论文标题
MedMnist分类十项全能：用于医学图像分析的轻量级汽车基准
MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysis
论文作者
论文摘要
我们提出了MedMnist，这是10个预处理的医疗开放数据集的集合。 MedMnist被标准化，以在轻巧的28x28图像上执行分类任务，这不需要背景知识。涵盖了医学图像分析中的主要数据方式，它的数据量表（从100到100,000）和任务（二进制/多级，序数回归和多标签）各不相同。 Medmnist可用于教育目的，快速原型，多模式机器学习或医学图像分析中的汽车。此外，MedMnist分类Decathlon旨在基准在所有10个数据集上基准的汽车算法；我们已经比较了几种基线方法，包括开源或商业汽车工具。 MedMnist的数据集，评估代码和基线方法可在https://medmnist.github.io/上公开获得。
We present MedMNIST, a collection of 10 pre-processed medical open datasets. MedMNIST is standardized to perform classification tasks on lightweight 28x28 images, which requires no background knowledge. Covering the primary data modalities in medical image analysis, it is diverse on data scale (from 100 to 100,000) and tasks (binary/multi-class, ordinal regression and multi-label). MedMNIST could be used for educational purpose, rapid prototyping, multi-modal machine learning or AutoML in medical image analysis. Moreover, MedMNIST Classification Decathlon is designed to benchmark AutoML algorithms on all 10 datasets; We have compared several baseline methods, including open-source or commercial AutoML tools. The datasets, evaluation code and baseline methods for MedMNIST are publicly available at https://medmnist.github.io/.
