cellular composition

About

Editor:

trinhvg

Contact email:

trinhvg@korea.ac.kr

Image Version:

02b3f12c-0b06-4bcc-93f1-4c802d268376 — March 4, 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	Generic Medical Image Description - Kind Image Read from `/input/<uuid>.mha` or `/input/<uuid>.tif` Generic Medical Image	Epithelial cell count Description List of epithelial cell count predictions Kind Anything Write to `/output/epithelial-cell-count.json` Example { "key": "value", "None": null } Epithelial cell count Lymphocyte count Description List of lymphocyte cell count predictions Kind Anything Write to `/output/lymphocyte-count.json` Example { "key": "value", "None": null } Lymphocyte count Plasma cell count Description List of plasma cell count predictions Kind Anything Write to `/output/plasma-cell-count.json` Example { "key": "value", "None": null } Plasma cell count Neutrophil count Description List of neutrophil cell count predictions Kind Anything Write to `/output/neutrophil-count.json` Example { "key": "value", "None": null } Neutrophil count Eosinophil count Description List of eosinophil cell count predictions Kind Anything Write to `/output/eosinophil-count.json` Example { "key": "value", "None": null } Eosinophil count Connective tissue cell count Description List of connective tissue cell count predictions Kind Anything Write to `/output/connective-tissue-cell-count.json` Example { "key": "value", "None": null } Connective tissue cell count

Validation and Performance

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Challenge Performance

Date	Challenge	Phase	Rank
March 5, 2022	CoNIC-Challenge	Cellular Composition - Final Test	23

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