Synapse
⌘+K
Synapse
PulseExploreClubsResearchersJournals
Instagram
HomeClubsExplore
September 10, 2025Digital HealthOpen Access

Recent advances in deep learning for lymphoma segmentation: Clinical applications and challenges

View Full Paper
Ask AI
Bookmark
Share

Authors

WLWanru LiangFYFeiyang YangPTPeihong Teng

Discussion

Loading...

Member takes

Overview

This review demonstrates the efficacy of deep learning methods for lymphoma segmentation, highlighting challenges in clinical applications.

Key Points

  • Deep learning significantly improves lymphoma segmentation accuracy compared to traditional methods.
  • Advancements utilize PET/CT scans and high-quality datasets to enhance model performance in clinical settings.
  • The analysis focuses on dataset characteristics and network adjustments, aiming for better clinical integration.
  • Future efforts must address challenges like computational demands and model generalizability to aid in lymphoma diagnosis.

Cite This Study

Liang et al. (2025) studied this question.

synapsesocial.com/papers/68c1a3f954b1d3bfb60ddffchttps://doi.org/10.1177/20552076251362508
View Full Paper
Ask AI
Bookmark
Share

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Deep Learning Applications in Lymphoma Imaging2025
  2. 2Artificial intelligence-based PET/CT analysis in lymphoma: segmentation, differential diagnosis, and prognostic stratification2026
  3. 3Revolutionizing Lymphoma Diagnosis with Deep Learning and Natural Language Generation2024 · 2 citations
  4. 4Deep Dive into Bone Tumor Segmentation and Classification: Methodological Review and Challenges with Deep Learning Approaches2025
  5. 5Deep Learning Based Automated Multi-Organ Segmentation in Lymphoma Patients using Whole Body Multiparametric MRI Images2024