性能的代价是什么？面向细胞和细胞核实例分割的可持续性感知性能指数

中文摘要 AI 辅助

用于细胞和细胞核实例分割的预训练模型在架构、预训练数据和目标、参数数量、推理策略、适配要求、后处理流程以及计算需求方面存在显著差异。大型预训练模型和基础模型因其强大的零样本能力而被日益广泛采用，但其使用也带来了更高的能耗、内存需求、计算需求、适配成本以及运行碳排放。这些额外需求是否由分割性能的有意义提升所证明，目前尚不清楚。我们通过引入可持续性感知性能指数（SAPI）来解决这一问题，该指数是一个可配置的指标，结合了分割性能、能耗和模型规模。我们在六个CellBinDB数据集上对19个预训练和基础模型进行了零样本推理基准测试，并使用冻结编码器和全模型微调两种方式，对16个可微调模型进行了少样本适配评估。我们使用基于软件的监控工具估算GPU、CPU和RAM的能耗。我们的结果表明，更大且计算需求更高的模型并不总能实现相应比例的分割质量提升。虽然少样本适配使多个模型受益，但不同架构、数据集和适配策略之间的收益和资源成本差异显著，导致基于SAPI的排名与仅基于性能的排名不同。本研究提供了一个更全面地比较分割模型的实用框架，并支持在生物医学图像分析中进行更具计算可及性和环境责任感的模型选择。

英文摘要

Pretrained models for cell and nuclear instance segmentation differ substantially in architecture, pretraining data and objectives, parameter count, inference strategy, adaptation requirements, postprocessing pipeline, and computational demand. Large pretrained and foundation models are increasingly adopted because of their strong zero-shot capabilities, but their use also imposes greater energy consumption, memory requirements, computational demands, adaptation costs, and operational carbon emissions. Whether these additional demands are justified by meaningful gains in segmentation performance remains unclear. We address this question by introducing the Sustainability-Aware Performance Index (SAPI), a configurable metric that combines segmentation performance, energy consumption, and model size. We benchmark 19 pretrained and foundation models across six CellBinDB datasets under zero-shot inference and evaluate 16 fine-tunable models using few-shot adaptation with both frozen encoder and full-model fine-tuning. We estimate energy consumption for GPU, CPU, and RAM using software-based monitoring tools. Our results show that larger and more computationally demanding models do not consistently achieve proportionate improvements in segmentation quality. While few-shot adaptation benefits several models, the gains and resource costs vary considerably across architectures, datasets, and adaptation strategies, causing SAPI-based rankings to differ from rankings based on performance alone. This study provides a practical framework for comparing segmentation models more comprehensively and supports more computationally accessible and environmentally responsible model selection in biomedical image analysis.

发表机构

• Bielefeld University of Applied Sciences and Arts（比勒费尔德应用科学与艺术大学）

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