8 research outputs found
A Hybrid Instance-based Transfer Learning Method
In recent years, supervised machine learning models have demonstrated
tremendous success in a variety of application domains. Despite the promising
results, these successful models are data hungry and their performance relies
heavily on the size of training data. However, in many healthcare applications
it is difficult to collect sufficiently large training datasets. Transfer
learning can help overcome this issue by transferring the knowledge from
readily available datasets (source) to a new dataset (target). In this work, we
propose a hybrid instance-based transfer learning method that outperforms a set
of baselines including state-of-the-art instance-based transfer learning
approaches. Our method uses a probabilistic weighting strategy to fuse
information from the source domain to the model learned in the target domain.
Our method is generic, applicable to multiple source domains, and robust with
respect to negative transfer. We demonstrate the effectiveness of our approach
through extensive experiments for two different applications.Comment: Machine Learning for Health (ML4H) Workshop at NeurIPS 2018
arXiv:cs/010120
Models and Analysis of Vocal Emissions for Biomedical Applications
The International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications (MAVEBA) came into being in 1999 from the particularly felt need of sharing know-how, objectives and results between areas that until then seemed quite distinct such as bioengineering, medicine and singing. MAVEBA deals with all aspects concerning the study of the human voice with applications ranging from the neonate to the adult and elderly. Over the years the initial issues have grown and spread also in other aspects of research such as occupational voice disorders, neurology, rehabilitation, image and video analysis. MAVEBA takes place every two years always in Firenze, Italy
Characterising pattern asymmetry in pigmented skin lesions
Abstract. In clinical diagnosis of pigmented skin lesions asymmetric pigmentation is often indicative of
melanoma. This paper describes a method and measures for characterizing lesion symmetry. The estimate of
mirror symmetry is computed first for a number of axes at different degrees of rotation with respect to the
lesion centre. The statistics of these estimates are the used to assess the overall symmetry. The method is
applied to three different lesion representations showing the overall pigmentation, the pigmentation pattern,
and the pattern of dermal melanin. The best measure is a 100% sensitive and 96% specific indicator of
melanoma on a test set of 33 lesions, with a separate training set consisting of 66 lesions