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PEFT techniques enhance SAM for liver tumor segmentation

By PulseAugur Editorial · [1 sources] ·

Researchers have explored parameter-efficient fine-tuning (PEFT) techniques for segmenting liver tumors in CT scans using the Segment Anything Model (SAM). The study compared several PEFT methods, including LoRA, QLoRA, Conv-Adapter, and a novel Directional Spectral Top-K adapter (DiSCo). While Conv-Adapter and LoRA achieved the highest segmentation accuracy, DiSCo demonstrated superior efficiency in terms of trainable parameters per accuracy. AI

IMPACT Demonstrates efficiency gains in medical imaging segmentation using PEFT, potentially lowering compute and data requirements for specialized AI applications.

RANK_REASON Academic paper detailing a novel method and evaluation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

PEFT techniques enhance SAM for liver tumor segmentation

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Academic paper detailing a novel method and evaluation. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Ramtin Mojtahedi, Mohammad Hamghalam, Jacob J. Peoples, Richard K. G. Do, Amber L. Simpson ·

    Parameter-Efficient Fine-Tuning of Foundation Models for Liver Tumor Segmentation in CT

    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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