We introduce MedMNIST v2, a large-scale MNIST-like dataset collection of standardized biomedical images, including 12 datasets for 2D and 6 datasets for 3D. All images are pre-processed into a small size of 28x28 (2D) or 28x28x28 (3D) with the corresponding classification labels so that no background knowledge is required for users. Covering primary data modalities in biomedical images, MedMNIST v2 is designed to perform classification on lightweight 2D and 3D images with various dataset scales (from 100 to 100,000) and diverse tasks (binary/multi-class, ordinal regression, and multi-label). The resulting dataset, consisting of 708,069 2D images and 10,214 3D images in total, could support numerous research / educational purposes in biomedical image analysis, computer vision, and machine learning. We benchmark several baseline methods on MedMNIST v2, including 2D / 3D neural networks and open-source / commercial AutoML tools. The data and code are publicly available at https://medmnist.com/.

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/.

Google's AutoML lets you train custom machine learning models without having to code Training high-performance deep networks is often a big task especially for those who have less experience in deep learning or AI. Also, we might require GPU in addition to RAM and CPU. I experienced a lot of issues while trying to classify with CNN. What if I said Google AutoML Vision will solve our problems? Yes, AutoML Vision enables us to train custom machine learning models to classify our images according to our own defined labels.

There are hundreds of blog posts on machine learning and deep learning projects, and I've learned a lot from the ones that I've read. I wanted to add to this body of knowledge by discussing a deep learning side project that I worked on recently. I've shared the project code in a Github repo. Cloud detection in satellite images is an important classification problem. It's used heavily in the field of Remote Sensing, because clouds obscure the land underneath, and too many cloudy images in a dataset make it harder for a model to learn meaningful patterns.