用解耦表征提升显微图像分类的可解释性。
Disentangled representations of microscopy images
- 基于合成数据预训练，学习解耦的图像表征。
- 在三个显微图像数据集上实现准确与可解释性的平衡。
- 适合需要模型透明性的生物医学分析场景。
显微图像分析在诊断、合成工程和环境监测等应用中至关重要。现代成像系统产生了大量图像，推动了基于深度学习的自动分析方法的发展。尽管深度神经网络在此领域表现优异，但可解释性这一关键需求仍面临挑战。本文提出一种解耦表征学习（DRL）方法，以增强显微图像分类的可解释性。利用来自三个不同显微图像领域（浮游生物、酵母液泡、人细胞）的基准数据集，我们证明：基于合成数据学习的表征迁移，可在该领域实现准确率与可解释性之间的良好权衡。
原文摘要 · Abstract (English)
Microscopy image analysis is fundamental for different applications, from diagnosis to synthetic engineering and environmental monitoring. Modern acquisition systems have granted the possibility to acquire an escalating amount of images, requiring a consequent development of a large collection of deep learning-based automatic image analysis methods. Although deep neural networks have demonstrated great performance in this field, interpretability, an essential requirement for microscopy image analysis, remains an open challenge. This work proposes a Disentangled Representation Learning (DRL) methodology to enhance model interpretability for microscopy image classification. Exploiting benchmark datasets from three different microscopic image domains (plankton, yeast vacuoles, and human cells), we show how a DRL framework, based on transferring a representation learnt from synthetic data, can provide a good trade-off between accuracy and interpretability in this domain.
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