Search

The early diagnosis of diabetic retinopathy (DR) can effectively prevent irreversible vision loss and assist ophthalmologists in providing timely and accurate treatment plans. However, the existing methods based on deep learning have a weak perception ability of different scale information in retinal fundus images, and the segmentation capability of subtle lesions is also insufficient. This paper aims to address these issues and proposes MLNet for DR lesion segmentation, which mainly consists of the Multi-Scale Attention Block (MSAB) and the Lesion Perception Block (LPB). The MSAB is designed to capture multi-scale lesion features in fundus images, while the LPB perceives subtle lesions in depth. In addition, a novel loss function with tailored lesion weight is designed to reduce the influence of imbalanced datasets on the algorithm. The performance comparison between MLNet and other state-of-the-art methods is carried out in the DDR dataset and DIARETDB1 dataset, and MLNet achieves the best results of 51.81% mAUPR, 49.85% mDice, and 37.19% mIoU in the DDR dataset, and 67.16% mAUPR and 61.82% mDice in the DIARETDB1 dataset. The generalization experiment of MLNet in the IDRiD dataset achieves 59.54% mAUPR, which is the best among other methods. The results show that MLNet has outstanding DR lesion segmentation ability. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy (DR) is a serious ocular complication that can pose a serious risk to a patient's vision and overall health. Currently, the automatic grading of DR is mainly using deep learning techniques. However, the lesion information in DR images is complex, variable in shape and size, and randomly distributed in the images, which leads to some shortcomings of the current research methods, i.e., it is difficult to effectively extract the information of these various features, and it is difficult to establish the connection between the lesion information in different regions. To address these shortcomings, we design a multi-scale dynamic fusion (MSDF) module and combine it with graph convolution operations to propose a multi-scale dynamic graph convolutional network (MDGNet) in this paper. MDGNet firstly uses convolution kernels with different sizes to extract features with different shapes and sizes in the lesion regions, and then automatically learns the corresponding weights for feature fusion according to the contribution of different features to model grading. Finally, the graph convolution operation is used to link the lesion features in different regions. As a result, our proposed method can effectively combine local and global features, which is beneficial for the correct DR grading. We evaluate the effectiveness of method on two publicly available datasets, namely APTOS and DDR. Extensive experiments demonstrate that our proposed MDGNet achieves the best grading results on APTOS and DDR, and is more accurate and diverse for the extraction of lesion information. [ABSTRACT FROM AUTHOR]

In this research, Lithium-ion (Li-ion) batteries were tested at four different charge rates (DCR): 0.2C, 0.5C, 1C, and 1.5C, and four different discharge rates (DDR): 0.5C, 0.9C, 1.3C, 1.6C. This paper proposes a capacity fade model for charging and discharging at accelerated current-rate (C-rate), to interpret the vulnerabilities of Li-ion batteries in energy storage system, because Lithium-ion (Li-ion) batteries are prone to ageing at the fluctuation of the loads in micro-grids. The characteristics of Li-ion batteries both at accelerated DCR and DDR are thoroughly investigated. It is discovered that charging and discharging Li-ion batteries outside of the standard C-rate accelerates their ageing. In addition, the degree of capacity fade is assessed at an accelerated C-rate to develop an ideal charge and discharge model for the micro-grids. Furthermore, the battery capacity fade model is then investigated with deep learning algorithm-based feed-forward neural network (FNN), and recurrent neural network with long-short term memory layer (RNN-LSTM). A comparison of the developed capacity fade models is performed, and it is discovered that the LSTM-RNN battery ageing model outperforms the conventional FNN network at accelerated C-rate. Nevertheless, the error metrics performance of both FNN and LSTM-RNN are less than 0.1%. [ABSTRACT FROM AUTHOR]

Video retrieval methods, e.g., for visual concept classification, person recognition, and similarity search, are essential to perform fine-grained semantic search in large video archives. However, such retrieval methods often have to be adapted to the users' changing search requirements: which concepts or persons are frequently searched for, what research topics are currently important or will be relevant in the future? In this paper, we present VIVA, a software tool for building content-based video retrieval methods based on deep learning models. VIVA allows non-expert users to conduct visual information retrieval for concepts and persons in video archives and to add new people or concepts to the underlying deep learning models as new requirements arise. For this purpose, VIVA provides a novel semi-automatic data acquisition workflow including a web crawler, image similarity search, as well as review and user feedback components to reduce the time-consuming manual effort for collecting training samples. We present experimental retrieval results using VIVA for four use cases in the context of a historical video collection of the German Broadcasting Archive based on about 34,000 h of television recordings from the former German Democratic Republic (GDR). We evaluate the performance of deep learning models built using VIVA for 91 GDR specific concepts and 98 personalities from the former GDR as well as the performance of the image and person similarity search approaches. [ABSTRACT FROM AUTHOR]

The presence of hard exudate (EX) lesions is an early clinical symptom of Diabetic Retinopathy (DR); its accurate segmentation is essential for diagnosis and treatment. Automatic segmentation of EX lesions is challenging because they have a wide range of sizes, shapes, and brightness, and can be confused with soft exudate lesions. In this paper, we present and assess an efficient segmentation method for interactively segmenting EX lesions in fundus images from DR patients. Our approach consisted of two main stages: (1) the first stage generates an automatic segmentation using a CNN-based method. (2) If the obtained segmentation is suboptimal, the second stage is used to refine each inaccurately segmented region using a CNN-based interactive segmentation method. To train and evaluate method performance, we used two public datasets (IDRiD and DDR) and two local datasets from two medical centers (EHos and EWT) containing a wide range of EX lesions. Several state-of-the art CNN-based models and the proposed method were trained and evaluated on IDRiD, DDR and EHos datasets using the area under the precision–recall curve (AUPR), the dice similarity coefficient (Dice) and the intersection over union (IoU). In addition, EWT dataset was used to evaluate the methods without retraining the CNN models. The evaluation results using IDRiD dataset showed that the proposed method achieved AUPR , mean Dice and mean IoU scores of 0.893, 76.6% and 62.4%, respectively, which are comparable to the state-of-the-art method on EX segmentation. When evaluated on the DDR dataset with large portion of small EX lesions, the proposed method achieved AUPR , mean Dice and mean IoU scores of 0.693, 60.8% and 45.5%, yielding state-of-the-art performance. Furthermore, with the main evaluation metric being AUPR , the evaluation results on two datasets, EHos and ETW, with scores of 0.689 and 0.667 demonstrated superiority of the proposed method over the other segmentation methods. The proposed two-stage CNN-based segmentation method is an useful approach for accurately segmenting EX lesions in color fundus images, enabling lesion quantification to aid experts in treating patients with DR. The source code, the trained CNN models and the data are publicly available at: https://github.com/dvquang2000/Interactive_HardExudates_Segmentation. • A two-stage CNN-based method was proposed for accurate EX lesions segmentation in fundus image • Multiple datasets in several hospitals and countries were included in the study • An assessment of the proposed method on two different field of view in image acquisition was performed • State-of-the-art and the proposed methods were tested with and without retraining the CNN models • The proposed method outperformed state-of-the-art methods in most evaluation scores [ABSTRACT FROM AUTHOR]

The German Broadcasting Archive maintains the cultural heritage of radio and television broadcasts of the former German Democratic Republic (GDR). The uniqueness and importance of the video material fosters a large scientific interest in the video content. In this paper, we present a system for automatic video content analysis and retrieval to facilitate search in historical collections of GDR television recordings. It relies on a distributed, service-oriented architecture and includes video analysis algorithms for shot boundary detection, concept classification, person recognition, text recognition and similarity search. The combination of different search modalities allows users to obtain answers for a wide range of queries, leading to satisfactory results in short time. The performance of the system is evaluated using 2500 h of GDR television recordings. [ABSTRACT FROM AUTHOR]