End-to-End Breast Cancer Radiotherapy Planning via LMMs with Consistency Embedding
- URL: http://arxiv.org/abs/2311.15876v3
- Date: Mon, 1 Jul 2024 20:51:59 GMT
- Title: End-to-End Breast Cancer Radiotherapy Planning via LMMs with Consistency Embedding
- Authors: Kwanyoung Kim, Yujin Oh, Sangjoon Park, Hwa Kyung Byun, Joongyo Lee, Jin Sung Kim, Yong Bae Kim, Jong Chul Ye,
- Abstract summary: We present RO-LMM, a comprehensive large multimodal model (LMM) tailored for the field of radiation oncology.
This model effectively manages a series of tasks within the clinical workflow, including clinical context summarization, radiation treatment plan suggestion, and plan-guided target volume segmentation.
We present a novel Consistency Embedding Fine-Tuning (CEFTune) technique, which boosts LMM's robustness to noisy inputs while preserving the consistency of handling clean inputs.
- Score: 47.360760580820966
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Recent advances in AI foundation models have significant potential for lightening the clinical workload by mimicking the comprehensive and multi-faceted approaches used by medical professionals. In the field of radiation oncology, the integration of multiple modalities holds great importance, so the opportunity of foundational model is abundant. Inspired by this, here we present RO-LMM, a multi-purpose, comprehensive large multimodal model (LMM) tailored for the field of radiation oncology. This model effectively manages a series of tasks within the clinical workflow, including clinical context summarization, radiation treatment plan suggestion, and plan-guided target volume segmentation by leveraging the capabilities of LMM. In particular, to perform consecutive clinical tasks without error accumulation, we present a novel Consistency Embedding Fine-Tuning (CEFTune) technique, which boosts LMM's robustness to noisy inputs while preserving the consistency of handling clean inputs. We further extend this concept to LMM-driven segmentation framework, leading to a novel Consistency Embedding Segmentation~(CESEG) techniques. Experimental results including multi-centre validation confirm that our RO-LMM with CEFTune and CESEG results in promising performance for multiple clinical tasks with generalization capabilities.
Related papers
- EMERGE: Integrating RAG for Improved Multimodal EHR Predictive Modeling [22.94521527609479]
EMERGE is a Retrieval-Augmented Generation driven framework aimed at enhancing multimodal EHR predictive modeling.
Our approach extracts entities from both time-series data and clinical notes by prompting Large Language Models.
The extracted knowledge is then used to generate task-relevant summaries of patients' health statuses.
arXiv Detail & Related papers (2024-05-27T10:53:15Z) - Med-MoE: Mixture of Domain-Specific Experts for Lightweight Medical Vision-Language Models [17.643421997037514]
We propose a novel framework that tackles both discriminative and generative multimodal medical tasks.
The learning of Med-MoE consists of three steps: multimodal medical alignment, instruction tuning and routing, and domain-specific MoE tuning.
Our model can achieve performance superior to or on par with state-of-the-art baselines.
arXiv Detail & Related papers (2024-04-16T02:35:17Z) - Intuition-aware Mixture-of-Rank-1-Experts for Parameter Efficient Finetuning [50.73666458313015]
Large Language Models (LLMs) have demonstrated significant potential in performing multiple tasks in multimedia applications.
MoE has been emerged as a promising solution with its sparse architecture for effective task decoupling.
Intuition-MoR1E achieves superior efficiency and 2.15% overall accuracy improvement across 14 public datasets.
arXiv Detail & Related papers (2024-04-13T12:14:58Z) - RJUA-MedDQA: A Multimodal Benchmark for Medical Document Question
Answering and Clinical Reasoning [14.366349078707263]
RJUA-MedDQA is a comprehensive benchmark in the field of medical specialization.
This work introduces RJUA-MedDQA, a comprehensive benchmark in the field of medical specialization.
arXiv Detail & Related papers (2024-02-19T06:57:02Z) - AI Hospital: Benchmarking Large Language Models in a Multi-agent Medical Interaction Simulator [69.51568871044454]
We introduce textbfAI Hospital, a framework simulating dynamic medical interactions between emphDoctor as player and NPCs.
This setup allows for realistic assessments of LLMs in clinical scenarios.
We develop the Multi-View Medical Evaluation benchmark, utilizing high-quality Chinese medical records and NPCs.
arXiv Detail & Related papers (2024-02-15T06:46:48Z) - REALM: RAG-Driven Enhancement of Multimodal Electronic Health Records
Analysis via Large Language Models [19.62552013839689]
Existing models often lack the medical context relevent to clinical tasks, prompting the incorporation of external knowledge.
We propose REALM, a Retrieval-Augmented Generation (RAG) driven framework to enhance multimodal EHR representations.
Our experiments on MIMIC-III mortality and readmission tasks showcase the superior performance of our REALM framework over baselines.
arXiv Detail & Related papers (2024-02-10T18:27:28Z) - XAI for In-hospital Mortality Prediction via Multimodal ICU Data [57.73357047856416]
We propose an efficient, explainable AI solution for predicting in-hospital mortality via multimodal ICU data.
We employ multimodal learning in our framework, which can receive heterogeneous inputs from clinical data and make decisions.
Our framework can be easily transferred to other clinical tasks, which facilitates the discovery of crucial factors in healthcare research.
arXiv Detail & Related papers (2023-12-29T14:28:04Z) - LLM-driven Multimodal Target Volume Contouring in Radiation Oncology [46.23891509553877]
Large language models (LLMs) can facilitate the integration of the textural information and images.
We present a novel LLM-driven multimodal AI, namely LLMSeg, that is applicable to the challenging task of target volume contouring for radiation therapy.
We demonstrate that the proposed model exhibits markedly improved performance compared to conventional unimodal AI models.
arXiv Detail & Related papers (2023-11-03T13:38:42Z) - When MOE Meets LLMs: Parameter Efficient Fine-tuning for Multi-task Medical Applications [57.342772288710044]
We propose a novel parameter efficient fine-tuning framework for multi-task medical applications, dubbed as MOELoRA.
For unifying MOE and LoRA, we devise multiple experts as the trainable parameters, where each expert consists of a pair of low-rank matrices to retain the small size of trainable parameters.
We conduct experiments on a multi-task medical dataset, indicating MOELoRA outperforms the existing parameter efficient fine-tuning methods.
arXiv Detail & Related papers (2023-10-21T17:18:09Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.