TY  - JOUR
AB  - Longitudinal epidemiological studies often face challenges with incomplete follow-up and missing data, which can bias results and reduce statistical power. Conventional imputation methods may not adequately capture the complex patterns and dependencies in such multivariate time series data. While more recently developed generative machine learning models offer improved solutions, few methods are available which can handle inconsistently spaced intervals between measurements across long time periods and completely missing time steps, characteristics which are common in real-world studies evaluating long-term health outcomes. This paper introduces a variational autoencoder-based generative neural network designed for imputing partially and fully missing information in irregular time series with extensive missingness. Our approach exploits both correlations between features at a single time step and trends of the same feature over time to reconstruct missing values. Experiments on synthetic data designed to resemble the characteristics of longitudinal epidemiological studies and a case study on a real-world dataset demonstrate the effectiveness of our approach. We show superior performance and parameter stability across varying degrees of missingness and missingness patterns compared to prior work.
AU  - Killing, C.*
AU  - Elsbernd, K.*
AU  - Wekerle, M.*
AU  - Hoelscher, M.
AU  - Rachow, A.
AU  - Castelletti, N.*
C1  - 76110
C2  - 58400
TI  - Generative neural networks for data imputation in longitudinal epidemiological studies.
JO  - IEEE J. Biomed. Health Inform.
PY  - 2025
SN  - 2168-2194
ER  - 

TY  - JOUR
AU  - Mortazavi, B.J.*
AU  - Chiu, Y.F.*
AU  - Lü, L.*
AU  - Das, A.*
AU  - Tourassi, G.D.*
AU  - Eskofier, B.M.
C1  - 75478
C2  - 58021
SP  - 6128 - 6131
TI  - Guest editorial: Transforming healthcare and medicine with biomedical informatics and emerging AI.
JO  - IEEE J. Biomed. Health Inform.
VL  - 29
IS  - 9
PY  - 2025
SN  - 2168-2194
ER  - 

TY  - JOUR
AB  - Monitoring the propagation of mechanical cardiac signals throughout the body is crucial for assessing cardiovascular health. A common drawback of current gold standard methods for vital sign monitoring is the necessity for continuous skin contact. Radar-based sensing offers a promising alternative by enabling contactless measurement of cardiac activity, including heart sound signals. As previous research has primarily focused on deriving signals from proximal body regions, insights into heart sound propagation to peripheral areas are lacking. To address this, we systematically investigated whether radar-based heart sound detection and propagation measurement is feasible across the whole body. We recorded heart sounds in N=22 participants sequentially at eleven locations using a custom-built continuous-wave radar system and phonocardiogram as heart sound gold standard. Additionally, an electrocardiogram was acquired as reference for overall heart activity. After synchronization and preprocessing, we manually segmented the heart sounds and extracted temporal characteristics from ensemble-averaged signals. Our findings show that heart sounds can be detected across the entire body with the radar-based as well as the gold standard system. Furthermore, the heart sounds’ temporal characteristics vary between measurement locations. As the distance to the heart increases, we observed significantly increased propagation time intervals. This finding is consistent across both systems, exhibiting a strong agreement for the first heart sound (r = 0.73, p < 0.001) and a moderate agreement for the second heart sound (r = 0.56, p < 0.001). In conclusion, our work is the first to demonstrate that radar-based systems are feasible for contactless evaluation of heart sound propagation, offering new possibilities for research and health monitoring.
AU  - Oesten, M.*
AU  - Abel, L.*
AU  - Albrecht, N.C.*
AU  - Richer, R.*
AU  - Langer, D.*
AU  - Griesshammer, S.G.*
AU  - Ghanem, K.*
AU  - Steigleder, T.*
AU  - Ostgathe, C.*
AU  - Koelpin, A.*
AU  - Eskofier, B.M.
C1  - 73184
C2  - 56949
TI  - Systematic investigation of heart sound propagation using continuous wave radar.
JO  - IEEE J. Biomed. Health Inform.
PY  - 2025
SN  - 2168-2194
ER  - 

TY  - JOUR
AB  - We introduce LYSTO, the Lymphocyte Assessment Hackathon, which was held in conjunction with the MICCAI 2019 Conference in Shenzhen (China). The competition required participants to automatically assess the number of lymphocytes, in particular T-cells, in images of colon, breast, and prostate cancer stained with CD3 and CD8 immunohistochemistry. Differently from other challenges setup in medical image analysis, LYSTO participants were solely given a few hours to address this problem. In this paper, we describe the goal and the multi-phase organization of the hackathon; we describe the proposed methods and the on-site results. Additionally, we present post-competition results where we show how the presented methods perform on an independent set of lung cancer slides, which was not part of the initial competition, as well as a comparison on lymphocyte assessment between presented methods and a panel of pathologists. We show that some of the participants were capable to achieve pathologist-level performance at lymphocyte assessment. After the hackathon, LYSTO was left as a lightweight plug-and-play benchmark dataset on grand-challenge website, together with an automatic evaluation platform. LYSTO has supported a number of research in lymphocyte assessment in oncology. LYSTO will be a long-lasting educational challenge for deep learning and digital pathology, it is available at https://lysto.grand-challenge.org/.
AU  - Jiao, Y.*
AU  - van der Laak, J.*
AU  - Albarqouni, S.
AU  - Li, Z.*
AU  - Tan, T.*
AU  - Bhalerao, A.*
AU  - Cheng, S.*
AU  - Ma, J.*
AU  - Pocock, J.M.*
AU  - Pluim, J.P.W.*
AU  - Koohbanani, N.A.*
AU  - Bashir, R.M.S.*
AU  - Raza, S.E.A.*
AU  - Liu, S.*
AU  - Graham, S.E.*
AU  - Wetstein, S.*
AU  - Khurram, S.A.*
AU  - Liu, X.*
AU  - Rajpoot, N.*
AU  - Veta, M.*
AU  - Ciompi, F.*
C1  - 68803
C2  - 53721
CY  - 445 Hoes Lane, Piscataway, Nj 08855-4141 Usa
SP  - 1161-1172
TI  - LYSTO: The lymphocyte assessment hackathon and benchmark dataset.
JO  - IEEE J. Biomed. Health Inform.
VL  - 28
IS  - 3
PB  - Ieee-inst Electrical Electronics Engineers Inc
PY  - 2023
SN  - 2168-2194
ER  -