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Computer Science > Computer Vision and Pattern Recognition

arXiv:2311.15876 (cs)
[Submitted on 27 Nov 2023]

Title:RO-LLaMA: Generalist LLM for Radiation Oncology via Noise
Augmentation and Consistency Regularization

Authors:Kwanyoung Kim, Yujin Oh, Sangjoon Park, Hwa Kyung Byun, Jin
Sung Kim, Yong Bae Kim, Jong Chul Ye
Download a PDF of the paper titled RO-LLaMA: Generalist LLM for
Radiation Oncology via Noise Augmentation and Consistency
Regularization, by Kwanyoung Kim and 6 other authors
Download PDF

    Abstract:Recent advancements in Artificial Intelligence (AI) have
    profoundly influenced medical fields, by providing tools to
    reduce clinical workloads. However, most AI models are
    constrained to execute uni-modal tasks, in stark contrast to the
    comprehensive approaches utilized by medical professionals. To
    address this, here we present RO-LLaMA, a versatile generalist
    large language model (LLM) tailored for the field of radiation
    oncology. This model seamlessly covers a wide range of the
    workflow of radiation oncologists, adept at various tasks such as
    clinical report summarization, radiation therapy plan suggestion,
    and plan-guided therapy target volume segmentation. In
    particular, to maximize the end-to-end performance, we further
    present a novel Consistency Embedding Fine-Tuning (CEFTune)
    technique, which boosts LLM's robustness to additional errors at
    the intermediates while preserving the capability of handling
    clean inputs, and creatively transform this concept into
    LLM-driven segmentation framework as Consistency Embedding
    Segmentation (CESEG). Experimental results on multi-centre cohort
    sets demonstrate our proposed RO-LLaMA's promising performance
    for diverse tasks with generalization capabilities.

Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial
          Intelligence (cs.AI); Machine Learning (cs.LG)
Cite as:  arXiv:2311.15876 [cs.CV]
          (or arXiv:2311.15876v1 [cs.CV] for this version)
          https://doi.org/10.48550/arXiv.2311.15876
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Jong Chul Ye [view email]
[v1] Mon, 27 Nov 2023 14:49:06 UTC (8,625 KB)
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    Regularization, by Kwanyoung Kim and 6 other authors
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