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Revealing novel insights from the relationship between molecular measurements and pathology remains a very impactful application of machine learning in biomedicine. Data in this domain typically contain only a few observations but thousands…
Robotic ultrasound has advanced local image-driven control, contact regulation, and view optimization, yet current systems lack the anatomical understanding needed to determine what to scan, where to begin, and how to adapt to individual…
eHealth systems deliver critical digital healthcare and wellness services for users by continuously monitoring physiological and contextual data. eHealth applications use multi-modal machine learning kernels to analyze data from different…
Multimodal Sentiment Analysis (MuSe) 2021 is a challenge focusing on the tasks of sentiment and emotion, as well as physiological-emotion and emotion-based stress recognition through more comprehensively integrating the audio-visual,…
Data is one of the essential ingredients to power deep learning research. Small datasets, especially specific to medical institutes, bring challenges to deep learning training stage. This work aims to develop a practical deep multimodal…
This paper describes a time-series-based classification approach to identify similarities between bio-medical-based situations. The proposed approach allows classifying collections of time-series representing bio-medical measurements, i.e.,…
EEG is a non-invasive, safe, and low-risk method to record electrophysiological signals inside the brain. Especially with recent technology developments like dry electrodes, consumer-grade EEG devices, and rapid advances in machine…
Leveraging multimodal information from biosignals is vital for building a comprehensive representation of people's physical and mental states. However, multimodal biosignals often exhibit substantial distributional shifts between…
Nowadays, the amount of heterogeneous biomedical data is increasing more and more thanks to novel sensing techniques and high-throughput technologies. In reference to biomedical image analysis, the advances in image acquisition modalities…
Traditional psychological evaluations rely heavily on human observation and interpretation, which are prone to subjectivity, bias, fatigue, and inconsistency. To address these limitations, this work presents a multimodal emotion recognition…
Accurate classification of second-trimester fetal ultrasound images remains challenging due to low image quality, high intra-class variability, and significant class imbalance. In this work, we introduce a simple yet powerful, biologically…
Recent advances in remote health monitoring systems have significantly benefited patients and played a crucial role in improving their quality of life. However, while physiological health-focused solutions have demonstrated increasing…
Emotion recognition based on Electroencephalography (EEG) has gained significant attention and diversified development in fields such as neural signal processing and affective computing. However, the unique brain anatomy of individuals…
Transcranial photobiomodulation (tPBM) therapy is an emerging, non-invasive neuromodulation technique that has demonstrated considerable potential in the field of neuropsychiatric disorders. Several studies have found that pulsed wave (PW)…
Due to the lack of large-scale datasets, the prevailing approach in visual sentiment analysis is to leverage models trained for object classification in large datasets like ImageNet. However, objects are sentiment neutral which hinders the…
Physiological signals such as electrocardiograms (ECG) and electroencephalograms (EEG) provide complementary insights into human health and cognition, yet multi-modal integration is challenging due to limited multi-modal labeled data, and…
In this paper, a non-invasive inductive link model for an Implantable Biomedical Microsystems (IBMs) such as, a pacemaker to monitor Arrhythmic Patients (APs) in Body Area Networks (BANs) is proposed. The model acts as a driving source to…
Multimodal clinical data are characterized by high dimensionality, heterogeneous representations, and structured missingness, posing significant challenges for predictive modeling, data integration, and interpretability. We propose BIONIC…
Micro-scale implantable medical devices (IMDs) extend the immense benefits of sensors used in health management. However, their development is limited by many requirements and challenges, such as the use of safe materials, size…
Objective: Tinnitus affects 10-15% of the population yet lacks objective diagnostic biomarkers. This study applied machine learning to EEG and fMRI data to identify neural signatures distinguishing tinnitus patients from healthy controls.…