activity
20202024
most citedInterpretable and intervenable ultrasonography-based machine learning models for pediatric appendicitis

51 citations · 60 across the 3 of their papers we have counts for

collaborators

5 papers

cs.LG2024★ 1 cited

Towards Scalable Newborn Screening: Automated General Movement Assessment in Uncontrolled Settings

Daphné Chopard, Sonia Laguna, Kieran Chin-Cheong +4

General movements (GMs) are spontaneous, coordinated body movements in infants that offer valuable insights into the developing nervous system. Assessed through the Prechtl GM Asse…

cs.LG2023★ 51 cited

Interpretable and intervenable ultrasonography-based machine learning models for pediatric appendicitis

Ričards Marcinkevičs, Patricia Reis Wolfertstetter, Ugne Klimiene +9

Appendicitis is among the most frequent reasons for pediatric abdominal surgeries. Previous decision support systems for appendicitis have focused on clinical, laboratory, scoring,…

cs.LG2021

On the Limitations of Multimodal VAEs

Imant Daunhawer, Thomas M. Sutter, Kieran Chin-Cheong +2

Multimodal variational autoencoders (VAEs) have shown promise as efficient generative models for weakly-supervised data. Yet, despite their advantage of weak supervision, they exhi…

cs.LG2021

Deep Conditional Gaussian Mixture Model for Constrained Clustering

Laura Manduchi, Kieran Chin-Cheong, Holger Michel +2

Constrained clustering has gained significant attention in the field of machine learning as it can leverage prior information on a growing amount of only partially labeled data. Fo…

cs.LG2020★ 8 cited

Generation of Differentially Private Heterogeneous Electronic Health Records

Kieran Chin-Cheong, Thomas Sutter, Julia E. Vogt

Electronic Health Records (EHRs) are commonly used by the machine learning community for research on problems specifically related to health care and medicine. EHRs have the advant…