhanced cancer research.

show abstract

“…Most AI models are developed using smaller tiles, rather than entire WSI, as input data, missing the efficacy warranted by the dual approach of the pathologist. To avoid this drawback recent studies have suggested the introduction of networks trained with images obtained at different magnification [64,65]. A different technical solution was proposed by Lin et al [66] who introduced in the neural network a further layer aimed to reconstruct the loss occurred in max pooling layers.…”

Section: Artificial Intelligence In Pathology: Future Perspetivesmentioning

confidence: 99%

Artificial Intelligence & Tissue Biomarkers: Advantages, Risks and Perspectives for Pathology

Lancellotti

Cancian

Savevski

et al. 2021

Cells

View full text Add to dashboard Cite

Tissue Biomarkers are information written in the tissue and used in Pathology to recognize specific subsets of patients with diagnostic, prognostic or predictive purposes, thus representing the key elements of Personalized Medicine. The advent of Artificial Intelligence (AI) promises to further reinforce the role of Pathology in the scenario of Personalized Medicine: AI-based devices are expected to standardize the evaluation of tissue biomarkers and also to discover novel information, which would otherwise be ignored by human review, and use them to make specific predictions. In this review we will present how AI has been used to support Tissue Biomarkers evaluation in the specific field of Pathology, give an insight to the intriguing field of AI-based biomarkers and discuss possible advantages, risk and perspectives for Pathology.

show abstract

Deep Multi-Magnification Networks for multi-class breast cancer image segmentation

Cited by 94 publications

References 26 publications

Evaluation of the Use of Single- and Multi-Magnification Convolutional Neural Networks for the Determination and Quantitation of Lesions in Nonclinical Pathology Studies

Evaluation of the Use of Single- and Multi-Magnification Convolutional Neural Networks for the Determination and Quantitation of Lesions in Nonclinical Pathology Studies

Integrated digital pathology at scale: A solution for clinical diagnostics and cancer research at a large academic medical center

Artificial Intelligence & Tissue Biomarkers: Advantages, Risks and Perspectives for Pathology

Contact Info

Product

Resources

About