First Densenet Team 1

About

Editor:

bobs

Contact email:

bob.sanders@ru.nl

Image Version:

0f15b0fd-a710-4b28-b4d8-d73dcb327c74 — June 12, 2022

Summary

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Mechanism

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Interfaces

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

Inputs Outputs

	Inputs	Outputs
1	CT Image Description Any CT image Kind Image Read from `/input/images/ct/<uuid>.mha` or `/input/images/ct/<uuid>.tif` CT Image	Lung nodule malignancy risk Description Malignancy risk of lung nodule detected in a chest CT ranging between 0 to 1 with 0 being the lowest malignancy risk and 1 being the highest malignancy risk Kind Float Write to `/output/lung-nodule-malignancy-risk.json` Example 42.0 Lung nodule malignancy risk Lung nodule type Description The sub-type / texture of a lung nodule detected in a chest CT. Labels follow this mapping: 0: "non-solid", 1: "part-solid", 2: "solid". Kind Integer Write to `/output/lung-nodule-type.json` Example 42 Lung nodule type

Validation and Performance

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Uses and Directions

This algorithm was developed for research purposes only.

Warnings

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Common Error Messages

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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