Nevin Manimala

IEEE Trans Med Imaging. 2024 Dec 26;PP. doi: 10.1109/TMI.2024.3523319. Online ahead of print.

ABSTRACT

Learning a generalizable medical image segmentation model is an important but challenging task since the unseen (testing) domains may have significant discrepancies from seen (training) domains due to different vendors and scanning protocols. Existing segmentation methods, typically built upon domain generalization (DG), aim to learn multi-source domain-invariant features through data or feature augmentation techniques, but the resulting models either fail to characterize global domains during training or cannot sense unseen domain information during testing. To tackle these challenges, we propose a domain Unifying and Adapting network (UniAda) for generalizable medical image segmentation, a novel “unifying while training, adapting while testing” paradigm that can learn a domain-aware base model during training and dynamically adapt it to unseen target domains during testing. First, we propose to unify the multi-source domains into a global inter-source domain via a novel feature statistics update mechanism, which can sample new features for the unseen domains, facilitating the training of a domain base model. Second, we leverage the uncertainty map to guide the adaptation of the trained model for each testing sample, considering the specific target domain may be outside the global inter-source domain. Extensive experimental results on two public cross-domain medical datasets and one inhouse cross-domain dataset demonstrate the strong generalization capacity of the proposed UniAda over state-of-the-art DG methods. The source code of our UniAda is available at https://github.com/ZhouZhang233/UniAda.

PMID:40030769 | DOI:10.1109/TMI.2024.3523319

IEEE Trans Neural Netw Learn Syst. 2025 Jan 1;PP. doi: 10.1109/TNNLS.2024.3521056. Online ahead of print.

ABSTRACT

Data markets facilitate decentralized data exchange for applications such as prediction, learning, or inference. The design of these markets is challenged by varying privacy preferences and data similarity among data owners. Related works have often overlooked how data similarity impacts pricing and data value through statistical information leakage. We demonstrate that data similarity and privacy preferences are integral to market design and propose a query-response protocol using local differential privacy (LDP) for a two-party data acquisition mechanism. In our regression data market model, we analyze strategic interactions between privacy-aware owners and the learner as a Stackelberg game over the asked price and privacy factor. Finally, we numerically evaluate how data similarity affects market participation and traded data value.

PMID:40030760 | DOI:10.1109/TNNLS.2024.3521056

IEEE Trans Biomed Eng. 2024 Dec 26;PP. doi: 10.1109/TBME.2024.3512572. Online ahead of print.

ABSTRACT

Microwave imaging has been proposed for breast cancer detection and treatment monitoring. The introduction of new approaches or next-generation prototype systems requires characterization of expected variability when scanning participants over clinically relevant timeframes.

OBJECTIVE: The objective of this study is to quantify the reliability and variability in scans of a group of 35 volunteers collected at multiple time points with a next-generation microwave imaging system.

METHODS: Multiple scans of the same volunteer are collected both during one visit and over multiple visits. Test-retest reliability and variability in measurements are investigated for the average permittivity of breast tissues.

RESULTS: The average permittivity for each volunteer exhibited similarity during and between sessions, with some variation noted for smaller breast sizes. The average properties of right and left breasts were also similar. Reliability was demonstrated with the intra-class correlation coefficient (ICC) values statistically greater than 0.9 both within and between sessions. Variability of the measurement was typically less than one unit and coefficient of variation less than 5% (within sessions) or 6% (between sessions).

CONCLUSION: The microwave imaging system exhibits excellent reliability when scanning volunteers multiple times during one session and between sessions. This study represents the largest group of participants scanned at multiple time points reported to date.

SIGNIFICANCE: The excellent reliability demonstrated in this study suggests that microwave breast imaging has strong potential for capturing changes over time, such as treatment or therapy related effects, along with detecting changes in breast tissues.

PMID:40030750 | DOI:10.1109/TBME.2024.3512572

IEEE Trans Pattern Anal Mach Intell. 2024 Dec 17;PP. doi: 10.1109/TPAMI.2024.3519420. Online ahead of print.

ABSTRACT

In this article, we propose a new method of representing and analyzing music audio records. The method is based on the concept of the trajectory of fifths, which was initially developed for the analysis of music represented in MIDI format. To adapt this concept to the needs of audio signal processing, we implement a short-term spectral analysis of a musical piece, followed by a mapping of its subsequent spectral timeframes onto signatures of fifths reflecting relative intensities of sounds associated with each of the 12 pitch classes. Subsequently, the calculation of the characteristic points of the consecutive signatures of fifths enables the creation of the trajectory of fifths. The results of the experiments and statistical analysis conducted in a set of 8996 audio music pieces belonging to 10 genres indicate that this kind of trajectory, just as its MIDI-compliant precursor, is a source of valuable information (i.e., feature coefficients) concerning the harmonic structure of music, which may find use in audio music classification processes.

PMID:40030732 | DOI:10.1109/TPAMI.2024.3519420

IEEE Trans Biomed Eng. 2024 Dec 18;PP. doi: 10.1109/TBME.2024.3517635. Online ahead of print.

ABSTRACT

OBJECTIVE: Despite advancements in artificial intelligence (AI) for predicting cardiac arrest (CA) with multivariate time-series vital signs data, existing models continue to face significant problems, particularly concerning balance, efficiency, accuracy, and explainability. While neural networks have been proposed to extract multiscale features from raw data in various applications, to our knowledge, no work has utilized multiscale feature extraction, specifically for diagnostic CA prediction. This paper presents a new framework that tackles these difficulties by utilizing multiscale feature aggregation via Independent Component Analysis (ICA).

METHODS: We present the Pareto optimal StrataChron Pyramid Fusion Framework (SPFF) that improves temporal vital signs statistics by capturing long-term dependencies using multi-scale temporal feature aggregation. SPFF integrates with ICA to eliminate information redundancy and enhance model efficiency. We have developed and validated the approach using the public MIMIC IV dataset.

RESULTS: The proposed model demonstrates resilience to data imbalance and enhances explainability through SHapley Additive exPlanations (SHAP), Partial Dependence Plots (PDP), and Individual Conditional Expectation (ICE) across varying time windows. The multilayer perceptron (MLP) using SPFF and ICA achieves an accuracy of 0.982, precision of 0.969, recall of 0.989, F1-score of 0.979, and AUROC of 0.998.

CONCLUSION: The proposed method effectively predicts CA across varying time windows, offering a robust solution to the challenges of efficiency, accuracy, and explainability in current models.

SIGNIFICANCE: This method is significant to biomedical research as it provides superior performance in CA prediction while capturing both short- and long-term dependencies in patient data, potentially improving patient outcomes and guiding clinical decision-making.

PMID:40030724 | DOI:10.1109/TBME.2024.3517635

IEEE Trans Biomed Eng. 2024 Dec 13;PP. doi: 10.1109/TBME.2024.3516943. Online ahead of print.

ABSTRACT

OBJECTIVE: Understanding brain dynamics during motor tasks is a significant challenge in neuroscience, often limited to studying pairwise interactions. This study provides a comprehensive hierarchical characterization of node-specific, pairwise and higher-order interactions within the human brain’s motor network during handgrip task execution.

METHODS: The brain source activity was reconstructed from scalp EEG signals of ten healthy subjects performing a motor task, identifying five brain regions within the contralateral and ipsilateral motor networks. Using the spectral entropy rate as the basis for the decomposition of dynamic information in the alpha and beta frequency bands, we assessed the predictability of the individual rhythms within each brain region, the information shared between the activity of pairs of regions, and the higher-order interactions among groups of signals from more than two regions.

RESULTS: An overall decrease in hierarchical interactions at different orders within the motor network was observed during motor task execution. In addition to an increase in the predictability of single-source dynamics and a decrease in the strength of pairwise interactions, a statistically significant reduction in redundancy between brain sources was found. These changes primarily affected the dynamics of the alpha frequency band, driven by the well-known sensorimotor mu rhythm.

CONCLUSIONS AND SIGNIFICANCE: This work emphasizes the importance of examining hierarchically-organized brain source interactions in the frequency domain using a unified framework which fully captures the complex dynamics of the motor network.

PMID:40030679 | DOI:10.1109/TBME.2024.3516943

IEEE Trans Neural Syst Rehabil Eng. 2024 Dec 25;PP. doi: 10.1109/TNSRE.2024.3522681. Online ahead of print.

ABSTRACT

Motor-evoked potentials (MEPs) are among the few directly observable responses to external brain stimulation and serve a variety of applications, often in the form of input-output (IO) curves. Previous statistical models with two variability sources inherently consider the small MEPs at the low-side plateau as part of the neural recruitment properties. However, recent studies demonstrated that small MEP responses under resting conditions are contaminated and over-shadowed by background noise of mostly technical quality, e.g., caused by the amplifier, and suggested that the neural recruitment curve should continue below this noise level. This work intends to separate physiological variability from background noise and improve the description of recruitment behaviour. We developed a triple-variability-source model around a logarithmic logistic function without a lower plateau and incorporated an additional source for background noise. Compared to models with two or fewer variability sources, our approach better described IO characteristics, evidenced by lower Bayesian Information Criterion scores across all subjects and pulse shapes. The model independently extracted hidden variability information across the stimulated neural system and isolated it from background noise, which led to an accurate estimation of the IO curve parameters. This new model offers a robust tool to analyse brain stimulation IO curves in clinical and experimental neuroscience and reduces the risk of spurious results from inappropriate statistical methods. The presented model together with the corresponding calibration method provides a more accurate representation of MEP responses and variability sources, advances our understanding of cortical excitability, and may improve the assessment of neuromodulation effects.

PMID:40030620 | DOI:10.1109/TNSRE.2024.3522681

IEEE J Biomed Health Inform. 2024 Dec 18;PP. doi: 10.1109/JBHI.2024.3519717. Online ahead of print.

ABSTRACT

Schizophrenia (SZ) is a severe mental disorder characterized by hallucinations, delusions, cognitive impairments, and social withdrawal. It leads to a series of brain abnormalities, particularly the deformation of the hippocampus and amygdala, which are highly associated with emotion, memory, and motivation. Most previous studies have used the hippocampal and amygdaloid volume, whereas surface-based morphometry reflects nuclear deformation more finely, but it is unclear the hippocampal and amygdaloid morphometry relates to schizophrenic pathology and its potential as a biomarker. In this study, we extracted individual multivariate morphometry statistics (MMS) of hippocampus and amygdala from MRI images and analyzed the morphometric differences between groups. After dictionary learning and max pooling, we obtain reduced dimensional features and use machine learning algorithms for individual diagnosis. The results showed that the hippocampus of the schizophrenia group was significantly atrophied bilaterally and the atrophied areas were symmetrical. Subregions of the amygdala are both atrophied and expanded, and in particular, the right amygdala shows a greater degree and extent of deformation. Using the random forest classifier, the accuracy of classification using hippocampal and amygdaloid morphometric features are 94.52% and 94.57%, respectively, and the accuracy of classification combining the two morphometric features reached 96.57%. Our study demonstrates the efficacy of MMS in identifying morphometric differences of the hippocampus and amygdala between healthy controls and schizophrenic, and these findings emphasize the potential of MMS as a reliable biomarker for the diagnosis of schizophrenia.

PMID:40030599 | DOI:10.1109/JBHI.2024.3519717

IEEE J Biomed Health Inform. 2024 Dec 25;PP. doi: 10.1109/JBHI.2024.3522485. Online ahead of print.

ABSTRACT

Event-Related Potentials (ERPs) studies are powerful and widespread tools in neuroscience. The standard pipeline foresees the individuation of relevant components, and the computation of discrete features characterizing them, as latency and amplitude. Nonetheless, this approach only evaluates one aspect of the signal at a time, without considering its overall morphology; consequently being highly susceptible to low signal to noise ratio. In this context, we resort to Functional Data Analysis: a statistical methodology designed for the examination of curves and functions. Treating functions as statistical units enables the extraction of features that encompass the complete signal morphology. Functional Principal Component Analysis addresses whole ERPs as statistical units, allowing for the extraction of interpretable and comprehensive features. Exploiting this method, we compute three functional features from ERPs registered during an image categorization task. To validate our approach, firstly we examine the correlation between functional and discrete features to address the amount of overlapping information, and we consider the consistency of the obtained insights with previous literature. Moreover, we assess the effectiveness of our method by evaluating the classification performance achieved when using our extracted features to identify the object observed during the ERP recording. Such performance is compared to state-of-the-art feature extraction methods, using multiple metrics, classification algorithms, and datasets. The functional features consistently perform better, or analogously, across metrics, algorithms, and datasets they also embed additional information and provide insights coherent with previous literature, proving the usefulness of Functional Data Analysis in the context of ERP studies.

PMID:40030597 | DOI:10.1109/JBHI.2024.3522485

IEEE J Biomed Health Inform. 2024 Dec 2;PP. doi: 10.1109/JBHI.2024.3509438. Online ahead of print.

ABSTRACT

Transcranial alternating current stimulation (tACS) has been reported to treat refractory auditory hallucinations in schizophrenia. Despite diligent efforts, it is imperative to underscore that tACS does not uniformly demonstrate efficacy across all patients as with all treatments currently employed in clinical practice. The study aims to find biomarkers predicting individual responses to tACS, guiding treatment decisions, and preventing healthcare resource wastage. We divided 17 schizophrenic patients with refractory auditory hallucinations into responsive(RE) and non-responsive(NR) groups based on their auditory hallucination symptom reduction rates after one month of tACS treatment. The pre-treatment resting-state electroencephalogram(rsEEG) was recorded and then computed absolute power spectral density (PSD), Hjorth parameters (HPs, Hjorth activity (HA), Hjorth mobility (HM), and Hjorth complexity (HC) included) from different frequency bands to portray the brain oscillations. The results demonstrated that statistically significant differences localized within the high gamma frequency bands of the right brain hemisphere. Immediately, we input the significant dissociable features into popular machine learning algorithms, the Cascade Forward Neural Network achieved the best recognition accuracy of 93.87%. These findings preliminarily imply that high gamma oscillations in the right brain hemisphere may be the main influencing factor leading to different responses to tACS treatment, and incorporating rsEEG signatures could improve personalized decisions for integrating tACS in clinical treatment.

PMID:40030555 | DOI:10.1109/JBHI.2024.3509438