Recently, with increasing interest in pet healthcare, the demand for
computer-aided diagnosis (CAD) systems in veterinary medicine has increased.
The development of veterinary CAD has stagnated due to a lack of sufficient
radiology data. To overcome the challenge, we propose a generative active
learning framework based on a variational autoencoder. This approach aims to
alleviate the scarcity of reliable data for CAD systems in veterinary medicine.
This study utilizes datasets comprising cardiomegaly radiograph data. After
removing annotations and standardizing images, we employed a framework for data
augmentation, which consists of a data generation phase and a query phase for
filtering the generated data. The experimental results revealed that as the
data generated through this framework was added to the training data of the
generative model, the frechet inception distance consistently decreased from
84.14 to 50.75 on the radiograph. Subsequently, when the generated data were
incorporated into the training of the classification model, the false positive
of the confusion matrix also improved from 0.16 to 0.66 on the radiograph. The
proposed framework has the potential to address the challenges of data scarcity
in medical CAD, contributing to its advancement.

利用变分自编码器提出的生成式主动学习框架解决兽医医学中计算机辅助诊断系统面临的数据缺乏挑战。通过生成数据的添加和训练模型的改进，该方法显著减少了放射照片中的 Frechet Inception 距离和混淆矩阵的假阳性，从而为医学 CAD 的进步做出了贡献。

应用变分自动编码器进行放射学数据生成的生成式主动学习在兽医学中的应用

Generative Active Learning with Variational Autoencoder for Radiology  Data Generation in Veterinary Medicine

This paper shows the machine learning system which performs instance
segmentation of cytological images in veterinary medicine. Eleven cell types
were used directly and indirectly in the experiments, including damaged and
unrecognized categories. The deep learning models employed in the system
achieve a high score of average precision and recall metrics, i.e. 0.94 and 0.8
respectively, for the selected three types of tumors. This variety of label
types allowed us to draw a meaningful conclusion that there are relatively few
mistakes for tumor cell types. Additionally, the model learned tumor cell
features well enough to avoid misclassification mistakes of one tumor type into
another. The experiments also revealed that the quality of the results improves
with the dataset size (excluding the damaged cells). It is worth noting that
all the experiments were done using a custom dedicated dataset provided by the
cooperating vet doctors.

本文介绍了一种在兽医医学中的细胞学图像实例分割的机器学习系统，利用该系统对 11 种不同类型的细胞进行了实验，并在 3 种选定的肿瘤类型上取得了高的平均精度和召回率指标，该数据集的规模也对结果产生了积极的影响。