Veta, Mitko

Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer

Ehteshami Bejnordi, B., Veta, M., Johannes van Diest, P., van Ginneken, B., Karssemeijer, N., Litjens, G., van der Laak, J. A. W. M., and the CAMELYON16 Consortium, Hermsen, M., Manson, Q. F., et al. (2017) · JAMA

AI in cancer detectionRadiomics and Machine Learning in Medical Imaging

Cited by 3,293

Mitosis Counting in Breast Cancer: Object-Level Interobserver Agreement and Comparison to an Automatic Method

Veta, M., van Diest, P. J., Jiwa, M., Al-Janabi, S., & Pluim, J. P. W. (2016) · PLOS ONE

AI in cancer detectionCell Image Analysis Techniques

Cited by 127

Dynamic imaging of myelin pathology in physiologically preserved human brain tissue using third harmonic generation microscopy

Meijns, N. R. C., Blokker, M., Idema, S., ’t Hart, B. A., Veta, M., Ettema, L., van Iersel, J., Zhang, Z., Schenk, G. J., Groot, M. L., et al. (2025) · PLOS ONE

Advanced Fluorescence Microscopy TechniquesSpectroscopy Techniques in Biomedical and Chemical Research

Cited by 10

Digital Pathology: 15th European Congress, ECDP 2019, Warwick, UK, April 10–13, 2019, Proceedings

Bankhead, P., Janowczyk, A. R., Reyes‐Aldasoro, C. C., Sirinukunwattana, K., Veta, M., Reyes-Aldasoro, C. C., Veta, M., Janowczyk, A., Bankhead, P., & Sirinukunwattana, K. (2019) · Lecture Notes in Computer Science

AI in cancer detectionCell Image Analysis Techniques

Cited by 9