35,735 research outputs found
Denoising Adversarial Autoencoders: Classifying Skin Lesions Using Limited Labelled Training Data
We propose a novel deep learning model for classifying medical images in the
setting where there is a large amount of unlabelled medical data available, but
labelled data is in limited supply. We consider the specific case of
classifying skin lesions as either malignant or benign. In this setting, the
proposed approach -- the semi-supervised, denoising adversarial autoencoder --
is able to utilise vast amounts of unlabelled data to learn a representation
for skin lesions, and small amounts of labelled data to assign class labels
based on the learned representation. We analyse the contributions of both the
adversarial and denoising components of the model and find that the combination
yields superior classification performance in the setting of limited labelled
training data.Comment: Under consideration for the IET Computer Vision Journal special issue
on "Computer Vision in Cancer Data Analysis
Learning Tasks for Multitask Learning: Heterogenous Patient Populations in the ICU
Machine learning approaches have been effective in predicting adverse
outcomes in different clinical settings. These models are often developed and
evaluated on datasets with heterogeneous patient populations. However, good
predictive performance on the aggregate population does not imply good
performance for specific groups.
In this work, we present a two-step framework to 1) learn relevant patient
subgroups, and 2) predict an outcome for separate patient populations in a
multi-task framework, where each population is a separate task. We demonstrate
how to discover relevant groups in an unsupervised way with a
sequence-to-sequence autoencoder. We show that using these groups in a
multi-task framework leads to better predictive performance of in-hospital
mortality both across groups and overall. We also highlight the need for more
granular evaluation of performance when dealing with heterogeneous populations.Comment: KDD 201
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