Publications of Matin Hosseinzadeh

Papers in international journals

  1. J.S. Bosma, A. Saha, M. Hosseinzadeh, I. Slootweg, M. de Rooij and H. Huisman, "Semi-supervised Learning with Report-guided Pseudo Labels for Deep Learning-based Prostate Cancer Detection Using Biparametric MRI", Radiology: Artificial Intelligence, 2023:e230031.
    Abstract DOI Cited by ~5
  2. M. Sunoqrot, A. Saha, M. Hosseinzadeh, M. Elschot and H. Huisman, "Artificial Intelligence for Prostate MRI: Open Datasets, Available Applications, and Grand Challenges", European Radiology Experimental, 2022:35.
    Abstract DOI Cited by ~26
  3. M. Hosseinzadeh, A. Saha, P. Brand, I. Slootweg, M. de Rooij and H. Huisman, "Deep learning-assisted prostate cancer detection on bi-parametric MRI: minimum training data size requirements and effect of prior knowledge", European Radiology, 2021.
    Abstract DOI Download Cited by ~46
  4. A. Saha, M. Hosseinzadeh and H. Huisman, "End-to-end Prostate Cancer Detection in bpMRI via 3D CNNs: Effects of Attention Mechanisms, Clinical Priori and Decoupled False Positive Reduction", Medical Image Analysis, 2021:102155.
    Abstract DOI Algorithm Download Cited by ~87
  5. A. Rossi, M. Hosseinzadeh, M. Bianchini, F. Scarselli and H. Huisman, "Multi-modal siamese network for diagnostically similar lesion retrieval in prostate MRI", IEEE Transactions on Medical Imaging, 2020.
    Abstract DOI PMID Cited by ~19

Preprints

  1. M. Hosseinzadeh, A. Saha, J. Bosma and H. Huisman, "Uncertainty-Aware Semi-Supervised Learning for Prostate MRI Zonal Segmentation", arXiv:2305.05984, 2023.
    Abstract DOI arXiv Cited by ~1
  2. J.S. Bosma, A. Saha, M. Hosseinzadeh, I. Slootweg, M. de Rooij and H. Huisman, "Annotation-efficient cancer detection with report-guided lesion annotation for deep learning-based prostate cancer detection in bpMRI", arXiv:2112.05151, 2021.
    Abstract DOI arXiv Cited by ~8

Papers in conference proceedings

  1. A. Saha, J.S. Bosma, J. Linmans, M. Hosseinzadeh and H. Huisman, "Anatomical and Diagnostic Bayesian Segmentation in Prostate MRI -- Should Different Clinical Objectives Mandate Different Loss Functions?", Medical Imaging Meets NeurIPS Workshop - 35th Conference on Neural Information Processing Systems (NeurIPS), 2021.
    Abstract arXiv Cited by ~6
  2. A. Saha, M. Hosseinzadeh and H. Huisman, "Encoding Clinical Priori in 3D Convolutional Neural Networks for Prostate Cancer Detection in bpMRI", Medical Imaging Meets NeurIPS Workshop - 34th Conference on Neural Information Processing Systems (NeurIPS), 2020.
    Abstract arXiv Cited by ~6
  3. M. Hosseinzadeh, P. Brand and H. Huisman, "Effect of Adding Probabilistic Zonal Prior in Deep Learning-based Prostate Cancer Detection", Medical Imaging with Deep Learning, 2019.
    Abstract Url Cited by ~12

Abstracts

  1. A. Saha, J.S. Bosma, C. Roest, M. Hosseinzadeh, J. Futterer and H. Huisman, "Deep Learning with Bayesian Inference for Prostate Cancer Diagnosis across Longitudinal Biparametric MRI", Annual Meeting of the Radiological Society of North America, 2021.
    Abstract
  2. J.S. Bosma, A. Saha, M. Hosseinzadeh and H. Huisman, "Augmenting AI with Automated Segmentation of Report Findings Applied to Prostate Cancer Detection in Biparametric MRI", Annual Meeting of the Radiological Society of North America, 2021.
    Abstract
  3. T. Riepe, M. Hosseinzadeh, P. Brand and H. Huisman, "Anisotropic Deep Learning Multi-planar Automatic Prostate Segmentation", Annual Meeting of the International Society for Magnetic Resonance in Medicine, 2020.
    Abstract

PhD theses

  1. M. Hosseinzadeh, "Prostate Cancer Detection in MRI using Deep Learning", PhD thesis, 2024.
    Abstract Url

Master theses

  1. J.S. Bosma, A. Saha, M. Hosseinzadeh and H. Huisman, "Augmenting AI with Automated Segmentation of Report Findings Applied to Prostate Cancer Detection in Biparametric MRI", Master thesis, 2021.
    Abstract Url
  2. A. Saha, M. Hosseinzadeh and H. Huisman, "Computer-Aided Detection of Clinically Significant Prostate Cancer in mpMRI", Master thesis, 2020.
    Abstract Url