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A Novel Multi-task Deep Learning Model for Skin Lesion Segmentation and Classification
In this study, a multi-task deep neural network is proposed for skin les...
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Crowd disagreement of medical images is informative
Classifiers for medical image analysis are often trained with a single c...
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Multi-task Learning with Weak Class Labels: Leveraging iEEG to Detect Cortical Lesions in Cryptogenic Epilepsy
Multi-task learning (MTL) is useful for domains in which data originates...
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A Deep Multi-task Learning Approach to Skin Lesion Classification
Skin lesion identification is a key step toward dermatological diagnosis...
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Label-Efficient Multi-Task Segmentation using Contrastive Learning
Obtaining annotations for 3D medical images is expensive and time-consum...
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Frequency and Spatial domain based Saliency for Pigmented Skin Lesion Segmentation
Skin lesion segmentation can be rather a challenging task owing to the p...
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TAL EmotioNet Challenge 2020 Rethinking the Model Chosen Problem in Multi-Task Learning
This paper introduces our approach to the EmotioNet Challenge 2020. We p...
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Multi-task Learning with Crowdsourced Features Improves Skin Lesion Diagnosis
Machine learning has a recognised need for large amounts of annotated data. Due to the high cost of expert annotations, crowdsourcing, where non-experts are asked to label or outline images, has been proposed as an alternative. Although many promising results are reported, the quality of diagnostic crowdsourced labels is still lacking. We propose to address this by instead asking the crowd about visual features of the images, which can be provided more intuitively, and by using these features in a multi-task learning framework. We compare our proposed approach to a baseline model with a set of 2000 skin lesions from the ISIC 2017 challenge dataset. The baseline model only predicts a binary label from the skin lesion image, while our multi-task model also predicts one of the following features: asymmetry of the lesion, border irregularity and color. We show that crowd features in combination with multi-task learning leads to improved generalisation. The area under the receiver operating characteristic curve is 0.754 for the baseline model and 0.782, 0.785 and 0.789 for multi-task models with border, color and asymmetry respectively. Finally, we discuss the findings, identify some limitations and recommend directions for further research.
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