Deep neural networks (DNNs) have achieved great success in a wide variety of medical
image analysis tasks. However, these achievements indispensably rely on the accurately-…

… For image colorization, we use a conditional GAN [19], where the generator is a U-Net
architecture based on resnet50-DeepLabV3+ [16] and the discriminator is a simple CNN model …

… a main stream deep learning model for biomedical image segmentation. The encoder fully
… our cascade decoder for several challenging biomedical image segmentation tasks, and the …

Melanoma is the most deadly form of skin cancer worldwide. Many efforts have been made
for early detection of melanoma with deep learning based on dermoscopic images. It is …

Modern advancements in deep learning provide a powerful framework for disease
classification based on neuroimaging data. However, interpreting the classification decision of …

… Therefore, in this work we will use the ISIC 2017-ISBI set [5], which is divided into training
(2000 images), validation (150 images), and test (600 images) sets. The task of this challenge …

We propose a generalized focal loss function based on the Tversky index to address the issue
of data imbalance in medical image segmentation. Compared to the commonly used Dice …

… We extend the table presented by [16] with a few more results [18], [19], including ours.
The results on the FCN and U-Net are reported from [16] and have been trained on data pre-…

… International symposium on biomedical imaging (ISBI), hosted by the international skin
imaging collaboration (ISIC),” in 2018 IEEE … national Symposium on Biomedical Imaging (ISBI …

… We conducted our experiments using the IEEE ISBI 2015 MS lesion segmentation
challenge (ISBI 2015) dataset [12], 2019 IEEE 16th International Symposium on Biomedical …