ITUnet2d


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About

Creator:

User Mugshot honeyk 

Contact email:
Version:
17ac14cc-19fc-4c1b-beac-4fc2bd15a903
Last updated:
Nov. 19, 2022, 6:47 a.m.
Inputs:
  • Coronal T2 Prostate MRI  (Coronal T2 MRI of the Prostate)
  • Transverse T2 Prostate MRI  (Transverse T2 MRI of the Prostate)
  • Sagittal T2 Prostate MRI  (Sagittal T2 MRI of the Prostate)
  • Transverse HBV Prostate MRI  (Transverse High B-Value Prostate MRI)
  • Transverse ADC Prostate MRI  (Transverse Apparent Diffusion Coefficient Prostate MRI)
  • Clinical Information Prostate MRI  (Clinical information to support clinically significant prostate cancer detection in prostate MRI. Provided information: patient age in years at the time of examination (patient_age), PSA level in ng/mL as reported (PSA_report or PSA), PSA density in ng/mL^2 as reported (PSAD_report), prostate volume as reported (prostate_volume_report), prostate volume derived from automatic whole-gland segmentation (prostate_volume_automatic), scanner manufacturer (scanner_manufacturer), scanner model name (scanner_model_name), diffusion b-value of (calculated) high b-value diffusion map (diffusion_high_bvalue), Malignant Neoplasm Histotype (histology_type), Prostate Imaging-Reporting and Data System (PIRADS), Neural invasion (neural_invasion, yes/no), Vascular invasion (vascular_invasion, yes/no), Lymphatic invasion (lymphatic_invasion, yes/no). Values acquired from radiology reports will be missing, if not reported.)
Outputs:
  • Case-level Cancer Likelihood Prostate MRI  (Case-level likelihood of harboring clinically significant prostate cancer, in range [0,1].)
  • Transverse Cancer Detection Map Prostate MRI  (Single-class, detection map of clinically significant prostate cancer lesions in 3D, where each voxel represents a floating point in range [0,1].)

Challenge Performance

Date Challenge Phase Rank
Nov. 19, 2022 PI-CAI Open Development Phase - Tuning 18
Nov. 20, 2022 PI-CAI Open Development Phase - Testing 6

Model Facts

Summary

Added transformer to Unet

Mechanism

Added transformer to Unet

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