BoundED: Neural boundary and edge detection in 3D point clouds via local neighborhood statistics

Extracting high-level structural information from 3D point clouds is challenging but essential for tasks like urban planning or autonomous driving requiring an advanced understanding of the scene at hand. Existing approaches are still not able to produce high-quality results consistently while being fast enough to be deployed in scenarios...

Automated Classification of Well Test Responses in Naturally Fractured Reservoirs Using Unsupervised Machine Learning

Understanding the impact of fractures on fluid flow is fundamental for developing geoenergy reservoirs. Pressure transient analysis could play a key role for fracture characterization purposes if better links can be established between the pressure derivative responses (p′) and the fracture properties. However, pressure transient analysis is...

Probabilistic vegetation transitions in dunes by combining spectral and lidar data

Monitoring the status of the vegetation is required for nature conservation. This monitoring task is time consuming as kilometers of area have to be investigated and classified. To make this task more manageable, remote sensing is used. The acquisition of airplane remote sensing data is dependent on weather conditions and permission to fly in...

Classification of clinically significant prostate cancer on multi-parametric MRI: A validation study comparing deep learning and radiomics

The computer-aided analysis of prostate multiparametric MRI (mpMRI) could improve significant-prostate-cancer (PCa) detection. Various deep-learning-and radiomics-based methods for significant-PCa segmentation or classification have been reported in the literature. To be able to assess the generalizability of the performance of these methods,...

An accurate and efficient method to train classifiers for atrial fibrillation detection in ECGs: Learning by asking better questions

Background: An increasing number of wearables are capable of measuring electrocardiograms (ECGs), which may help in early detection of atrial fibrillation (AF). Therefore, many studies focus on automated detection of AF in ECGs. A major obstacle is the required amount of manually labelled data. This study aimed to provide an efficient and...

Automated classification of significant prostate cancer on MRI: A systematic review on the performance of machine learning applications

Significant prostate carcinoma (sPCa) classification based on MRI using radiomics or deep learning approaches has gained much interest, due to the potential application in assisting in clinical decision-making. Objective: To systematically review the literature (i) to determine which algorithms are most frequently used for sPCa classification...