The precise segmentation of organs from computed tomography is a fundamental and pivotal task for correct diagnosis and proper treatment of diseases. Neural network models are widely explored for their promising perfo...
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This study assesses the outcomes of the NTIRE 2023 Challenge on Non-Homogeneous Dehazing, wherein novel techniques were proposed and evaluated on new image dataset called HD-NH-HAZE. The HD-NH-HAZE dataset contains 50...
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image completion is a challenging task, particularly when ensuring that generated content seamlessly integrates with existing parts of an image. While recent diffusion models have shown promise, they often struggle wi...
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The main objective of this work is to develop novel fault diagnosis techniques using ensemble learning and multivariate statistical techniques. The proposed methods are capable of identifying and classifying PV faults...
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An important aim of research in medical imaging is the development of computer aided diagnosis (CAD) systems. A fundamental step in these systems is the image segmentation and convolutional neural networks (CNNs) are ...
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Background and purpose: Skin tumours have become one of the most common diseases worldwide. While benign ones are not usually a threat to human health, malignant ones can develop into skin cancer and become life-threa...
Background and purpose: Skin tumours have become one of the most common diseases worldwide. While benign ones are not usually a threat to human health, malignant ones can develop into skin cancer and become life-threatening if left untreated. Early detection of the disease is important for the treatment of patients with skin tumours and dermoscopy is the most effective means of diagnosing skin tumours. However, the complexity of skin tumour cells makes the diagnosis somewhat erroneous for doctors. Therefore, a dermoscopic classification network based on deep learning and computer-aided diagnostic techniques is needed to obtain a high diagnostic accuracy rate for skin tumours. Methods: In this paper, Deep-skin, a model for dermoscopic image classification is proposed, which is based on both attention mechanism and ensemble learning. Considering the characteristics of dermoscopic images, embedding different attention mechanisms on top of Inception-V3 has been suggested to obtain more potential features. We then improve the classification performance by late fusion of the different models. To demonstrate the effectiveness of Deep-skin, experiments and evaluations are performed on the publicly available dataset Skin Cancer: Malignant vs. Benign and compare the performance of Deep-skin with other classification models. Results: The experimental results indicate that Deep-skin performs well on the dataset in comparison to other models, achieving a maximum accuracy of 87.8%.Conclusion: In this paper, the Deep-skin model is proposed for the classification of dermoscopic images and has shown better performance. In the future, we intend to investigate better classification models for automatic diagnosis of skin tumours. Such models can potentially assist physicians and patients in clinical settings.
Understanding how the brain works is a base of cognitive info-communication. To this aim we focus on multiple target tracking (MTT) as a key task that involves two important cognitive factors, attention and memory. Hu...
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ISBN:
(数字)9798350378245
ISBN:
(纸本)9798350378252
Understanding how the brain works is a base of cognitive info-communication. To this aim we focus on multiple target tracking (MTT) as a key task that involves two important cognitive factors, attention and memory. Humans track multiple objects in their daily life while facing various challenges including occlusion and set-size. Eye movement research has shown that there are within and between subjects’ differences in scanpaths while performing MTT tasks. However, it is unclear if there is a winning scan pattern that would lead to a successful tracking of targets. To answer this question, we used dynamic time warping to compare the similarities between subjects’ scan patterns during an MTT task with different challenges. We studied the effect of set-size, occlusion, and trial response on the similarities. Then a mixed effect analysis was applied on the output to measure whether the findings were statistically significant. Results demonstrated that scan patterns were more similar when MTT task was performed correctly. It suggests that there is a common tracking strategy adopted by the viewers that leads to a correct response. Decoding this strategy has countless applications in the fields including human-computer interaction, brain-modeling and cognitive info-communication.
Various depth estimation models are now widely used on many mobile and IoT devices for image segmentation, bokeh effect rendering, object tracking and many other mobile tasks. Thus, it is very crucial to have efficien...
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This work reviews the results of the NTIRE 2023 Challenge on image Shadow Removal. The described set of solutions were proposed for a novel dataset, which captures a wide range of object-light interactions. It consist...
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In this work, the seasonal predictive capabilities of Neural Radiance Fields (NeRF) applied to satellite images are investigated. Focusing on the utilization of satellite data, the study explores how Sat-NeRF, a novel...
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