Back to search

Article

Pathology-informed Generative Adversarial Network Augmentation Improves Classification of Peripheral Nerve Sheath Tumors by Modeling Morphological Variability

2026-05-19

Abstract excerpt

<title>Abstract</title> <p> <bold>Background:</bold> Peripheral nerve sheath tumors (PNSTs) of the head and neck (H&N) show histopathological overlap. Although convolutional neural networks (CNNs) have demonstrated feasibility in soft tissue tumor classification, limited intra-class variability related to perineurioma remains a critical constraint for rare tumor subtypes. <bold>Methods:</bold> This retrospect...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work
f0a30e51-2261-5e74-a0ba-7a8036a3bbcf
DOI
10.21203/rs.3.rs-9596946/v1
Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest
Interactive article relationship graphSelect a related publication card to move it into the centre and load its closest explainable connections. Solid lines are source-backed structured connections. Dashed lines are semantic discovery signals and are not scientific evidence.
Pathology-informed Generative Adversarial Network Augmentation Improves Classification of Peripheral Nerve Sheath Tumors by Modeling Morphological VariabilityDOI 10.21203/rs.3.rs-9596946/v1
Select a neighboring publication to make it the new centre.