Recent Advances in the Applications of Convolutional Neural Networks to Medical Image Contour Detection

Zhang, Zizhao; Xing, Fuyong; Su, Hua; Shi, Xiaoshuang; Yang, Lin

doi:10.48550/arxiv.1708.07281

Cited by 5 publications

(5 citation statements)

References 214 publications

(304 reference statements)

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“…Data is transformed into tasks (results of classification) based on learned features over the convolutional neural network (CNN) [31]. As the overall performance of the classification procedures in the contemporary deep learning mechanisms outperforms average human accuracy [32], it was of the utmost importance to empower the designed algorithm by means of the AlexNET module for the MATLAB environment. As the currentstate performance of both hardware and software solutions creates a firm layer in terms of deep learning enablers (i.e., acceleration of the flow with the use of Graphics Processing Units-GPUs [33], massive labeled data sources, and pre-trained models), the selected approach was based on a deep neural network fine-tuned with a pre-trained model [34].…”

Section: Efficiency Features Valuementioning

confidence: 99%

Estimating the Measurement Uncertainty of the Number of Vehicles in a Car Park Using an Indirect Method

et al. 2023

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The main problem with counting vehicles in rest areas in Europe and America is the overflowing of parking lots in the rest areas. Thus it is impossible to count parked vehicles with traditional, direct methods, which use cameras and Lidar (light detection and ranging) to detect the presence of vehicles in individual parking spots. The solution to this problem may be an indirect method which uses cameras to count the vehicles entering and leaving the rest area and which sorts the vehicles into categories. This article introduces a method for determining car park occupancy in rest areas using indirect measurement and evaluates the uncertainty of this method for determining the occupancy. This indirect method counts the vehicles entering and leaving the car park and sorts the vehicles into categories. The difference between the number of vehicles that entered and the number of vehicles that left in a given time gives the number of vehicles occupying the car park, i.e., the parking space demand for a given time. This demand will vary over time. In order to register the vehicles entering and leaving, mains-free supply video cameras were installed next to the entrance and exit. The counting and categorizing could be conducted organoleptically, semi-automatically, or using an image computer analysis using artificial intelligence networks. Because the difference between the number of vehicles entering and leaving is calculated over a long period, a relative error (regarding car park capacity) might be grave even when the counting errors are minor. The authors will show how the certainty of this indirect counting method can be improved.

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Section: Efficiency Features Valuementioning

confidence: 99%

Estimating the Measurement Uncertainty of the Number of Vehicles in a Car Park Using an Indirect Method

et al. 2023

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“…Computer-aided diagnosis not only can ease the burden on pathologists', but also benefit to eliminate the misdiagnosis rate and inconsistency between different observers. Convolution neural networks (CNNs) already been extensively used for numerous imaging process including histopathological slides because of its efficient structure in deep learning [42,2,23,17]. Some investigations have proved CNNs as promising tools for the detections of whole slide images (WSIs) which are extremely large [17,22,37].…”

Section: Tinementioning

confidence: 99%

Computer-aided Detection of Squamous Carcinoma of the Cervix in Whole Slide Images

Tian,

Yang,

Wang

et al. 2019

Preprint

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Goal: Squamous cell carcinoma of cervix is one of the most prevalent cancer worldwide in females. Traditionally, the most indispensable diagnosis of cervix squamous carcinoma is histopathological assessment which is achieved under microscope by pathologist. However, human evaluation of pathology slide is highly depending on the experience of pathologist, thus big inter-and intra-observer variability exists. Digital pathology, in combination with deep learning provides an opportunity to improve the objectivity and efficiency of histopathologic slide analysis. Methods: In this study, we obtained 800 haematoxylin and eosin stained slides from 300 patients suffered from cervix squamous carcinoma. Based on information from morphological heterogeneity in the tumor and its adjacent area, we established deep learning models using popular convolution neural network architectures (inception-v3, InceptionResnet-v2 and Resnet50). Then random forest was introduced to feature extractions and slide-based classification. Results: The overall performance of our proposed models on slide-based tumor discrimination were outstanding with an AUC scores ¿ 0.94. While, location identifications of lesions in whole slide images were mediocre (FROC scores ¿ 0.52) duo to the extreme complexity of tumor tissues. Conclusion: For the first time, our analysis workflow highlighted a quantitative visual-based slide analysis of cervix squamous carcinoma. Significance: This study demonstrates a pathway to assist pathologist and accelerate the diagnosis of patients by utilizing new computational approaches.

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“…Recently, one of the most popular techniques for model compression is knowledge distillation (KD). The basic idea of KD is to compress the model by transferring (Zhang et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Area-wise relational knowledge distillation

Cho,

Park,

Lim

2023

Communications for Statistical Applications and Methods

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Knowledge distillation (KD) refers to extracting knowledge from a large and complex model (teacher) and transferring it to a relatively small model (student). This can be done by training the teacher model to obtain the activation function values of the hidden or the output layers and then retraining the student model using the same training data with the obtained values. Recently, relational KD (RKD) has been proposed to extract knowledge about relative differences in training data. This method improved the performance of the student model compared to conventional KDs. In this paper, we propose a new method for RKD by introducing a new loss function for RKD. The proposed loss function is defined using the area difference between the teacher model and the student model in a specific hidden layer, and it is shown that the model can be successfully compressed, and the generalization performance of the model can be improved. We demonstrate that the accuracy of the model applying the method proposed in the study of model compression of audio data is up to 1.8% higher than that of the existing method. For the study of model generalization, we demonstrate that the model has up to 0.5% better performance in accuracy when introducing the RKD method to self-KD using image data.

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Recent Advances in the Applications of Convolutional Neural Networks to Medical Image Contour Detection

Cited by 5 publications

References 214 publications

Estimating the Measurement Uncertainty of the Number of Vehicles in a Car Park Using an Indirect Method

Estimating the Measurement Uncertainty of the Number of Vehicles in a Car Park Using an Indirect Method

Computer-aided Detection of Squamous Carcinoma of the Cervix in Whole Slide Images

Area-wise relational knowledge distillation

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