A Benchmark Dataset of Endoscopic Images and Novel Deep Learning Method to Detect Intestinal Metaplasia and Gastritis Atrophy.

Endoscopy has been routinely used to diagnose stomach diseases including intestinal metaplasia (IM) and gastritis atrophy (GA). Such routine examination usually demands highly skilled radiologists to focus on a single patient with substantial time, causing the following two key challenges: 1) the dependency on the radiologist's experience leading to inconsistent diagnosis results across different radiologists; 2) limited examination efficiency due to the demanding time and energy consumption to the radiologist. This paper proposes to address these two issues in endoscopy using novel machine learning method in three-folds. Firstly, we build a novel and relatively big endoscopy dataset of 21,420 images from the widely used White Light Imaging (WLI) endoscopy and more recent Linked Color Imaging (LCI) endoscopy, which were annotated by experienced radiologists and validated with biopsy results, presenting a benchmark dataset. Secondly, we propose a novel machine learning model inspired by the human visual system, named as local attention grouping, to effectively extract key visual features, which is further improved by learning from multiple randomly selected regional images via ensemble learning. Such a method avoids the significant problem in the deep learning methods that decrease the resolution of original images to reduce the size of input samples, which would remove smaller lesions in endoscopy images. Finally, we propose a dual transfer learning strategy to train the model with co-distributed features between WLI and LCI images to further improve the performance. The experiment results, measured by accuracy, specificity, sensitivity, positive detection rate and negative detection rate, on IM are 99.18 %, 98.90 %, 99.45 %, 99.45 %, 98.91 %, respectively, and on GA are 97.12 %, 95.34 %, 98.90 %, 98.86 %, 95.50 %, respectively, achieving state of the art performance that outperforms current mainstream deep learning models.

A Fully Deep Learning Paradigm for Pneumoconiosis Staging on Chest Radiographs.

Pneumoconiosis staging has been a very challenging task, both for certified radiologists and computer-aided detection algorithms. Although deep learning has shown proven advantages in the detection of pneumoconiosis, it remains challenging in pneumoconiosis staging due to the stage ambiguity of pneumoconiosis and noisy samples caused by misdiagnosis when they are used in training deep learning models. In this article, we propose a fully deep learning pneumoconiosis staging paradigm that comprises a segmentation procedure and a staging procedure. The segmentation procedure extracts lung fields in chest radiographs through an Asymmetric Encoder-Decoder Network (AED-Net) that can mitigate the domain shift between multiple datasets. The staging procedure classifies the lung fields into four stages through our proposed deep log-normal label distribution learning and focal staging loss. The two cascaded procedures can effectively solve the problem of model overfitting caused by stage ambiguity and noisy labels of pneumoconiosis. Besides, we collect a clinical chest radiograph dataset of pneumoconiosis from the certified radiologist's diagnostic reports. The experimental results on this novel pneumoconiosis dataset confirm that the proposed deep pneumoconiosis staging paradigm achieves an Accuracy of 90.4%, a Precision of 84.8%, a Sensitivity of 78.4%, a Specificity of 95.6%, an F1-score of 80.9% and an Area Under the Curve (AUC) of 96%. In particular, we achieve 68.4% Precision, 76.5% Sensitivity, 95% Specificity, 72.2% F1-score and 89% AUC on the early pneumoconiosis 'stage-1'.

See the wind: Wind scale estimation with optical flow and VisualWind dataset.

Wind sensing by learning from video clips could empower cameras to sense the wind scale and significantly improve the spatiotemporal resolution of existing professional weather records that are often at the city scale. Humans can interpret the wind scale from the motion of surrounding environment objects, especially the moving dynamics of trees in the wind. The goal of this paper is to train cameras to sense the wind by capturing such motion information using optical flow and machine learning models. To this end, we introduce a novel video dataset of over 6000 labeled video clips, covering eleven wind classes of the Beaufort scale. The videos are collected from social media, pubic cameras, and self-recording with varying numbers of clips in each class. Every video clip has a length of 10 s with varied frame rates, and contains scenes of various trees swaying in different scales of wind from an approximately fixed viewpoint. The variation in scene over the course of a single video is minimal. We propose a dual-branch model to estimate the wind scale including a motion branch, which uses optical flow to extract the tree movement dynamics, and a visual branch, to provide visual complementary clues for wind scale estimation. The two branches are fused adaptively in the end of the network, achieving 86.69 % accuracy which confirms the effectiveness of the proposed method. We have conducted experiments compared with a two-stage baseline model and a model only consisting of the motion branch, achieving the best accuracy with the potential to significantly improve the efficiency in time and storage. The dataset and the code are publicly accessible online.

Distributed UAV Swarm Formation and Collision Avoidance Strategies Over Fixed and Switching Topologies.

This article proposes a controlling framework for multiple unmanned aerial vehicles (UAVs) to integrate the modes of formation flight and swarm deployment over fixed and switching topologies. Formation strategies enable UAVs to enjoy key collective benefits including reduced energy consumption, but the shape of the formation and each UAV's freedom are significantly restrained. Swarm strategies are thus proposed to maximize each UAV's freedom following simple yet powerful rules. This article investigates the integration and switch between these two strategies, considering the deployment environment factors, such as poor network conditions and unknown and often highly mobile obstacles. We design a distributed formation controller to guide multiple UAVs in orderless states to swiftly reach an intended formation. Inspired by starling birds and similar biological creatures, a distributed collision avoidance controller is proposed to avoid unknown and mobile obstacles. We further illustrated the stability of the controllers over both fixed and switching topologies. The experimental results confirm the effectiveness of the framework.

RF Signal-Based UAV Detection and Mode Classification: A Joint Feature Engineering Generator and Multi-Channel Deep Neural Network Approach.

With the proliferation of Unmanned Aerial Vehicles (UAVs) to provide diverse critical services, such as surveillance, disaster management, and medicine delivery, the accurate detection of these small devices and the efficient classification of their flight modes are of paramount importance to guarantee their safe operation in our sky. Among the existing approaches, Radio Frequency (RF) based methods are less affected by complex environmental factors. The similarities between UAV RF signals and the diversity of frequency components make accurate detection and classification a particularly difficult task. To bridge this gap, we propose a joint Feature Engineering Generator (FEG) and Multi-Channel Deep Neural Network (MC-DNN) approach. Specifically, in FEG, data truncation and normalization separate different frequency components, the moving average filter reduces the outliers in the RF signal, and the concatenation fully exploits the details of the dataset. In addition, the multi-channel input in MC-DNN separates multiple frequency components and reduces the interference between them. A novel dataset that contains ten categories of RF signals from three types of UAVs is used to verify the effectiveness. Experiments show that the proposed method outperforms the state-of-the-art UAV detection and classification approaches in terms of 98.4% and F1 score of 98.3%.

A stochastic modelling framework for the reconstruction of cardiovascular signals.

This paper presents a common stochastic modelling framework for physiological signals which allows patient simulation following a synthesis-by-analysis approach. Within this framework, we propose a general model-based methodology able to reconstruct missing or artifacted signal intervals in cardiovascular monitoring applications. The proposed model consists of independent stages which provide high flexibility to incorporate signals of different nature in terms of shape, cross-correlation and variability. The reconstruction methodology is based on model sampling and selection based on a wide range of boundary conditions, which include prior information. Results on real data show how the proposed methodology fits the particular approaches presented so far for electrocardiogram (ECG) reconstruction and how a simple extension within the framework can significantly improve their performance.