Incorporating Intratumoral Heterogeneity into Weakly-Supervised Deep Learning Models via Variance Pooling

Carmichael, Iain; Song, Andrew H.; Chen, Richard J.; Williamson, Drew F. K.; Chen, Tiffany; Mahmood, Faisal

doi:10.1007/978-3-031-16434-7_38

Cited by 4 publications

(2 citation statements)

References 20 publications

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“…44 Although our method has better interpretability because all analyses are traceable, deep learning applications are continuously improving and might be able to outperform the classification method presented in this article. Recent work by Carmichael et al 45 showed that incorporating a measure for intratumor heterogeneity improved the survival prediction in a deep learning model. 45 Implementing Haralick features in deep learning models might therefore be an interesting next step.…”

Section: Classification Of Histologic Characteristicsmentioning

confidence: 99%

See 1 more Smart Citation

Using Intratumor Heterogeneity of Immunohistochemistry Biomarkers to Classify Laryngeal and Hypopharyngeal Tumors Based on Histologic Features

Smits¹,

Ruiter²,

Breimer³

et al. 2023

Modern Pathology

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Section: Classification Of Histologic Characteristicsmentioning

confidence: 99%

“…Recent work by Carmichael et al 45 showed that incorporating a measure for intratumor heterogeneity improved the survival prediction in a deep learning model. 45 Implementing Haralick features in deep learning models might therefore be an interesting next step.…”

Section: Classification Of Histologic Characteristicsmentioning

confidence: 99%

Using Intratumor Heterogeneity of Immunohistochemistry Biomarkers to Classify Laryngeal and Hypopharyngeal Tumors Based on Histologic Features

Smits¹,

Ruiter²,

Breimer³

et al. 2023

Modern Pathology

View full text Add to dashboard Cite

Surformer: An interpretable pattern-perceptive survival transformer for cancer survival prediction from histopathology whole slide images

Wang

Gao

et al. 2023

Computer Methods and Programs in Biomedicine

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Applications of artificial intelligence in the analysis of histopathology images of gliomas: a review

Redlich,

Feuerhake,

Weis

et al. 2024

npj Imaging

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In recent years, the diagnosis of gliomas has become increasingly complex. Analysis of glioma histopathology images using artificial intelligence (AI) offers new opportunities to support diagnosis and outcome prediction. To give an overview of the current state of research, this review examines 83 publicly available research studies that have proposed AI-based methods for whole-slide histopathology images of human gliomas, covering the diagnostic tasks of subtyping (23/83), grading (27/83), molecular marker prediction (20/83), and survival prediction (29/83). All studies were reviewed with regard to methodological aspects as well as clinical applicability. It was found that the focus of current research is the assessment of hematoxylin and eosin-stained tissue sections of adult-type diffuse gliomas. The majority of studies (52/83) are based on the publicly available glioblastoma and low-grade glioma datasets from The Cancer Genome Atlas (TCGA) and only a few studies employed other datasets in isolation (16/83) or in addition to the TCGA datasets (15/83). Current approaches mostly rely on convolutional neural networks (63/83) for analyzing tissue at 20x magnification (35/83). A new field of research is the integration of clinical data, omics data, or magnetic resonance imaging (29/83). So far, AI-based methods have achieved promising results, but are not yet used in real clinical settings. Future work should focus on the independent validation of methods on larger, multi-site datasets with high-quality and up-to-date clinical and molecular pathology annotations to demonstrate routine applicability.

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Incorporating Intratumoral Heterogeneity into Weakly-Supervised Deep Learning Models via Variance Pooling

Cited by 4 publications

References 20 publications

Using Intratumor Heterogeneity of Immunohistochemistry Biomarkers to Classify Laryngeal and Hypopharyngeal Tumors Based on Histologic Features

Using Intratumor Heterogeneity of Immunohistochemistry Biomarkers to Classify Laryngeal and Hypopharyngeal Tumors Based on Histologic Features

Surformer: An interpretable pattern-perceptive survival transformer for cancer survival prediction from histopathology whole slide images

Applications of artificial intelligence in the analysis of histopathology images of gliomas: a review

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