nnUNet semi-supervised (trained w/ external public dataset [ProstateX])

Logo for nnUNet semi-supervised (trained w/ external public dataset [ProstateX])

About

Creator:

User Mugshot krgz.ahmet1 

Contact email:
krgz.ahmet1@gmail.com
Image Version:
a4d009a4-e0a3-4421-9141-a0437b12e677
Last updated:
Oct. 15, 2022, 8:05 a.m.
Associated publications:
Cuocolo R, Comelli A, Stefano A, et al.. Deep Learning Whole‐Gland and Zonal Prostate Segmentation on a Public MRI Dataset. Magnetic Resonance Imaging. 2021;54(2):452-459.
Armato SG, Huisman H, Drukker K, et al.. PROSTATEx Challenges for computerized classification of prostate lesions from multiparametric magnetic resonance images. J Med Imag. 2018;5(04):1.

Interfaces

This algorithm implements all of the following input-output combinations:

Inputs Outputs
1
  • Coronal T2 Prostate MRI (Image)
  • Transverse T2 Prostate MRI (Image)
  • Sagittal T2 Prostate MRI (Image)
  • Transverse HBV Prostate MRI (Image)
  • Transverse ADC Prostate MRI (Image)
  • Clinical Information Prostate MRI (Anything)
  • Case-level Cancer Likelihood Prostate MRI (Float)
  • Transverse Cancer Detection Map Prostate MRI (Heat Map)

    • Challenge Performance

    Date Challenge Phase Rank
    Jan. 22, 2025 PI-CAI Open Development Phase - Tuning 36

    Model Facts

    Summary

    Additional dataset was used (ProstateX dataset).

    Mechanism

    Additional dataset was used (ProstateX dataset).

    Validation and Performance

    Uses and Directions

    This algorithm was developed for research purposes only.

    Warnings

    Common Error Messages

    Information on this algorithm has been provided by the Algorithm Editors, following the Model Facts labels guidelines from Sendak, M.P., Gao, M., Brajer, N. et al. Presenting machine learning model information to clinical end users with model facts labels. npj Digit. Med. 3, 41 (2020). 10.1038/s41746-020-0253-3