Quantitative feature extraction pipeline on 2 sample images, which segments the image in a stepwise manner.

<p>First, the image is segmented into carcinoma (green), stroma (light blue), mucin (dark blue), TB/PDC (red), necrosis (brown), smooth muscle (purple, and fat (yellow). Next, the stroma is segmented into immature (teal), mature (green), and inflammatory (gray). The carcinoma is segmented into...

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Bibliographic Details
Main Author:	Ramón L. Correa-Medero (20314598) (author)
Other Authors:	Rish Pai (13894442) (author), Kingsley Ebare (20314601) (author), Daniel D. Buchanan (9101491) (author), Mark A. Jenkins (9292697) (author), Amanda I. Phipps (6870275) (author), Polly A. Newcomb (9335463) (author), Steven Gallinger (108153) (author), Robert Grant (74834) (author), Loic Le marchand (20314604) (author), Imon Banerjee (6307898) (author)
Published:	2024
Subjects:	Ecology Cancer Science Policy Biological Sciences not elsewhere classified opened new possibility latent probability adjustment extremely challenging problem causal reasoning framework histopathology &# 8212 causal debiasing solution div >< p ai model performance ai model generalizibility causal debiasing histopathology domain ai model ai models model training deepsurvival model unknown </ source codebase saves hours primarily due predict recurrence novel addition free survival external sites expert effort demonstrated benefit data acquisition clinical application auxiliary losses also makes ablation study
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Quantitative feature extraction pipeline on 2 sample images, which segments the image in a stepwise manner.

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