Pathologic myopia (PM) is a common blinding retinal degeneration suffered by
highly myopic population. Early screening of this condition can reduce the
damage caused by the associated fundus lesions and therefore prevent vision
loss. Automated diagnostic tools based on artificial intelligence methods can
benefit this process by aiding clinicians to identify disease signs or to
screen mass populations using color fundus photographs as inputs. This paper
provides insights about PALM, our open fundus imaging dataset for pathological
myopia recognition and anatomical structure annotation. Our databases comprises
1200 images with associated labels for the pathologic myopia category and
manual annotations of the optic disc, the position of the fovea and
delineations of lesions such as patchy retinal atrophy (including peripapillary
atrophy) and retinal detachment. In addition, this paper elaborates on other
details such as the labeling process used to construct the database, the
quality and characteristics of the samples and provides other relevant usage
notes.

该研究提供了有关用于识别 pathologic myopia 的开放式眼底图像数据集 PALM 的信息，包括与其相关的结构注释，为该病的早期筛查以及基于人工智能的自动化诊断工具的开发提供了可能。

PALM：开放式黄斑图像数据集，包含病理性近视识别和解剖结构标注

PALM: Open Fundus Photograph Dataset with Pathologic Myopia Recognition  and Anatomical Structure Annotation

Brain pathologies can vary greatly in size and shape, ranging from few pixels
(i.e. MS lesions) to large, space-occupying tumors. Recently proposed
Autoencoder-based methods for unsupervised anomaly segmentation in brain MRI
have shown promising performance, but face difficulties in modeling
distributions with high fidelity, which is crucial for accurate delineation of
particularly small lesions. Here, similar to these previous works, we model the
distribution of healthy brain MRI to localize pathologies from erroneous
reconstructions. However, to achieve improved reconstruction fidelity at higher
resolutions, we learn to compress and reconstruct different frequency bands of
healthy brain MRI using the laplacian pyramid. In a range of experiments
comparing our method to different State-of-the-Art approaches on three
different brain MR datasets with MS lesions and tumors, we show improved
anomaly segmentation performance and the general capability to obtain much more
crisp reconstructions of input data at native resolution. The modeling of the
laplacian pyramid further enables the delineation and aggregation of lesions at
multiple scales, which allows to effectively cope with different pathologies
and lesion sizes using a single model.

使用拉普拉斯金字塔对健康脑 MRI 的不同频带进行压缩和重建来提高重构保真度，实现对大、小脑病变的定位和聚合，并且表现出优异的异常分割效果。