Drozdzal, Michal

The Liver Tumor Segmentation Benchmark (LiTS)

Bilic, P., Christ, P., Li, H. B., Vorontsov, E., Ben-Cohen, A., Kaissis, G., Szeskin, A., Jacobs, C., Mamani, G. E. H., Chartrand, G., et al. (2019) · arXiv

Medical Image Segmentation TechniquesBrain Tumor Detection and Classification

Cited by 1,412

The Liver Tumor Segmentation Benchmark (LiTS)

Bilic, P., Christ, P., Li, H. B., Vorontsov, E., Ben-Cohen, A., Kaissis, G., Szeskin, A., Jacobs, C., Mamani, G. E. H., Chartrand, G., et al. (2023) · Medical Image Analysis

Medical Image Segmentation TechniquesBrain Tumor Detection and Classification

Cited by 1,412

Deep Learning: A Primer for Radiologists

Chartrand, G., Cheng, P. M., Vorontsov, E., Drozdzal, M., Turcotte, S., Pal, C. J., Kadoury, S., & Tang, A. (2017) · RadioGraphics

Artificial Intelligence in Healthcare and EducationRadiomics and Machine Learning in Medical Imaging

Cited by 1,115

Reducing Uncertainty in Undersampled MRI Reconstruction With Active Acquisition

Zhang, Z., Romero, A., Muckley, M. J., Vincent, P., Yang, L., & Drozdzal, M. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Advanced MRI Techniques and ApplicationsImage and Signal Denoising Methods

Cited by 106

Towards Geographic Inclusion in the Evaluation of Text-to-Image Models

Hall, M., Bell, S. J., Ross, C., Williams, A., Drozdzal, M., & Soriano, A. R. (2024) · The 2024 ACM Conference on Fairness Accountability and Transparency

Generative Adversarial Networks and Image SynthesisEthics and Social Impacts of AI

Cited by 11

Elucidating image-to-set prediction: An analysis of models, losses and datasets

Pineda, L., Salvador, A., Drozdzal, M., & Romero, A. (2019) · arXiv

Advanced Neural Network ApplicationsMultimodal Machine Learning Applications

Cited by 6