LMM-Assisted Breast Cancer Treatment Target Segmentation with Consistency Embedding
(2311.15876)Abstract
Recent advancements in AI have profoundly influenced medical fields, by providing tools to reduce clinical workloads. However, most AI models are constrained to execute unimodal tasks, in stark contrast to the comprehensive approaches utilized by medical professionals. To address this, here we present RO-LMM, a multi-purpose large multimodal model (LMM) tailored for the field of radiation oncology. This model covers series of tasks within clinical workflow, adept at clinical report summarization, radiation treatment plan suggestion, and plan-guided target volume segmentation. In particular, to perform consecutive clinical tasks, we further present a novel Consistency Embedding Fine-Tuning (CEFTune) technique, which boosts LMM's robustness to noisy inputs while preserving the capability of handling clean inputs, and transform this concept into LMM-driven segmentation framework as Consistency Embedding Segmentation~(CESEG). Experimental results on multi-centre cohorts demonstrate our RO-LMM's promising performance for multiple clinical tasks with generalization capabilities.
Overview
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RO-LLaMA is a new AI model for enhancing clinical workflow in radiation oncology through multi-task capabilities.
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It can summarize patient histories, propose treatment plans, and delineate radiation target volumes from reports.
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The model uses Noisy Embedding Fine-Tuning (NEFTune) and Consistency Embedding Fine-Tuning (CEFTune) to improve robustness.
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In experiments, RO-LLaMA outperformed traditional methods in text summarization, treatment suggestion, and 3D volume segmentation.
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The research suggests the potential for AI models like RO-LLaMA to become fully generalist tools in medical workflows.
Introduction
AI has dramatically impacted the medical field by providing tools to assist in clinical decisions and reducing workloads. Despite that, most AI models are designed to handle single tasks with uni-modal data, which does not align well with the multifaceted nature of medical professional responsibilities. This paper introduces a novel AI model, RO-LLaMA, which operates as a generalist LLM specifically for the clinical workflow in radiation oncology.
Methodology
RO-LLaMA exhibits capabilities in three crucial areas: (1) efficiently summarizing comprehensive patient histories into concise clinical notes, (2) proposing treatment plans from a clinical expert perspective, and (3) delineating radiation target volumes directly from clinical reports. To enhance robustness against inevitable errors during sequential tasks, two pioneering techniques are introduced: Noisy Embedding Fine-Tuning (NEFTune), which injects noise into embeddings during training, and Consistency Embedding Fine-Tuning (CEFTune), which enforces prediction consistency between noisy and clean inputs. These techniques, when applied to 3D segmentation tasks, lead to Noisy Embedding Segmentation (NESEG) and Consistency Embedding Segmentation (CESEG), thereby boosting the model's generalization abilities.
Experiments and Results
A comprehensive set of experiments conducted on multi-centre cohorts established RO-LLaMA's promise. For text-related tasks like clinical report summarization and treatment plan suggestion, the model—augmented with NEFTune and CEFTune—outperformed baseline methods on both internal and external datasets. When assessing the 3D target volume segmentation task, RO-LLaMA, combined with NESEG and CESEG, advanced beyond traditional methods, validating its adeptness in multi-modal reasoning.
Discussion and Conclusion
RO-LLaMA is poised as a versatile, multifunctional tool that could revolutionize the integration of AI into routine medical workflows. It extends beyond current AI solutions, which are often constrained to uni-modal, single-task applications. This model's innovations in noise augmentation and consistency regularization may lead to the development of fully generalist medical AI models, capable of holistically grasping clinical workflows in departments such as radiation oncology.
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