BrainTumNet: multi-task deep learning framework for brain tumor segmentation and classification using adaptive masked transformers

BrainTumNet: multi-task deep learning framework for brain tumor segmentation and classification using adaptive masked transformers

Background and objectiveAccurate diagnosis of brain tumors significantly impacts patient prognosis and treatment planning. Traditional diagnostic methods primarily rely on clinicians’ subjective interpretation of medical images, which is heavily dependent on physician experience and limited by time...

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Bibliographic Details
Main Authors: Cheng Lv, Xu-Jun Shu, Quan Liang, Jun Qiu, Zi-Cheng Xiong, Jing bo Ye, Shang bo Li, Cheng Qing Liu, Jing Zhen Niu, Sheng-Bo Chen, Hong Rao
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-05-01
Series:Frontiers in Oncology
Subjects:
brain tumor diagnosis
deep learning
multi-task learning
medical image analysis
Convolutional Neural Networks
Online Access:https://www.frontiersin.org/articles/10.3389/fonc.2025.1585891/full
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https://www.frontiersin.org/articles/10.3389/fonc.2025.1585891/full

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