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On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practice

A comprehensive survey on support vector machine classification: Applications, challenges and trends

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A hybrid deep transfer learning model with machine learning methods for face mask detection in the era of the COVID-19 pandemic

Research in artificial intelligence for radiology and radiotherapy has recently become increasingly reliant on the use of deep learning-based algorithms. While the performance of the models which these algorithms produce can significantly outperform more traditional machine learning methods, they do rely on larger datasets being available for training. To address this issue, data augmentation has become a popular method for increasing the size of a training dataset, particularly in fields where large datasets aren't typically available, which is often the case when working with medical images. Data augmentation aims to generate additional data which is used to train the model and has been shown to improve performance when validated on a separate unseen dataset. This approach has become commonplace so to help understand the types of data augmentation techniques used in state-of-the-art deep learning models, we conducted a systematic review of the literature where data augmentation was utilised on medical images (limited to CT and MRI) to train a deep learning model. Articles were categorised into basic, deformable, deep learning or other data augmentation techniques. As artificial intelligence models trained using augmented data make their way into the clinic, this review aims to give an insight to these techniques and confidence in the validity of the models produced.

A review of medical image data augmentation techniques for deep learning applications.

This paper is focused on automatic emotion recognition from static grayscale images. Here, we propose a new approach to this problem, which combines a few other methods. The facial region is divided into small subregions, which are selected for processing based on a face relevance map. From these regions, local directional pattern histograms are extracted and concatenated into a single feature histogram, which is classified into one of seven defined emotional states using support vector machines. In our case, we distinguish: anger, disgust, fear, happiness, neutrality, sadness and surprise. In our experimental study we demonstrate that the expression recognition accuracy for Japanese Female Facial Expression database is one of the best compared with the results reported in the literature.

Emotion recognition from facial images using binary face relevance maps

Skin detection in color images has become an active research topic due to its numerous practical applications. In this paper, we couple classical and deep machine learning, together with an evolutionary training set selection algorithm for this task, and build an end-to-end pipeline that can flexibly benefit from such techniques. The experiments indicate that our approach can deliver accurate skin detection in a short time that may be generalized over different datasets, and that evolutionary training set selection can play a key role to allow for training the models from large training data.

Evolutionary algorithms meet classical and deep machine learning for skin detection in color images

Multi-image super-resolution is a challenging computer vision problem that aims at recovering a high-resolution image from its multiple low-resolution counterparts. In recent years, deep learning-based approaches have shown promising results, however, they often lack the flexibility of modeling complex relations between pixels, permutability of the input data, or they were designed to process a specific number of input images. In this paper, we propose an improved version of our earlier graph neural network that benefits from permutation-invariant graph-based representation of multiple low-resolution images. Importantly, we demonstrate that our solution allows for performing reconstruction from a set of heterogeneous input images, which is not straightforward for other state-of-the-art techniques. Such flexibility is a crucial feature for practical applications, which is confirmed qualitatively and quantitatively for a set of real-world (rather than simulated) input images. 

Graph-Based Representation for Multi-image Super-Resolution

The goal of metagenomic analysis is to extract relevant information concerning the organisms that have left their genetic traces in an environmental sample. Each sample is subject to nucleotide sequencing, and obtained DNA fragments are decomposed into k-mers---short sequences of k nucleotides. Based on the found k-mers and their occurrence frequencies, it is possible to identify the organisms present in the sample---this allows for further analysis, but requires using large taxonomic datasets. Alternatively, depending on the specific goal of the analysis, the whole sample may be classified directly based on its k-mer profile. However, this is challenging due to a large number of possible k-mers, and choosing the most valuable ones remains an open research problem. In this paper, we propose a new technique that exploits a genetic algorithm for selecting a subset of k-mer features that are used for classification. We report our initial, yet promising results obtained for the problem of detecting the type 2 diabetes from human gut metagenomic samples. We expect that the proposed classification framework will enhance the capabilities of metagenomic analysis, without the need for performing costly taxonomic classification.

A genetic algorithm for classifying metagenomic data

This paper presents a novel approach towards human skin regions detection and segmentation. The main contribution is concerned with proposing the discriminative texture analysis performed over skin probability maps obtained using conventional color-based methods. Results of the experimental validation reported in the paper confirm that the texture is an important source of information, neglected by many skin detection techniques.

Skin region detection in digital images using discriminative textural features

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