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Found 13 papers, 9 papers with code
Unlocking the Heart Using Adaptive Locked Agnostic Networks
Supervised training of deep learning models for medical imaging applications requires a significant amount of labeled data.
On the Importance of Sign Labeling: The Hamburg Sign Language Notation System Case Study
Labeling is the cornerstone of supervised machine learning, which has been exploited in a plethora of various applications, with sign language recognition being one of them.
Towards Explainable Motion Prediction using Heterogeneous Graph Representations
In this work, we aim to improve the explainability of motion prediction systems by using different approaches.
Towards trustworthy multi-modal motion prediction: Holistic evaluation and interpretability of outputs
First, we comprehensively analyse the evaluation metrics, identify the main gaps of current benchmarks, and propose a new holistic evaluation framework.
GAN-based generative modelling for dermatological applications -- comparative study
The lack of sufficiently large open medical databases is one of the biggest challenges in AI-powered healthcare.
The (de)biasing effect of GAN-based augmentation methods on skin lesion images
In addition, we examined classification models trained on both real and synthetic data with counterfactual bias explanations.
Handling sign language transcription system with the computer-friendly numerical multilabels
This paper presents our recent developments in the automatic processing of sign language corpora using the Hamburg Sign Language Annotation System (HamNoSys).
Self-Normalized Density Map (SNDM) for Counting Microbiological Objects
The statistical properties of the density map (DM) approach to counting microbiological objects on images are studied in detail.
Generation of microbial colonies dataset with deep learning style transfer
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.
Modelling Arbitrary Complex Dielectric Properties -- an automated implementation for gprMax
There is a need to accurately simulate materials with complex electromagnetic properties when modelling Ground Penetrating Radar (GPR), as many objects encountered with GPR contain water, e. g. soils, curing concrete, and water-filled pipes.
Deep neural networks approach to microbial colony detection -- a comparative analysis
Counting microbial colonies is a fundamental task in microbiology and has many applications in numerous industry branches.
AGAR a microbial colony dataset for deep learning detection
The Annotated Germs for Automated Recognition (AGAR) dataset is an image database of microbial colonies cultured on agar plates.
Waste detection in Pomerania: non-profit project for detecting waste in environment
Our team conducted comprehensive research on Artificial Intelligence usage in waste detection and classification to fight the world's waste pollution problem.
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