Tumor Cellularity Assessment of Breast Histopathological Slides via Instance Segmentation and Pathomic Features Explainability

Tumor Cellularity Assessment of Breast Histopathological Slides via Instance Segmentation and Pathomic Features Explainability

The segmentation and classification of cell nuclei are pivotal steps in the pipelines for the analysis of bioimages. Deep learning (DL) approaches are leading the digital pathology field in the context of nuclei detection and classification. Nevertheless, the features that are exploited by DL models...

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Bibliographic Details
Main Authors: Nicola Altini, Emilia Puro, Maria Giovanna Taccogna, Francescomaria Marino, Simona De Summa, Concetta Saponaro, Eliseo Mattioli, Francesco Alfredo Zito, Vitoantonio Bevilacqua
Format: Article
Language:English
Published: MDPI AG 2023-03-01
Series:Bioengineering
Subjects:
nuclei detection
tumor cellularity
instance segmentation
explainable artificial intelligence
Online Access:https://www.mdpi.com/2306-5354/10/4/396

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https://www.mdpi.com/2306-5354/10/4/396

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