English(EN) Parameter-Efficient Fine-Tuning of Foundation Models for Liver Tumor Segmentation in CT

PEFT技术增强SAM用于肝脏肿瘤分割

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-15 04:00

研究人员探索了使用Segment Anything Model (SAM)进行CT肝脏肿瘤分割的参数高效微调（PEFT）技术。该研究比较了几种PEFT方法，包括LoRA、QLoRA、Conv-Adapter以及一种新颖的方向谱Top-K适配器（DiSCo）。虽然Conv-Adapter和LoRA实现了最高的分割精度，但DiSCo在每准确度可训练参数方面表现出更高的效率。 AI

影响展示了使用PEFT在医学影像分割方面提高效率，可能降低专业AI应用的计算和数据需求。

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arXiv cs.CV TIER_1 English(EN) · Ramtin Mojtahedi, Mohammad Hamghalam, Jacob J. Peoples, Richard K. G. Do, Amber L. Simpson · 2026-09-15 04:00

用于CT肝脏肿瘤分割的基础模型的参数高效微调

arXiv:2609.14106v1 Announce Type: new Abstract: We evaluated parameter-efficient fine-tuning (PEFT) of the Segment Anything Model (SAM) for liver tumor segmentation in abdominal CT of colorectal liver metastases. We compared Low-Rank Adaptation (LoRA), 4-bit Quantized LoRA (QLoRA…

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用于CT肝脏肿瘤分割的基础模型的参数高效微调

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