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Found 13 papers, 1 papers with code
Feature transforms for image data augmentation
These novel methods are based on the Fourier Transform (FT), the Radon Transform (RT) and the Discrete Cosine Transform (DCT).
Deep ensembles in bioimage segmentation
Semantic segmentation consists in classifying each pixel of an image by assigning it to a specific label chosen from a set of all the available ones.
Gated recurrent units and temporal convolutional network for multilabel classification
Multilabel learning tackles the problem of associating a sample with multiple class labels.
High performing ensemble of convolutional neural networks for insect pest image detection
The best performing ensemble, which combined the CNNs using the different augmentation methods and the two new Adam variants proposed here, achieved state of the art on both insect data sets: 95. 52% on Deng and 73. 46% on IP102, a score on Deng that competed with human expert classifications.
Deep ensembles based on Stochastic Activation Selection for Polyp Segmentation
The basic architecture in image segmentation consists of an encoder and a decoder: the first uses convolutional filters to extract features from the image, the second is responsible for generating the final output.
Exploiting Adam-like Optimization Algorithms to Improve the Performance of Convolutional Neural Networks
In this work, we compare Adam based variants based on the difference between the present and the past gradients, the step size is adjusted for each parameter.
Neural networks for Anatomical Therapeutic Chemical (ATC) classification
Motivation: Automatic Anatomical Therapeutic Chemical (ATC) classification is a critical and highly competitive area of research in bioinformatics because of its potential for expediting drug develop-ment and research.
Comparisons among different stochastic selection of activation layers for convolutional neural networks for healthcare
In this paper we classify biomedical images using ensembles of neural networks.
Deep learning for Plankton and Coral Classification
We study how to create an ensemble based of different CNN models, fine tuned on several datasets with the aim of exploiting their diversity.
Learning morphological operators for skin detection
In this work we propose a novel post processing approach for skin detectors based on trained morphological operators.
iProStruct2D: Identifying protein structural classes by deep learning via 2D representations
In this paper we address the problem of protein classification starting from a multi-view 2D representation of proteins.
Ensemble of Deep Learned Features for Melanoma Classification
The aim of this work is to propose an ensemble of descriptors for Melanoma Classification, whose performance has been evaluated on validation and test datasets of the melanoma challenge 2018.
Fair comparison of skin detection approaches on publicly available datasets
Skin detection is the process of discriminating skin and non-skin regions in a digital image and it is widely used in several applications ranging from hand gesture analysis to track body parts and face detection.
