Home › Entities › academia › Interpretable deep learning model to predict the molecular classification of endometrial cancer from haematoxylin and eosin-stained whole-slide images: a combined analysis of the PORTEC randomised trials and clinical cohorts

Interpretable deep learning model to predict the molecular classification of endometrial cancer from haematoxylin and eosin-stained whole-slide images: a combined analysis of the PORTEC randomised trials and clinical cohorts

Research article (The Lancet Digital Health, 2022) · cited 118× · AI/ML
academia openalex:W4310828474
Press Enter · cited answer in seconds

Interpretable deep learning model to predict the molecular classification of endometrial cancer from haematoxylin and eosin-stained whole-slide images: a combined analysis of the PORTEC randomised trials and clinical cohorts

Summary

Interpretable deep learning model to predict the molecular classification of endometrial cancer from haematoxylin and eosin-stained whole-slide images: a combined analysis of the PORTEC randomised trials and clinical cohorts is a scholarly article[1].

Key Facts

  • Interpretable deep learning model to predict the molecular classification of endometrial cancer from haematoxylin and eosin-stained whole-slide images: a combined analysis of the PORTEC randomised trials and clinical cohorts's instance of is recorded as scholarly article[2].

References

Programmatic citations — every numbered marker resolves to a verifiable graph row below.

Direct Wikidata claims

  1. [2] ↑ . wikidata.org.

Class ancestry

  1. [1] ↑ . Wikidata. wikidata.org.

📑 Cite this page

Use these citations when quoting this entity in research, articles, AI prompts, or wherever provenance matters. We aggregate Wikidata + Wikipedia + authoritative open-data sources; the stitched, scored, cross-referenced view is what 4ort.xyz contributes.

APA 4ort.xyz Knowledge Graph. (2026). Interpretable deep learning model to predict the molecular classification of endometrial cancer from haematoxylin and eosin-stained whole-slide images: a combined analysis of the PORTEC randomised trials and clinical cohorts. Retrieved May 24, 2026, from https://4ort.xyz/entity/interpretable-deep-learning-model-to-predict-the-molecular-classification-of-endometrial-cancer-from-haematoxylin-and-eo
MLA “Interpretable deep learning model to predict the molecular classification of endometrial cancer from haematoxylin and eosin-stained whole-slide images: a combined analysis of the PORTEC randomised trials and clinical cohorts.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/interpretable-deep-learning-model-to-predict-the-molecular-classification-of-endometrial-cancer-from-haematoxylin-and-eo.
BibTeX @misc{4ortxyz_interpretable-deep-learning-model-to-predict-the-molecular-classification-of-endometrial-cancer-from-haematoxylin-and-eo_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Interpretable deep learning model to predict the molecular classification of endometrial cancer from haematoxylin and eosin-stained whole-slide images: a combined analysis of the PORTEC randomised trials and clinical cohorts}}, year = {2026}, url = {https://4ort.xyz/entity/interpretable-deep-learning-model-to-predict-the-molecular-classification-of-endometrial-cancer-from-haematoxylin-and-eo}, note = {Accessed: 2026-05-24}}
LLM prompt According to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Interpretable deep learning model to predict the molecular classification of endometrial cancer from haematoxylin and eosin-stained whole-slide images: a combined analysis of the PORTEC randomised trials and clinical cohorts — https://4ort.xyz/entity/interpretable-deep-learning-model-to-predict-the-molecular-classification-of-endometrial-cancer-from-haematoxylin-and-eo (retrieved 2026-05-24)

Canonical URL: https://4ort.xyz/entity/interpretable-deep-learning-model-to-predict-the-molecular-classification-of-endometrial-cancer-from-haematoxylin-and-eo · Last refreshed: