LMM-Assisted Breast Cancer Treatment Target Segmentation with Consistency Embedding

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 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.