修剪如何影响长尾多标签医学图像分类器?

Gregory Holste, Ziyu Jiang, Ajay Jaiswal, Maria Hanna, Shlomo Minkowitz, Alan C Legasto, Joanna G Escalon, Sharon Steinberger, Mark Bittman, Thomas C Shen, Ying Ding, Ronald M Summers, George Shih, Yifan Peng, Zhangyang Wang
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摘要

修剪已成为一种强大的压缩深度神经网络的技术,可以在不显著影响整体性能的情况下减少内存使用和推理时间。然而,修剪影响模型行为的细微方式尚不清楚,尤其是对于临床环境中常见的长尾多标签数据集。当部署修剪模型进行诊断时,这种知识差距可能会产生危险的影响,因为意外的模型行为可能会影响患者的健康。为了填补这一空白,我们首次分析了修剪对经过训练的神经网络的影响,这些神经网络用于通过胸部X射线(CXR)诊断胸部疾病。在两个大型CXR数据集上,我们检查了哪些疾病受到修剪的影响最大,并基于疾病频率和共现行为来表征类“可遗忘性”。此外,我们确定了未压缩和大量修剪模型不一致的单个CXR,称为修剪已识别样本(PIE),并进行了人类读者研究,以评估其统一性。我们发现放射科医生认为PIE具有更多的标签噪声、更低的图像质量和更高的诊断难度。这项工作代表着理解修剪对深度长尾、多标签医学图像分类中模型行为的影响的第一步。所有代码、模型权重和数据访问指令都可以在https://github.com/VITA-Group/PruneCXR.
本文章由计算机程序翻译,如有差异,请以英文原文为准。
How Does Pruning Impact Long-Tailed Multi-label Medical Image Classifiers?

Pruning has emerged as a powerful technique for compressing deep neural networks, reducing memory usage and inference time without significantly affecting overall performance. However, the nuanced ways in which pruning impacts model behavior are not well understood, particularly for long-tailed, multi-label datasets commonly found in clinical settings. This knowledge gap could have dangerous implications when deploying a pruned model for diagnosis, where unexpected model behavior could impact patient well-being. To fill this gap, we perform the first analysis of pruning's effect on neural networks trained to diagnose thorax diseases from chest X-rays (CXRs). On two large CXR datasets, we examine which diseases are most affected by pruning and characterize class "forgettability" based on disease frequency and co-occurrence behavior. Further, we identify individual CXRs where uncompressed and heavily pruned models disagree, known as pruning-identified exemplars (PIEs), and conduct a human reader study to evaluate their unifying qualities. We find that radiologists perceive PIEs as having more label noise, lower image quality, and higher diagnosis difficulty. This work represents a first step toward understanding the impact of pruning on model behavior in deep long-tailed, multi-label medical image classification. All code, model weights, and data access instructions can be found at https://github.com/VITA-Group/PruneCXR.

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