Omkar2118/datasets huggingface dataset:
Brain-Score

The Brain-Score platform aims to yield strong computational models of the ventral stream. We enable researchers to quickly get a sense of how their model scores against standardized brain benchmarks on multiple dimensions and facilitate comparisons to other state-of-the-art models. At the same time, new brain data can quickly be tested against a wide range of models to determine how well existing models explain the data.

Brain-Score is organized by the Brain-Score team in collaboration with researchers and labs worldwide. We are working towards an easy-to-use platform where a model can easily be submitted to yield its scores on a range of brain benchmarks and new benchmarks can be incorporated to challenge the models.

This quantified approach lets us keep track of how close our models are to the brain on a range of experiments (data) using different evaluation techniques (metrics). For more details, please refer to the [technical paper](https://www.biorxiv.org/content/early/2018/09/05/407007) and the [perspective paper](https://www.cell.com/neuron/fulltext/S0896-6273(20)30605-X).

Introducing paper: Brain-Score: Which Artificial Neural Network for Object Recognition is most Brain-Like?
ACDC Scribbles

We release expert-made scribble annotations for the medical ACDC dataset [1]. The released data must be considered as extending the original ACDC dataset.
The ACDC dataset contains cardiac MRI images, paired with hand-made segmentation masks. It is possible to use the segmentation masks provided in the ACDC dataset to evaluate the performance of methods trained using only scribble supervision. 

References: 
[1] Bernard, Olivier, et al. "Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: is the problem solved?." IEEE transactions on medical imaging 37.11 (2018): 2514-2525.

Introducing paper: Learning to Segment from Scribbles using Multi-scale Adversarial Attention Gates
Synthetic COVID-19 CXR Dataset

A public open dataset of synthetic chest X-ray images of COVID-19.

The dataset consists of 21,295 synthetic COVID-19 chest X-ray images. Images are generated using an unsupervised domain adaptation approach by leveraging class conditioning and adversarial training from source datasets [RSNA Kaggle Dataset](https://academictorrents.com/details/95588a735c9ae4d123f3ca408e56570409bcf2a9) and [COVID-19 Image Data Collection](https://github.com/ieee8023/covid-chestxray-dataset). 

Implementation of the algorithm is available [here](https://github.com/hasibzunair/adversarial-lesions).

Introducing paper: Synthesis of COVID-19 Chest X-rays using Unpaired Image-to-Image Translation