B. Helba

发表

Comparison of the accuracy of human readers versus machine-learning algorithms for pigmented skin lesion classification: an open, web-based, international, diagnostic study.

R. Hofmann-Wellenhof, H. Cabo, A. Lallas, 2019, The Lancet. Oncology.

Results of the 2016 International Skin Imaging Collaboration International Symposium on Biomedical Imaging challenge: Comparison of the accuracy of computer algorithms to dermatologists for the diagnosis of melanoma from dermoscopic images

A. Kalloo, A. Marghoob, N. Codella, 2018, Journal of the American Academy of Dermatology.

Computer Algorithms Show Potential for Improving Dermatologists' Accuracy to Diagnose Cutaneous Melanoma; Results of ISIC 2017.

A. Kalloo, N. Codella, David Gutman, 2020, Journal of the American Academy of Dermatology.

A multimodal cell census and atlas of the mammalian primary motor cortex

Satrajit S. Ghosh, Nicholas N. Foster, Evan Z. Macosko, 2020, Nature.

A New Platform for Quantitative CT Imaging Dose Optimization

B. Helba, D. Yankelevitz, R. Avila, 2013 .

Validation of artificial intelligence prediction models for skin cancer diagnosis using dermoscopy images: the 2019 International Skin Imaging Collaboration Grand Challenge

P. Tschandl, N. Codella, David Gutman, 2022, The Lancet. Digital health.

Computational Modeling of Macrophage Iron Sequestration during Host Defense against Aspergillus

B. Helba, B. Mehrad, C. Atkinson, 2022, bioRxiv.

BCN20000: Dermoscopic Lesions in the Wild pdf

Verónica Vilaplana, Allan C. Halpern, Noel C. F. Codella, 2019, Scientific data.

Skin lesion analysis toward melanoma detection: A challenge at the 2017 International symposium on biomedical imaging (ISBI), hosted by the international skin imaging collaboration (ISIC)

Noel C. F. Codella, David Gutman, M. Emre Celebi, 2016, 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018).

Checklist for Evaluation of Image-Based Artificial Intelligence Reports in Dermatology: CLEAR Derm Consensus Guidelines From the International Skin Imaging Collaboration Artificial Intelligence Working Group.

P. Tschandl, N. Codella, David Gutman, 2021, JAMA dermatology.

A modular computational framework for medical digital twins

J. Masison, J. Beezley, Y. Mei, 2021, Proceedings of the National Academy of Sciences.

Deep learning ensembles for melanoma recognition in dermoscopy images pdf

Sharath Pankanti, John R. Smith, Noel C. F. Codella, 2016, IBM J. Res. Dev..

Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC) pdf

Noel C. F. Codella, M. Emre Celebi, Veronica Rotemberg, 2019, ArXiv.

Tumor volume measurement error using computed tomography imaging in a phase II clinical trial in lung cancer

Karthik Krishnan, David F Yankelevitz, Claudia I Henschke, 2016, Journal of medical imaging.

Endocrine Pathology

B. Helba, A. Bellizzi, T. O'Dorisio, 2023, Laboratory Investigation.

Simultaneous Estimation of Bias and Resolution in PET Images With a Long-Lived “Pocket” Phantom System

Darrin W. Byrd, B. Helba, Paul Kinahan, 2018, Tomography.

A patient-centric dataset of images and metadata for identifying melanomas using clinical context pdf

George Shih, H. Peter Soyer, Marc Combalia, 2020, Scientific Data.

Skin Lesion Analysis toward Melanoma Detection: A Challenge at the International Symposium on Biomedical Imaging (ISBI) 2016, hosted by the International Skin Imaging Collaboration (ISIC)

N. Codella, David Gutman, B. Helba, 2016, ArXiv.