MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysis
By Jiancheng Yang, R. Shi, Bingbing Ni. IEEE International Symposium on Biomedical Imaging, 2020
441 citations recorded by the source corpus.
We present MedMNIST, a collection of 10 pre-processed medical open datasets. MedMNIST is standardized to perform classification tasks on lightweight 28 $\times$ 28 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/.
Subfields: Artificial Intelligence, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/57a7f546d94bbf09141a045bd9783c2f271d18ee
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Weidi Xie
- Shahriar Baradaran Shokouhi
- Ahmad Ayatollahi
- Yanfeng Wang
- Omid Nejati Manzari
- Hamid Ahmadabadi
- Ya Zhang
- Mingxia Liu
In Radiology, Nuclear Medicine and Imaging
Citations in the corpus
- MedViT: A Robust Vision Transformer for Generalized Medical Image Classification (2023, methodology): “Although there are many other metrics, to establish a fair comparison, we select ACC and AUC for the benchmarking methods reported in the original publications [63, 74].”
- Federated learning for medical image analysis: A survey (2023, background): “5) MedMNIST: MedMNIST [140] is a dataset for medical image classification.”
- MedMNIST v2 - A large-scale lightweight benchmark for 2D and 3D biomedical image classification (2021, neutral): “16, 17 Colon Pathology MC (9) 107,180 89,996 / 10,004 / 7,180 ChestMNIST Wang et al.”
- PMC-VQA: Visual Instruction Tuning for Medical Visual Question Answering (2023, methodology): “Same with MedVInT-TE, it also encodes the question input into embedding features q and can perform multi-choice or blank through different prompts.”