Intelligent Data Engineering and Automated Learning – IDEAL 2023
24th International Conference, Évora, Portugal, November 22–24, 2023, Proceedings
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57 Result(s)
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Chapter and Conference Paper
Efficient NAS with FaDE on Hierarchical Spaces
Neural architecture search (NAS) is a challenging problem. Hierarchical search spaces allow for cheap evaluations of neural network sub modules to serve as surrogate for architecture evaluations. Yet, sometime...
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Chapter and Conference Paper
Identifying Relevant Factors of Requirements Quality: An Industrial Case Study
[Context and Motivation]: The quality of requirements specifications impacts subsequent, dependent software engineering activities. Requirements quality defects like ambiguous statements can result in incomplete ...
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Chapter and Conference Paper
ICoNIK: Generating Respiratory-Resolved Abdominal MR Reconstructions Using Neural Implicit Representations in k-Space
Motion-resolved reconstruction for abdominal magnetic resonance imaging (MRI) remains a challenge due to the trade-off between residual motion blurring caused by discretized motion states and undersampling art...
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Chapter and Conference Paper
Reveal to Revise: An Explainable AI Life Cycle for Iterative Bias Correction of Deep Models
State-of-the-art machine learning models often learn spurious correlations embedded in the training data. This poses risks when deploying these models for high-stake decision-making, such as in medical applica...
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Chapter and Conference Paper
NeuroExplainer: Fine-Grained Attention Decoding to Uncover Cortical Development Patterns of Preterm Infants
In addition to model accuracy, current neuroimaging studies require more explainable model outputs to relate brain development, degeneration, or disorders to uncover atypical local alterations. For this purpos...
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Chapter and Conference Paper
MixUp-MIL: Novel Data Augmentation for Multiple Instance Learning and a Study on Thyroid Cancer Diagnosis
Multiple instance learning is a powerful approach for whole slide image-based diagnosis in the absence of pixel- or patch-level annotations. In spite of the huge size of whole slide images, the number of indiv...
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Book and Conference Proceedings
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Chapter and Conference Paper
LSOR: Longitudinally-Consistent Self-Organized Representation Learning
Interpretability is a key issue when applying deep learning models to longitudinal brain MRIs. One way to address this issue is by visualizing the high-dimensional latent spaces generated by deep learning via ...
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Chapter and Conference Paper
Multimodal Human Pose Feature Fusion for Gait Recognition
Gait recognition allows identifying people at a distance based on the way they walk (i.e. gait) in a non-invasive approach. Most of the approaches published in the last decades are dominated by the use of silhoue...
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Chapter and Conference Paper
An Explainable Geometric-Weighted Graph Attention Network for Identifying Functional Networks Associated with Gait Impairment
One of the hallmark symptoms of Parkinson’s Disease (PD) is the progressive loss of postural reflexes, which eventually leads to gait difficulties and balance problems. Identifying disruptions in brain functio...
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Chapter and Conference Paper
Power Efficient Video Super-Resolution on Mobile NPUs with Deep Learning, Mobile AI & AIM 2022 Challenge: Report
Video super-resolution is one of the most popular tasks on mobile devices, being widely used for an automatic improvement of low-bitrate and low-resolution video streams. While numerous solutions have been pro...
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Chapter and Conference Paper
Acceptance of Mobile Payment: A Cross-Cultural Examination Between Mainland China, Taiwan, and Germany
This research paper examines the influence of cross-culture
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Chapter and Conference Paper
NISF: Neural Implicit Segmentation Functions
Segmentation of anatomical shapes from medical images has taken an important role in the automation of clinical measurements. While typical deep-learning segmentation approaches are performed on discrete voxel...
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Chapter and Conference Paper
Punctate White Matter Lesion Segmentation in Preterm Infants Powered by Counterfactually Generative Learning
Accurate segmentation of punctate white matter lesions (PWMLs) are fundamental for the timely diagnosis and treatment of related developmental disorders. Automated PWMLs segmentation from infant brain MR image...
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Chapter and Conference Paper
COVID Detection and Severity Prediction with 3D-ConvNeXt and Custom Pretrainings
Since COVID strongly affects the respiratory system, lung CT-scans can be used for the analysis of a patients health. We introduce a neural network for the prediction of the severity of lung damage and the det...
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Chapter and Conference Paper
Recent Applications of Pre-aggregation Functions
In recent years, a hot topic that has emerged is the concept of pre-aggregation functions. This kind of function respects the same property as an aggregation function, however, with a directional increase. Tak...
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Chapter and Conference Paper
EndoSurf: Neural Surface Reconstruction of Deformable Tissues with Stereo Endoscope Videos
Reconstructing soft tissues from stereo endoscope videos is an essential prerequisite for many medical applications. Previous methods struggle to produce high-quality geometry and appearance due to their inade...
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Chapter and Conference Paper
LCMV: Lightweight Classification Module for Video Domain Adaptation
Video action recognition models exhibit high performance on in-distribution data but struggle with distribution shifts in test data. To mitigate this issue, Unsupervised Domain Adaptation (UDA) methods have be...
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Chapter and Conference Paper
One-Shot Federated Learning on Medical Data Using Knowledge Distillation with Image Synthesis and Client Model Adaptation
One-shot federated learning (FL) has emerged as a promising solution in scenarios where multiple communication rounds are not practical. Notably, as feature distributions in medical data are less discriminativ...
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Chapter and Conference Paper
Towards Multi-modal Anatomical Landmark Detection for Ultrasound-Guided Brain Tumor Resection with Contrastive Learning
Homologous anatomical landmarks between medical scans are instrumental in quantitative assessment of image registration quality in various clinical applications, such as MRI-ultrasound registration for tissue ...
