96 research outputs found
Transfer learning for diagnosis of congenital abnormalities of the kidney and urinary tract in children based on Ultrasound imaging data
Classification of ultrasound (US) kidney images for diagnosis of congenital
abnormalities of the kidney and urinary tract (CAKUT) in children is a
challenging task. It is desirable to improve existing pattern classification
models that are built upon conventional image features. In this study, we
propose a transfer learning-based method to extract imaging features from US
kidney images in order to improve the CAKUT diagnosis in children.
Particularly, a pre-trained deep learning model (imagenet-caffe-alex) is
adopted for transfer learning-based feature extraction from 3-channel feature
maps computed from US images, including original images, gradient features, and
distanced transform features. Support vector machine classifiers are then built
upon different sets of features, including the transfer learning features,
conventional imaging features, and their combination. Experimental results have
demonstrated that the combination of transfer learning features and
conventional imaging features yielded the best classification performance for
distinguishing CAKUT patients from normal controls based on their US kidney
images.Comment: Accepted paper in IEEE International Symposium on Biomedical Imaging
(ISBI), 201
Distribution-Based Categorization of Classifier Transfer Learning
Transfer Learning (TL) aims to transfer knowledge acquired in one problem,
the source problem, onto another problem, the target problem, dispensing with
the bottom-up construction of the target model. Due to its relevance, TL has
gained significant interest in the Machine Learning community since it paves
the way to devise intelligent learning models that can easily be tailored to
many different applications. As it is natural in a fast evolving area, a wide
variety of TL methods, settings and nomenclature have been proposed so far.
However, a wide range of works have been reporting different names for the same
concepts. This concept and terminology mixture contribute however to obscure
the TL field, hindering its proper consideration. In this paper we present a
review of the literature on the majority of classification TL methods, and also
a distribution-based categorization of TL with a common nomenclature suitable
to classification problems. Under this perspective three main TL categories are
presented, discussed and illustrated with examples
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