Since the fully convolutional network has achieved great success in semantic segmentation,lots of works have been proposed to extract discriminative pixel representations.However,the authors observe that existing meth...Since the fully convolutional network has achieved great success in semantic segmentation,lots of works have been proposed to extract discriminative pixel representations.However,the authors observe that existing methods still suffer from two typical challenges:(i)The intra-class feature variation between different scenes may be large,leading to the difficulty in maintaining the consistency between same-class pixels from different scenes;(ii)The inter-class feature distinction in the same scene could be small,resulting in the limited performance to distinguish different classes in each scene.The authors first rethink se-mantic segmentation from a perspective of similarity between pixels and class centers.Each weight vector of the segmentation head represents its corresponding semantic class in the whole dataset,which can be regarded as the embedding of the class center.Thus,the pixel-wise classification amounts to computing similarity in the final feature space between pixels and the class centers.Under this novel view,the authors propose a Class Center Similarity(CCS)layer to address the above-mentioned challenges by generating adaptive class centers conditioned on each scenes and supervising the similarities between class centers.The CCS layer utilises the Adaptive Class Center Module to generate class centers conditioned on each scene,which adapt the large intra-class variation between different scenes.Specially designed Class Distance Loss(CD Loss)is introduced to control both inter-class and intra-class distances based on the predicted center-to-center and pixel-to-center similarity.Finally,the CCS layer outputs the processed pixel-to-center similarity as the segmentation prediction.Extensive experiments demonstrate that our model performs favourably against the state-of-the-art methods.展开更多
Deep learning (DL) has experienced an exponential development in recent years, with major impact in many medical fields, especially in the field of medical image and, respectively, as a specific task, in the segmentat...Deep learning (DL) has experienced an exponential development in recent years, with major impact in many medical fields, especially in the field of medical image and, respectively, as a specific task, in the segmentation of the medical image. We aim to create a computer assisted diagnostic method, optimized by the use of deep learning (DL) and validated by a randomized controlled clinical trial, is a highly automated tool for diagnosing and staging precancerous and cervical cancer and thyroid cancers. We aim to design a high-performance deep learning model, combined from convolutional neural network (U-Net)-based architectures, for segmentation of the medical image that is independent of the type of organs/tissues, dimensions or type of image (2D/3D) and to validate the DL model in a randomized, controlled clinical trial. We used as a methodology primarily the analysis of U-Net-based architectures to identify the key elements that we considered important in the design and optimization of the combined DL model, from the U-Net-based architectures, imagined by us. Secondly, we will validate the performance of the DL model through a randomized controlled clinical trial. The DL model designed by us will be a highly automated tool for diagnosing and staging precancers and cervical cancer and thyroid cancers. The combined model we designed takes into account the key features of each of the architectures Overcomplete Convolutional Network Kite-Net (Kite-Net), Attention gate mechanism is an improvement added on convolutional network architecture for fast and precise segmentation of images (Attention U-Net), Harmony Densely Connected Network-Medical image Segmentation (HarDNet-MSEG). In this regard, we will create a comprehensive computer assisted diagnostic methodology validated by a randomized controlled clinical trial. The model will be a highly automated tool for diagnosing and staging precancers and cervical cancer and thyroid cancers. This would help drastically minimize the time and effort that specialists put into analyzing medical images, help to achieve a better therapeutic plan, and can provide a “second opinion” of computer assisted diagnosis.展开更多
in this poper a novel data-and rule-driven system for 3D scene description and segmentation inan unknown environment is presented.This system generatss hierachies of features that correspond tostructural elements such...in this poper a novel data-and rule-driven system for 3D scene description and segmentation inan unknown environment is presented.This system generatss hierachies of features that correspond tostructural elements such as boundaries and shape classes of individual object as well as relationshipsbetween objects.It is implemented as an added high-level component to an existing low-level binocularvision system[1]. Based on a pair of matched stereo images produced by that system,3D segmentation is firstperformed to group object boundary data into several edge-sets,each of which is believed to belong to aparticular object.Then gross features of each object are extracted and stored in an object recbrd.The finalstructural description of the scene is accomplished with information in the object record,a set of rules and arule implementor. The System is designed to handle partially occluded objects of different shapes and sizeson the 2D imager.Experimental results have shown its success in computing both object and structurallevel descriptions of common man-made objects.展开更多
研究一种电视图像目标实时分割和识别算法.在二维图像不变矩和相对矩的基础上,进一步组合优化得出4个不变矩,结合复数矩、圆方差和椭圆方差组成目标特征向量,利用k-近邻法实现目标的识别和分类.图像分割采用改进矩不变阈值分割和基于梯...研究一种电视图像目标实时分割和识别算法.在二维图像不变矩和相对矩的基础上,进一步组合优化得出4个不变矩,结合复数矩、圆方差和椭圆方差组成目标特征向量,利用k-近邻法实现目标的识别和分类.图像分割采用改进矩不变阈值分割和基于梯度的自适应阈值分割提取目标.仿真实验表明,提取的目标特征量对于平移、缩放和旋转均能保持较好的不变性.用该分割算法分割的图像边缘清晰,分割时间为8 m s,易于硬件实现.展开更多
基金Hubei Provincial Natural Science Foundation of China,Grant/Award Number:2022CFA055National Natural Science Foundation of China,Grant/Award Number:62176097。
文摘Since the fully convolutional network has achieved great success in semantic segmentation,lots of works have been proposed to extract discriminative pixel representations.However,the authors observe that existing methods still suffer from two typical challenges:(i)The intra-class feature variation between different scenes may be large,leading to the difficulty in maintaining the consistency between same-class pixels from different scenes;(ii)The inter-class feature distinction in the same scene could be small,resulting in the limited performance to distinguish different classes in each scene.The authors first rethink se-mantic segmentation from a perspective of similarity between pixels and class centers.Each weight vector of the segmentation head represents its corresponding semantic class in the whole dataset,which can be regarded as the embedding of the class center.Thus,the pixel-wise classification amounts to computing similarity in the final feature space between pixels and the class centers.Under this novel view,the authors propose a Class Center Similarity(CCS)layer to address the above-mentioned challenges by generating adaptive class centers conditioned on each scenes and supervising the similarities between class centers.The CCS layer utilises the Adaptive Class Center Module to generate class centers conditioned on each scene,which adapt the large intra-class variation between different scenes.Specially designed Class Distance Loss(CD Loss)is introduced to control both inter-class and intra-class distances based on the predicted center-to-center and pixel-to-center similarity.Finally,the CCS layer outputs the processed pixel-to-center similarity as the segmentation prediction.Extensive experiments demonstrate that our model performs favourably against the state-of-the-art methods.
文摘Deep learning (DL) has experienced an exponential development in recent years, with major impact in many medical fields, especially in the field of medical image and, respectively, as a specific task, in the segmentation of the medical image. We aim to create a computer assisted diagnostic method, optimized by the use of deep learning (DL) and validated by a randomized controlled clinical trial, is a highly automated tool for diagnosing and staging precancerous and cervical cancer and thyroid cancers. We aim to design a high-performance deep learning model, combined from convolutional neural network (U-Net)-based architectures, for segmentation of the medical image that is independent of the type of organs/tissues, dimensions or type of image (2D/3D) and to validate the DL model in a randomized, controlled clinical trial. We used as a methodology primarily the analysis of U-Net-based architectures to identify the key elements that we considered important in the design and optimization of the combined DL model, from the U-Net-based architectures, imagined by us. Secondly, we will validate the performance of the DL model through a randomized controlled clinical trial. The DL model designed by us will be a highly automated tool for diagnosing and staging precancers and cervical cancer and thyroid cancers. The combined model we designed takes into account the key features of each of the architectures Overcomplete Convolutional Network Kite-Net (Kite-Net), Attention gate mechanism is an improvement added on convolutional network architecture for fast and precise segmentation of images (Attention U-Net), Harmony Densely Connected Network-Medical image Segmentation (HarDNet-MSEG). In this regard, we will create a comprehensive computer assisted diagnostic methodology validated by a randomized controlled clinical trial. The model will be a highly automated tool for diagnosing and staging precancers and cervical cancer and thyroid cancers. This would help drastically minimize the time and effort that specialists put into analyzing medical images, help to achieve a better therapeutic plan, and can provide a “second opinion” of computer assisted diagnosis.
文摘in this poper a novel data-and rule-driven system for 3D scene description and segmentation inan unknown environment is presented.This system generatss hierachies of features that correspond tostructural elements such as boundaries and shape classes of individual object as well as relationshipsbetween objects.It is implemented as an added high-level component to an existing low-level binocularvision system[1]. Based on a pair of matched stereo images produced by that system,3D segmentation is firstperformed to group object boundary data into several edge-sets,each of which is believed to belong to aparticular object.Then gross features of each object are extracted and stored in an object recbrd.The finalstructural description of the scene is accomplished with information in the object record,a set of rules and arule implementor. The System is designed to handle partially occluded objects of different shapes and sizeson the 2D imager.Experimental results have shown its success in computing both object and structurallevel descriptions of common man-made objects.
文摘研究一种电视图像目标实时分割和识别算法.在二维图像不变矩和相对矩的基础上,进一步组合优化得出4个不变矩,结合复数矩、圆方差和椭圆方差组成目标特征向量,利用k-近邻法实现目标的识别和分类.图像分割采用改进矩不变阈值分割和基于梯度的自适应阈值分割提取目标.仿真实验表明,提取的目标特征量对于平移、缩放和旋转均能保持较好的不变性.用该分割算法分割的图像边缘清晰,分割时间为8 m s,易于硬件实现.
