Optical coherence tomography (OCT) has stimulated a wide range of medical
image-based diagnosis and treatment in fields such as cardiology and
ophthalmology. Such applications can be further facilitated by deep
learning-based super-resolution technology, which improves the capability of
resolving morphological structures. However, existing deep learning-based
method only focuses on spatial distribution and disregard frequency fidelity in
image reconstruction, leading to a frequency bias. To overcome this limitation,
we propose a frequency-aware super-resolution framework that integrates three
critical frequency-based modules (i.e., frequency transformation, frequency
skip connection, and frequency alignment) and frequency-based loss function
into a conditional generative adversarial network (cGAN). We conducted a
large-scale quantitative study from an existing coronary OCT dataset to
demonstrate the superiority of our proposed framework over existing deep
learning frameworks. In addition, we confirmed the generalizability of our
framework by applying it to fish corneal images and rat retinal images,
demonstrating its capability to super-resolve morphological details in eye
imaging.

光学相干断层扫描（OCT）通过提高形态结构解析能力，利用深度学习技术实现了基于医学图像的诊断与治疗。为了克服现有深度学习方法在图像重建中的频率偏差问题，本文提出了一种结合频率感知超分辨率框架，通过集成三个关键的基于频率的模块（频率变换、频率跳跃连接和频率对齐）及频率损失函数到条件生成对抗网络（cGAN）的方法。通过对一个现有的冠状动脉 OCT 数据集进行大规模定量研究，证明了我们提出的框架优于现有的深度学习框架。此外，我们还将该框架应用于鱼角膜图像和大鼠视网膜图像，证明了它在眼科成像中对解析形态细节的能力。

基于条件生成对抗神经网络的频率感知光学相干层析图像超分辨率重建

Frequency-aware optical coherence tomography image super-resolution via  conditional generative adversarial neural network

The examination of blood samples at a microscopic level plays a fundamental
role in clinical diagnostics, influencing a wide range of medical conditions.
For instance, an in-depth study of White Blood Cells (WBCs), a crucial
component of our blood, is essential for diagnosing blood-related diseases such
as leukemia and anemia. While multiple datasets containing WBC images have been
proposed, they mostly focus on cell categorization, often lacking the necessary
morphological details to explain such categorizations, despite the importance
of explainable artificial intelligence (XAI) in medical domains. This paper
seeks to address this limitation by introducing comprehensive annotations for
WBC images. Through collaboration with pathologists, a thorough literature
review, and manual inspection of microscopic images, we have identified 11
morphological attributes associated with the cell and its components (nucleus,
cytoplasm, and granules). We then annotated ten thousand WBC images with these
attributes. Moreover, we conduct experiments to predict these attributes from
images, providing insights beyond basic WBC classification. As the first public
dataset to offer such extensive annotations, we also illustrate specific
applications that can benefit from our attribute annotations. Overall, our
dataset paves the way for interpreting WBC recognition models, further
advancing XAI in the fields of pathology and hematology.

本文通过引入 WBC 显微图像的综合标注，解释了 XAI 在医学领域中的重要性和对基于图像的 WBC 细胞识别技术的提升，进一步促进了病理和血液学领域的发展。