In healthcare, accurately classifying medical images is vital, but
conventional methods often hinge on medical data with a consistent grid
structure, which may restrict their overall performance. Recent medical
research has been focused on tweaking the architectures to attain better
performance without giving due consideration to the representation of data. In
this paper, we present a novel approach for transforming grid based data into
its higher dimensional representations, leveraging unstructured point cloud
data structures. We first generate a sparse point cloud from an image by
integrating pixel color information as spatial coordinates. Next, we construct
a hypersurface composed of points based on the image dimensions, with each
smooth section within this hypersurface symbolizing a specific pixel location.
Polygonal face construction is achieved using an adjacency tensor. Finally, a
dense point cloud is generated by densely sampling the constructed
hypersurface, with a focus on regions of higher detail. The effectiveness of
our approach is demonstrated on a publicly accessible brain tumor dataset,
achieving significant improvements over existing classification techniques.
This methodology allows the extraction of intricate details from the original
image, opening up new possibilities for advanced image analysis and processing
tasks.

提出了一种使用点云数据结构将网格数据转换为更高维度表示的方法，其中每个平滑部分表示特定的像素位置，通过在具有更高细节的区域密集采样所构建的高曲面上获得密集的点云，该方法在公开的脑瘤数据集上实现了显着的改进，可从原始图像中提取错综复杂的细节，开辟了先进的图像分析和处理任务的新可能性。