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New AI model DAMM-Net++ enhances thoracic radiotherapy auto-contouring

By PulseAugur Editorial · [1 sources] ·

Researchers have developed DAMM-Net++, a novel 2.5D architecture designed to improve auto-contouring in thoracic radiotherapy. This system addresses challenges such as inter-slice surface incoherence and failure on small, low-contrast targets by incorporating an anatomy-change-aware bidirectional selective state-space memory. The model demonstrated strong performance across multiple patient cohorts and a reader study, significantly reducing contouring time and improving accuracy, with its uncertainty head providing calibrated confidence for clinical triage. AI

IMPACT This development could significantly improve the efficiency and accuracy of radiotherapy planning, potentially leading to better patient outcomes.

RANK_REASON The cluster contains a research paper detailing a new AI model for a specific medical application. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New AI model DAMM-Net++ enhances thoracic radiotherapy auto-contouring

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The cluster contains a research paper detailing a new AI model for a specific medical application. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Galib Ahmed, Istiak Ahmed, Aritra Islam Saswato, Asib Mostakim Fony, Kazi Shahriar Sanjid, Md. Tanzim Hossain, Md. Anwarul Islam, Md. Nishan Khan, Md. Misbah Khan, Labiba Faiza Karim, Jobaer Rahman, S M Hasibul Hoque, Rahnuma Shahrin Rista, Kamruzzaman R… ·

    Anatomy-Change-Aware Bidirectional Selective State-Space Memory for Clinically Deployed Thoracic Radiotherapy Auto-Contouring

    arXiv:2609.16036v1 Announce Type: cross Abstract: We developed DAMM-Net++, a 2.5D architecture for thoracic OAR and target volume segmentation that addresses three persistent challenges in radiotherapy auto-contouring: inter-slice surface incoherence, systematic failure on small …

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