PolicySegNet: a policy-based reinforcement learning framework with pretrained embeddings and transformer decoder for joint brain tumors segmentation and classification in MRI

PolicySegNet: a policy-based reinforcement learning framework with pretrained embeddings and transformer decoder for joint brain tumors segmentation and classification in MRI

Abstract PolicySegNet is a novel hybrid deep learning architecture developed for joint brain tumor segmentation and classification using MRI scans. It combines a pretrained SegFormer-B4 encoder (with a MiT backbone, originally trained on the ADE20K dataset) as a fixed feature extractor with a UNet-i...

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Bibliographic Details
Main Authors: Vishv Patel, Vandana Patel, Aakash Shinde
Format: Article
Language:English
Published: SpringerOpen 2025-08-01
Series:The Egyptian Journal of Radiology and Nuclear Medicine
Subjects:
Classification
Image embeddings
MRI images
Semantic segmentation
Reinforcement learning
Vision transformer
Online Access:https://doi.org/10.1186/s43055-025-01557-3
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