Article-Journal

Automatic mapping of aquaculture activity in the Atlantic Ocean
The production of wild fish has remained relatively stable in the last two decades, whereas aquaculture organism production has …
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Clustering using graph convolution networks
Graph convolution networks (GCNs) have emerged as powerful approaches for semi-supervised classification of attributed graph data. In …
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Data augmentation for deep visual recognition using superpixel based pairwise image fusion
Data augmentation is an important paradigm for boosting the generalization capability of deep learning in image classification tasks. …
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Discriminative Feature Learning Through Angular Margin-Based Softmax Losses for Breast Cancer Classification
When many histopathological breast images with different magnification levels need to be analyzed, diagnosing benign or malignant …
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DL4MicEverywhere: deep learning for microscopy made flexible, shareable and reproducible
DL4MicEverywhere is a platform that lets users train and implement their models in different computational environments. These …
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DL4MicEverywhere: deep learning for microscopy made flexible, shareable and reproducible
Emb-trattunet: a novel edge loss function and transformer-CNN architecture for multi-classes pneumonia infection segmentation in low annotation regimes
One of the primary challenges in applying deep learning approaches to medical imaging is the limited availability of data due to …
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Enhancing MRI brain tumor classification: A comprehensive approach integrating real-life scenario simulation and augmentation techniques
Brain cancer poses a significant global health challenge, with mortality rates showing a concerning surge over recent decades. The …
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Facial Age Estimation Using Multi-Stage Deep Neural Networks
Over the last decade, the world has witnessed many breakthroughs in artificial intelligence, largely due to advances in deep learning …
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LCAMix: Local-and-contour aware grid mixing based data augmentation for medical image segmentation
Medical image segmentation often faces challenges related to overfitting, primarily due to the limited and complex training samples. …
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LCAMix: Local-and-contour aware grid mixing based data augmentation for medical image segmentation
LGCOAMix: Local and Global Context-and-Object-Part-Aware Superpixel-Based Data Augmentation for Deep Visual Recognition
Cutmix-based data augmentation, which uses a cut-and-paste strategy, has shown remarkable generalization capabilities in deep learning. …
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LGCOAMix: Local and Global Context-and-Object-Part-Aware Superpixel-Based Data Augmentation for Deep Visual Recognition