The Liquid Metal Age: A Transition From Hg to Ga.

This review offers an illuminating journey through the historical evolution and modern-day applications of liquid metals, presenting a comprehensive view of their significance in diverse fields. Tracing the trajectory from mercury applications to contemporary innovations, the paper explores their pivotal role in industry and research. The analysis spans electrical switches, mechanical applications, electrodes, chemical synthesis, energy storage, thermal transport, electronics, and biomedicine.

Robustness of texture-based roundwood tracking.

The proof of origin of wood logs is becoming more and more important. In the context of Industry 4.0 and to combat illegal logging, there is an increased interest to track each individual log.

Improving CNN training on endoscopic image data by extracting additionally training data from endoscopic videos.

In this work we present a technique to deal with one of the biggest problems for the application of convolutional neural networks (CNNs) in the area of computer assisted endoscopic image diagnosis, the insufficient amount of training data. Based on patches from endoscopic images of colonic polyps with given label information, our proposed technique acquires additional (labeled) training data by tracking the area shown in the patches through the corresponding endoscopic videos and by extracting additional image patches from frames of these areas. So similar to the widely used augmentation strategies, additional training data is produced by adding images with different orientations, scales and points of view than the original images.

Quest for the best endoscopic imaging modality for computer-assisted colonic polyp staging.

Background: It was shown in previous studies that high definition endoscopy, high magnification endoscopy and image enhancement technologies, such as chromoendoscopy and digital chromoendoscopy [narrow-band imaging (NBI), i-Scan] facilitate the detection and classification of colonic polyps during endoscopic sessions. However, there are no comprehensive studies so far that analyze which endoscopic imaging modalities facilitate the automated classification of colonic polyps. In this work, we investigate the impact of endoscopic imaging modalities on the results of computer-assisted diagnosis systems for colonic polyp staging.

Fisher encoding of convolutional neural network features for endoscopic image classification.

We propose an approach for the automated diagnosis of celiac disease (CD) and colonic polyps (CP) based on applying Fisher encoding to the activations of convolutional layers. In our experiments, three different convolutional neural network (CNN) architectures (AlexNet, VGG-f, and VGG-16) are applied to three endoscopic image databases (one CD database and two CP databases). For each network architecture, we perform experiments using a version of the net that is pretrained on the ImageNet database, as well as a version of the net that is trained on a specific endoscopic image database.

Training of polyp staging systems using mixed imaging modalities.

Background: In medical image data sets, the number of images is usually quite small. The small number of training samples does not allow to properly train classifiers which leads to massive overfitting to the training data. In this work, we investigate whether increasing the number of training samples by merging datasets from different imaging modalities can be effectively applied to improve predictive performance.

Exploring Deep Learning and Transfer Learning for Colonic Polyp Classification.

Recently, Deep Learning, especially through Convolutional Neural Networks (CNNs) has been widely used to enable the extraction of highly representative features. This is done among the network layers by filtering, selecting, and using these features in the last fully connected layers for pattern classification. However, CNN training for automated endoscopic image classification still provides a challenge due to the lack of large and publicly available annotated databases.

Directional wavelet based features for colonic polyp classification.

In this work, various wavelet based methods like the discrete wavelet transform, the dual-tree complex wavelet transform, the Gabor wavelet transform, curvelets, contourlets and shearlets are applied for the automated classification of colonic polyps. The methods are tested on 8 HD-endoscopic image databases, where each database is acquired using different imaging modalities (Pentax's i-Scan technology combined with or without staining the mucosa), 2 NBI high-magnification databases and one database with chromoscopy high-magnification images. To evaluate the suitability of the wavelet based methods with respect to the classification of colonic polyps, the classification performances of 3 wavelet transforms and the more recent curvelets, contourlets and shearlets are compared using a common framework.

Local fractal dimension based approaches for colonic polyp classification.

This work introduces texture analysis methods that are based on computing the local fractal dimension (LFD; or also called the local density function) and applies them for colonic polyp classification. The methods are tested on 8 HD-endoscopic image databases, where each database is acquired using different imaging modalities (Pentax's i-Scan technology combined with or without staining the mucosa) and on a zoom-endoscopic image database using narrow band imaging. In this paper, we present three novel extensions to a LFD based approach.

A systematic evaluation of the scale invariance of texture recognition methods.

A large variety of well-known scale-invariant texture recognition methods is tested with respect to their scale invariance. The scale invariance of these methods is estimated by comparing the results of two test setups. In the first test setup, the images of the training and evaluation set are acquired under same scale conditions and in the second test setup, the images in the evaluation set are gathered under different scale conditions than those of the training set.

Scale invariant texture descriptors for classifying celiac disease.

Scale invariant texture recognition methods are applied for the computer assisted diagnosis of celiac disease. In particular, emphasis is given to techniques enhancing the scale invariance of multi-scale and multi-orientation wavelet transforms and methods based on fractal analysis. After fine-tuning to specific properties of our celiac disease imagery database, which consists of endoscopic images of the duodenum, some scale invariant (and often even viewpoint invariant) methods provide classification results improving the current state of the art.