ArtiDiffuser:通过反事实扩散模型的组织学图像的文物恢复和合成的统一框架
Chong Wang1, Kaili Qu2, Shuxin Li2
1College of Medical Engineering, Xinxiang Medical University, Xinxiang 453000, China; Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang 453000, China; Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang 453000, China.
Medical image analysis
|April 6, 2025
概括
组织学图像文物阻碍了深度学习的诊断. 一种新的扩散模型ArtiDiffuser可以恢复文物区域,同时保持图像完整性,并使文物合成用于数据增强.
科学领域:
- 数字病理学数字病理学
- 医学图像分析 医学图像分析
- 人工智能在组织学中的应用
背景情况:
- 组织学图像文物对深度学习诊断模型构成重大挑战,经常导致误解.
- 目前的文物修复方法,主要是生成对抗网络 (GAN),将问题视为图像对图像的翻译,但遭受模式崩和关键图像特征的改变.
- 这些局限性需要先进的技术来准确处理数字病理学的文物.
研究的目的:
- 介绍ArtiDiffuser,这是一个反事实扩散模型,旨在在组织学图像中精确地恢复文物.
- 在一个单一的框架内统一文物合成以进行数据增强和文物恢复.
- 在组织学文物存在的情况下,提高深度学习模型的稳定性和准确性.
主要方法:
- 开发了ArtiDiffuser,一种扩散模型,可以选择性地恢复艺术品扭曲的区域,同时保留未受影响的区域.
- 在ArtiDiffuser中集成文物合成功能,用于数据增强,改进模型概括.
- 采用一个Swin-Transformer无证网络,并使用以类为导向的专家混合 (MoE) 和类特定的代币来增强功能处理.
- 利用面具加权损失函数来针对性地纠正受文物影响的区域,以及下游应用的一致性规范化策略.
- 策划并发布了第一个全面的组织学数据集,其中包含723个注释补丁用于文物研究.
主要成果:
- 与传统的基于GAN的方法相比,ArtiDiffuser在文物修复和合成方面都表现出卓越的性能.
- 通过ArtiDiffuser合成和恢复的统一方法显著超过了单独的传统方法.
- 在四个不同的数据集上的评估证实了ArtiDiffuser在基于GAN的预处理和增强技术上的有效性.
- 该模型成功地保存了形态特征和染色风格,同时纠正了文物.
结论:
- ArtiDiffuser提供了一种有效和多功能解决方案,用于处理组织学图像中的文物,提高诊断准确度.
- 该模型能够统一文物修复和合成,这对现有方法来说是一个显著的进步.
- 开发的数据集和模型为未来的数字病理学和医学图像分析研究提供了宝贵的资源.
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