Journal: Artificial intelligence in medicine

Abbreviation

Artif Intell Med

Publisher

Elsevier

Journal Volumes

ISSN

0933-3657

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2008 - 200922020 - 20232
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Publications 1 - 4 of 4
  • Neural collaborative filtering for unsupervised mitral valve segmentation in echocardiography
    Item type: Journal Article
    Corinzia, Luca; Laumer, Fabian; Candreva, Alessandro; et al. (2020)
    Artificial intelligence in medicine
    The segmentation of the mitral valve annulus and leaflets specifies a crucial first step to establish a machine learning pipeline that can support physicians in performing multiple tasks, e.g. diagnosis of mitral valve diseases, surgical planning, and intraoperative procedures. Current methods for mitral valve segmentation on 2D echocardiography videos require extensive interaction with annotators and perform poorly on low-quality and noisy videos. We propose an automated and unsupervised method for the mitral valve segmentation based on a low dimensional embedding of the echocardiography videos using neural network collaborative filtering. The method is evaluated in a collection of echocardiography videos of patients with a variety of mitral valve diseases, and additionally on an independent test cohort. It outperforms state-of-the-art unsupervised and supervised methods on low-quality videos or in the case of sparse annotation. © 2020 Elsevier B.V.
  • Recognition of dietary activity events using on-body sensors
    Item type: Journal Article
    Amft, Oliver; Troester, Gerhard (2008)
    Artificial intelligence in medicine
  • Explainable domain transfer of distant supervised cancer subtyping model via imaging-based rules extraction
    Item type: Journal Article
    Cavinato, Lara; Gozzi, Noemi; Sollini, Martina; et al. (2023)
    Artificial intelligence in medicine
    Image texture analysis has for decades represented a promising opportunity for cancer assessment and disease progression evaluation, evolving in a discipline, i.e., radiomics. However, the road to a complete translation into clinical practice is still hampered by intrinsic limitations. As purely supervised classification models fail in devising robust imaging-based biomarkers for prognosis, cancer subtyping approaches would benefit from the employment of distant supervision, for instance exploiting survival/recurrence information. In this work, we assessed, tested, and validated the domain-generality of our previously proposed Distant Supervised Cancer Subtyping model on Hodgkin Lymphoma. We evaluate the model performance on two independent datasets coming from two hospitals, comparing and analyzing the results. Although successful and consistent, the com-parison confirmed the instability of radiomics due to an across-center lack of reproducibility, leading to explainable results in one center and poor interpretability in the other. We thus propose a Random Forest-based Explainable Transfer Model for testing the domain-invariance of imaging biomarkers extracted from retrospec-tive cancer subtyping. In doing so, we tested the predictive ability of cancer subtyping in a validation and perspective setting, which led to successful results and supported the domain-generality of the proposed approach. On the other hand, the extraction of decision rules enables to draw of risk factors and robust bio-markers to inform clinical decisions. This work shows the potentialities of the Distant Supervised Cancer Sub-typing model to be further evaluated in larger multi-center datasets, to reliably translate radiomics into medical practice. The code is available at this GitHub repository.
  • Adaptive bandwidth selection for biomarker discovery in mass spectrometry
    Item type: Journal Article
    Fischer, Bernd; Roth, Volker; Buhmann, Joachim M. (2009)
    Artificial intelligence in medicine
Publications 1 - 4 of 4