We outline emerging opportunities and challenges to enhance the utility of AI for scientific discovery. The distinct goals of AI for industry versus the goals of AI for science create tension between identifying patte...
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The electroencephalogram (EEG) provides a non-invasive, minimally restrictive, and relatively low-cost measure of mesoscale brain dynamics with high temporal resolution. Although signals recorded in parallel by multip...
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The electroencephalogram (EEG) provides a non-invasive, minimally restrictive, and relatively low-cost measure of mesoscale brain dynamics with high temporal resolution. Although signals recorded in parallel by multiple, near-adjacent EEG scalp electrode channels are highly-correlated and combine signals from many different sources, biological and non-biological, independent component analysis (ICA) has been shown to isolate the various source generator processes underlying those recordings. Independent components (IC) found by ICA decomposition can be manually inspected, selected, and interpreted, but doing so requires both time and practice as ICs have no order or intrinsic interpretations and therefore require further study of their properties. Alternatively, sufficiently-accurate automated IC classifiers can be used to classify ICs into broad source categories, speeding the analysis of EEG studies with many subjects and enabling the use of ICA decomposition in near-real-time applications. While many such classifiers have been proposed recently, this work presents the ICLabel project comprised of (1) the ICLabel dataset containing spatiotemporal measures for over 200,000 ICs from more than 6,000 EEG recordings and matching component labels for over 6,000 of those ICs, all using common average reference, (2) the ICLabel website for collecting crowdsourced IC labels and educating EEG researchers and practitioners about IC interpretation, and (3) the automated ICLabel classifier, freely available for MATLAB. The ICLabel classifier improves upon existing methods in two ways: by improving the accuracy of the computed label estimates and by enhancing its computational efficiency. The classifier outperforms or performs comparably to the previous best publicly available automated IC component classification method for all measured IC categories while computing those labels ten times faster than that classifier as shown by a systematic comparison against other publicly av
Accurate segmentation and biometric analysis are essential for studying the developing fetal brain in utero. The Fetal Brain Tissue Annotation (FeTA) Challenge 2024 builds upon previous editions to further advance the...
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Accurate segmentation and biometric analysis are essential for studying the developing fetal brain in utero. The Fetal Brain Tissue Annotation (FeTA) Challenge 2024 builds upon previous editions to further advance the clinical relevance and robustness of automated fetal brain MRI analysis. This year’s challenge introduced biometry prediction as a new task complementing the usual segmentation task. The segmentation task also included a new low-field (0.55T) MRI testing set and used Euler characteristic difference (ED) as a topology-aware metric for ranking, extending the traditional overlap or distance-based measures. A total of 16 teams submitted segmentation methods for evaluation. Segmentation performance across top teams was highly consistent across both standard and low-field MRI data. Longitudinal analysis over past FeTA editions revealed minimal improvement in accuracy over time, suggesting a potential performance plateau, particularly as results now approach or surpass reported levels of inter-rater variability. However, the introduction of the ED metric revealed topological differences that were not captured by conventional metrics, underscoring its value in assessing segmentation quality. Notably, the curated low-field MRI dataset achieved the highest segmentation performance, illustrating the potential of affordable imaging systems when combined with high-quality preprocessing and reconstruction. A total of 7 teams submitted automated biometry methods for evaluation. While promising, this task exposed a critical limitation: most submitted methods failed to outperform a simple baseline that predicted measurements based solely on gestational age, without using image data. Performance varied widely across biometric measurements and between teams, indicating both current challenges and opportunities for improvement in this area. These findings highlight the need for better integration of volumetric context and stronger modeling strategies needed for the clinic
There are threefold challenges in emotion recognition. First, it is difficult to recognize human’s emotional states only considering a single modality. Second, it is expensive to manually annotate the emotional data....
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Learning of classification rules is a popular approach of machine learning, which can be achieved through two strategies, namely divide-and-conquer and separate-and-conquer. The former is aimed at generating rules in ...
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ISBN:
(纸本)9781538652152
Learning of classification rules is a popular approach of machine learning, which can be achieved through two strategies, namely divide-and-conquer and separate-and-conquer. The former is aimed at generating rules in the form of a decision tree, whereas the latter generates if-then rules directly from training data. From this point of view, the above two strategies are referred to as decision tree learning and rule learning, respectively. Both learning strategies can lead to production of complex rule based classifiers that overfit training data, which has motivated researchers to develop pruning algorithms towards reduction of overfitting. In this paper, we propose a J-measure based pruning algorithm, which is referred to as Jmean-pruning. The proposed pruning algorithm is used to advance the performance of the information entropy based rule generation method that follows the separate and conquer strategy. An experimental study is reported to show how Jmean-pruning can effectively help the above rule learning method avoid overfitting. The results show that the use of Jmean-pruning achieves to advance the performance of the rule learning method and the improved performance is very comparable or even considerably better than the one of C4.5.
Motion estimation is a basic issue for many computer vision tasks, such as human-computer interaction, motion objection detection and intelligent robot. In many practical scenes, the object movement goes with camera m...
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Motion estimation is a basic issue for many computer vision tasks, such as human-computer interaction, motion objection detection and intelligent robot. In many practical scenes, the object movement goes with camera motion. Generally, motion descriptors directly based on optical flow are inaccurate and have low discrimination power. To this end, a novel motion correction method is proposed and a novel motion feature descriptor called the motion difference histogram (MDH) for recognising human action is proposed in this study. Motion estimation results are corrected by background motion estimation and MDH encodes the motion difference between the background and the objects. Experimental results on video shot with camera motion show that the proposed motion correction method is effective and the recognition accuracy of MDH is better than that of the state-of-the-art motion descriptor.
Diffusion magnetic resonance imaging (dMRI) is currently the only tool for noninvasively imaging the brain’s white matter tracts. The fiber orientation (FO) is a key feature computed from dMRI for tract reconstructio...
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Accurate and automated real-Time building monitoring is a challenging task due to the large number of different parameters that are involved. This paper presents several aspects of a building monitoring and control sy...
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Face alignment, which fits a face model to an image and extracts the semantic meanings of facial pixels, has been an important topic in the computer vision community. However, most algorithms are designed for faces in...
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