An enhanced deep learning architecture for classification of Tuberculosis types from CT lung images

In this work, an enhanced ResNet deep learning network, depth-ResNet, has been developed to classify the five types of Tuberculosis (TB) lung CT images. Depth-ResNet takes 3D CT images as a whole and processes the volumatic blocks along depth directions. It builds on the ResNet-50 model to obtain 2D features on each frame and injects depth information at each process block. As a result, the averaged accuracy for classification is 71.60% for depth-ResNet and 68.59% for ResNet. The datasets are collected from the ImageCLEF 2018 competition with 1008 training data in total, where the top reported accuracy was 42.27%

Seeking Optimum System Settings for Physical Activity Recognition on Smartwatches

Physical activity recognition (PAR) using wearable devices can provide valued information regarding an individual's degree of functional ability and lifestyle. In this regards, smartphone-based physical activity recognition is a well-studied area. Research on smartwatch-based PAR, on the other hand, is still in its infancy. Through a large-scale exploratory study, this work aims to investigate the smartwatch-based PAR domain. A detailed analysis of various feature banks and classification methods are carried out to find the optimum system settings for the best performance of any smartwatch-based PAR system for both personal and impersonal models. To further validate our hypothesis for both personal (The classifier is built using the data only from one specific user) and impersonal (The classifier is built using the data from every user except the one under study) models, we tested single subject validation process for smartwatch-based activity recognition.Comment: 15 pages, 2 figures, Accepted in CVC'1

Nanostructure transfer using cyclic olefin copolymer templates fabricated by thermal nanoimprint lithography

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Multiplex cytokine analysis of dermal interstitial blister fluid defines local disease mechanisms in systemic sclerosis.

Clinical diversity in systemic sclerosis (SSc) reflects multifaceted pathogenesis and the effect of key growth factors or cytokines operating within a disease-specific microenvironment. Dermal interstitial fluid sampling offers the potential to examine local mechanisms and identify proteins expressed within lesional tissue. We used multiplex cytokine analysis to profile the inflammatory and immune activity in the lesions of SSc patients

Enhancing In Vitro Stability of Albumin Microbubbles Produced Using Microfluidic T-Junction Device

Microfluidics is an efficient technique for continuous synthesis of monodispersed microbubbles. However, microbubbles produced using microfluidic devices possess lower stability due to quick dissolution of core gas when exposed to an aqueous environment. This work aims at generating highly stable monodispersed albumin microbubbles using microfluidic T-junction devices. Microbubble generation was facilitated by an aqueous phase consisting of bovine serum albumin (BSA) as a model protein and nitrogen (N2) gas. Microbubbles were chemically cross-linked using dilute glutaraldehyde (0.75% v/v) solution and thermally cross-linked by collecting microbubbles in hot water maintained at 368 (±2) K. These microbubbles were then subjected to in vitro dissolution in an air-saturated water. Microbubbles cross-linked with a combined treatment of thermal and chemical cross-linking (TC & CC) had longer dissolution time compared to microbubbles chemically cross-linked (CC) alone, thermally cross-linked (TC) alone, and non-cross-linked microbubbles. Circular dichroism (CD) spectroscopy analysis revealed that percent reduction in alpha-helices of BSA was higher for the combined treatment of TC & CC when compared to other treatments. In contrast to non-cross-linked microbubbles where microbubble shell dissolved completely, a significant shell detachment was observed during the final phase of the dissolution for cross-linked microbubbles captured using high speed camera, depending upon the extent of cross-linking of the microbubble shell. SEM micrographs of the microbubble shell revealed the shell thickness of microbubbles treated with TC & CC to be highest compared to only thermally or only chemically cross-linked microbubbles. Comparison of microbubble dissolution data to a mass transfer model showed that shell resistance to gas permeation was highest for microbubbles subjected to a combined treatment of TC & CC

POEM: Pricing Longer for Edge Computing in the Device Cloud

Multiple access mobile edge computing has been proposed as a promising technology to bring computation services close to end users, by making good use of edge cloud servers. In mobile device clouds (MDC), idle end devices may act as edge servers to offer computation services for busy end devices. Most existing auction based incentive mechanisms in MDC focus on only one round auction without considering the time correlation. Moreover, although existing single round auctions can also be used for multiple times, users should trade with higher bids to get more resources in the cascading rounds of auctions, then their budgets will run out too early to participate in the next auction, leading to auction failures and the whole benefit may suffer. In this paper, we formulate the computation offloading problem as a social welfare optimization problem with given budgets of mobile devices, and consider pricing longer of mobile devices. This problem is a multiple-choice multi-dimensional 0-1 knapsack problem, which is a NP-hard problem. We propose an auction framework named MAFL for long-term benefits that runs a single round resource auction in each round. Extensive simulation results show that the proposed auction mechanism outperforms the single round by about 55.6% on the revenue on average and MAFL outperforms existing double auction by about 68.6% in terms of the revenue.Comment: 8 pages, 1 figure, Accepted by the 18th International Conference on Algorithms and Architectures for Parallel Processing (ICA3PP

Endoscopic image analysis using Deep Convolutional GAN and traditional data

One big challenge encountered in the medical field is the availability of only limited annotated datasets for research. On the other hand, medical image annotation requires a lot of input from medical experts. It is noticed that machine learning and deep learning are producing better results in the area of image classification. However, these techniques require large training datasets, which is the major concern for medical image processing. Another issue is the unbalanced nature of the different classes of data, leading to the under-representation of some classes. Data augmentation has emerged as a good technique to deal with these challenges. In this work, we have applied traditional data augmentation and Generative Adversarial Network (GAN) on endoscopic esophagus images to increase the number of images for the training datasets. Eventually we have applied two deep learning models namely ResNet50 and VGG16 to extract and represent the relevant cancer features. The results show that the accuracy of the model increases with data augmentation and GAN. In fact, GAN has achieved the highest accuracy, that is, 94% over non-augmented training set and traditional data augmentation for VGG16

Robust Re-Identification by Multiple Views Knowledge Distillation

To achieve robustness in Re-Identification, standard methods leverage tracking information in a Video-To-Video fashion. However, these solutions face a large drop in performance for single image queries (e.g., Image-To-Video setting). Recent works address this severe degradation by transferring temporal information from a Video-based network to an Image-based one. In this work, we devise a training strategy that allows the transfer of a superior knowledge, arising from a set of views depicting the target object. Our proposal - Views Knowledge Distillation (VKD) - pins this visual variety as a supervision signal within a teacher-student framework, where the teacher educates a student who observes fewer views. As a result, the student outperforms not only its teacher but also the current state-of-the-art in Image-To-Video by a wide margin (6.3% mAP on MARS, 8.6% on Duke-Video-ReId and 5% on VeRi-776). A thorough analysis - on Person, Vehicle and Animal Re-ID - investigates the properties of VKD from a qualitatively and quantitatively perspective