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Matin Hosseinzadeh

matinhz

  •  Netherlands
  •  Radboud University Medical Center
  •  Diagnostic Image Analysis Group
  •  Website
Statistics
  • Member for 7 years, 10 months
  • 21 algorithms run

Activity Overview

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PROMISE12
Challenge User

The goal of this challenge is to compare interactive and (semi)-automatic segmentation algorithms for MRI of the prostate.

ICIAR2018-Challenge Logo
ICIAR 2018
Challenge User

Can you develop a method for automatic detection of cancerous regions in breast cancer histology images?

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ROCC
Challenge User

Retinal OCT Classification Challenge (ROCC) is organized as a one day Challenge in conjunction with MVIP2017. The goal of this challenge is to call different automated algorithms that are able to detect DR disease from normal retina on a common dataset of OCT volumes, acquired with Topcon SD-OCT devices.

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IDRiD
Challenge User

This challenge evaluates automated techniques for analysis of fundus photographs. We target segmentation of retinal lesions like exudates, microaneurysms, and hemorrhages and detection of the optic disc and fovea. Also, we seek grading of fundus images according to the severity level of DR and DME.

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PROSTATEx
Challenge Editor

Classification of clinical significance of prostate lesions using multi-parametric MRI data

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QUBIQ
Challenge User

Quantification of Uncertainties in Biomedical Image Segmentation Challenge

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QUBIQ2021
Challenge User

Quantification of Uncertainties in Biomedical Image Segmentation Challenge 2021

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The PI-CAI Challenge
Challenge User

Artificial Intelligence and Radiologists at Prostate Cancer Detection in MRI

Clinically Significant Prostate Cancer Detection in bpMRI Logo
Clinically Significant Prostate Cancer Detection in bpMRI
Algorithm Editor

Deep learning-based 3D detection/diagnosis model trained, validated and tested using 2732 prostate biparametric MRI exams from two centers.