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Neural networks can be applied to distinguish between melanoma and non-melanoma cases in a few seconds, which makes it a great help to the diagnosing physician.

The two-stage detector for litter localization and classification is presented, namely, EfficientDet-D2 is used to localize litter, and EfficientNet-B2 to classify the detected waste into seven categories.

We compared the performance of three well-known deep learning approaches for object detection on the AGAR dataset, namely two-stage, one-stage, and transformer-based neural networks.

We introduce an effective strategy to generate an annotated synthetic dataset of microbiological images of Petri dishes that can be used to train deep learning models in a fully supervised fashion.

Create shared models together with both, or several, regions, and discover how Swedish hospitals can collaborate in a practical sense.

Using generative modelling techniques for generating synthetic data of skin diseases.

Non-profit project with the aim to use AI for Polish Sign Language.

We introduced the Annotated Germs for Automated Recognition (AGAR) dataset, an image database of microbial colonies cultured on agar plates. It contains 18000 photos of five different microorganisms as single or mixed cultures, taken under diverse lighting conditions with two different cameras.

The detect-waste team conducted comprehensive research on Artificial Intelligence usage in waste detection and classification to fight the world's waste pollution problem.

The detect-waste team conducted comprehensive research on Artificial Intelligence usage in waste detection and classification to fight the world's waste pollution problem.