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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2601.13897 |
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| _version_ | 1866917211522727936 |
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| author | Joshi, Ankita Sharma, Ashutosh Goel, Anoushkrit Jha, Ranjeet Ranjan Ahuja, Chirag Bhavsar, Arnav Nigam, Aditya |
| author_facet | Joshi, Ankita Sharma, Ashutosh Goel, Anoushkrit Jha, Ranjeet Ranjan Ahuja, Chirag Bhavsar, Arnav Nigam, Aditya |
| contents | Tractography plays a pivotal role in the non-invasive reconstruction of white matter fiber pathways, providing vital information on brain connectivity and supporting precise neurosurgical planning. Although traditional methods relied mainly on classical deterministic and probabilistic approaches, recent progress has benefited from supervised deep learning (DL) and deep reinforcement learning (DRL) to improve tract reconstruction. A persistent challenge in tractography is accurately reconstructing white matter tracts while minimizing spurious connections. To address this, we propose TractRLFusion, a novel GPT-based policy fusion framework that integrates multiple RL policies through a data-driven fusion strategy. Our method employs a two-stage training data selection process for effective policy fusion, followed by a multi-critic fine-tuning phase to enhance robustness and generalization. Experiments on HCP, ISMRM, and TractoInferno datasets demonstrate that TractRLFusion outperforms individual RL policies as well as state-of-the-art classical and DRL methods in accuracy and anatomical reliability. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_13897 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | TractRLFusion: A GPT-Based Multi-Critic Policy Fusion Framework for Fiber Tractography Joshi, Ankita Sharma, Ashutosh Goel, Anoushkrit Jha, Ranjeet Ranjan Ahuja, Chirag Bhavsar, Arnav Nigam, Aditya Machine Learning Artificial Intelligence Tractography plays a pivotal role in the non-invasive reconstruction of white matter fiber pathways, providing vital information on brain connectivity and supporting precise neurosurgical planning. Although traditional methods relied mainly on classical deterministic and probabilistic approaches, recent progress has benefited from supervised deep learning (DL) and deep reinforcement learning (DRL) to improve tract reconstruction. A persistent challenge in tractography is accurately reconstructing white matter tracts while minimizing spurious connections. To address this, we propose TractRLFusion, a novel GPT-based policy fusion framework that integrates multiple RL policies through a data-driven fusion strategy. Our method employs a two-stage training data selection process for effective policy fusion, followed by a multi-critic fine-tuning phase to enhance robustness and generalization. Experiments on HCP, ISMRM, and TractoInferno datasets demonstrate that TractRLFusion outperforms individual RL policies as well as state-of-the-art classical and DRL methods in accuracy and anatomical reliability. |
| title | TractRLFusion: A GPT-Based Multi-Critic Policy Fusion Framework for Fiber Tractography |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2601.13897 |