Background Breast cancer has the highest prevalence among all cancers in women globally.The classification of histopathological images in the diagnosis of breast cancers is an area of clinical concern.In computer-aide...Background Breast cancer has the highest prevalence among all cancers in women globally.The classification of histopathological images in the diagnosis of breast cancers is an area of clinical concern.In computer-aided diagnosis,most traditional classification models use a single network to extract features,although this approach has significant limitations.Moreover,many networks are trained and optimized on patient-level datasets,ignoring lower-level data labels.Methods This paper proposed a deep ensemble model based on image-level labels for the binary classification of breast histopathological images of benign and malignant lesions.First,the BreaKHis dataset was randomly divided into training,validation,and test sets.Then,data augmentation techniques were used to balance the numbers of benign and malignant samples.Third,based on their transfer learning performance and the complementarity between networks,VGG16,Xception,ResNet50,and DenseNet201 were selected as base classifiers.Results In a ensemble network model with accuracy as the weight,the image-level binary classification achieved an accuracy of 98.90%.To verify the capabilities of our method,it was experimentally compared with the latest transformer and multilayer perception(MLP)models on the same dataset.Our ensemble model showed a 5%-20%advantage,emphasizing its far-reaching abilities in classification tasks.Conclusions This research focuses on improving the performance of a classification model with an ensemble algorithm.Transfer learning has an essential role in classification of small datasets,improving training speed and accuracy.Our model may outperform many existing approaches with respect to accuracy and has applications in the field of auxiliary medical diagnosis.展开更多
This research work develops new and better prognostic markers for predicting Childhood MedulloBlastoma(CMB)using a well-defined deep learning architecture.A deep learning architecture could be designed using ideas fro...This research work develops new and better prognostic markers for predicting Childhood MedulloBlastoma(CMB)using a well-defined deep learning architecture.A deep learning architecture could be designed using ideas from image processing and neural networks to predict CMB using histopathological images.First,a convolution process transforms the histopathological image into deep features that uniquely describe it using different two-dimensional filters of various sizes.A 10-layer deep learning architecture is designed to extract deep features.The introduction of pooling layers in the architecture reduces the feature dimension.The extracted and dimension-reduced deep features from the arrangement of convolution layers and pooling layers are used to classify histopathological images using a neural network classifier.The performance of the CMB classification system is evaluated using 1414(10×magnification)and 1071(100×magnification)augmented histopathological images with five classes of CMB such as desmoplastic,nodular,large cell,classic,and normal.Experimental results show that the average classification accuracy of 99.38%(10×)and 99.07%(100×)is attained by the proposed CNB classification system.展开更多
Early diagnosis of breast cancer,the most common disease among women around the world,increases the chance of treatment and is highly important.Nuclear atypia grading in histopathological images plays an important rol...Early diagnosis of breast cancer,the most common disease among women around the world,increases the chance of treatment and is highly important.Nuclear atypia grading in histopathological images plays an important role in the final diagnosis and grading ofbreast cancer.Grading images by pathologists is a time consuming and subjective task.Therefore,the existence of a computer-aided system for nuclear atypia grading is very useful and necessary;In this stud%two automatic systems for grading nuclear atypia in breast cancer histopathological images based on deep learning methods are proposed.A patch-based approach is introduced due to the large size of the histopathological images and restriction of the training data.In the proposed system I,the most important patches in the image are detected first and then a three-hidden-layer convolutional neural network(CNN)is designed and trained for feature extraction and to classify the patches individually.The proposed system II is based on a combination of the CNN for feature extraction and a two-layer Long short-term memoty(LSTM)network for classification.The LSTM network is utilised to consider all patches of an image simultaneously for image grading.The simulation results show the efficiency of the proposed systems for automatic nuclear atypia grading and outperform the current related studies in the literature.展开更多
提出一种基于方向梯度直方图(histograms of oriented gradient,HOG)特征和滑动窗口的细胞检测方法,能快速、高效、准确地检测高分辨率病理组织图像中的细胞。该检测算法首先对训练集中的细胞样本块和非细胞样本块提取HOG特征,然后运用...提出一种基于方向梯度直方图(histograms of oriented gradient,HOG)特征和滑动窗口的细胞检测方法,能快速、高效、准确地检测高分辨率病理组织图像中的细胞。该检测算法首先对训练集中的细胞样本块和非细胞样本块提取HOG特征,然后运用HOG特征训练分类器。训练好的分类器用于在整幅病理图像中自动检测细胞。先运用滑动窗的方法在整幅高分辨率病理图像中选取相同尺寸的所有可能的细胞块,被滑动窗选定的图像块提取HOG特征后,送到训练好的分类器中判断是否是细胞块。为了验证提出方法的有效性,将此方法运用于17名乳腺患者的共37张H&E(hematoxylin&eosin)染色高分辨率穿刺切片病理图像上自动检测细胞,通过与softmax(SM)分类器、稀疏自编码器+SM、局部二值模式+SM、支持向量机(support vector machine,SVM)、HOG+SVM、以及HOG+SVM多个模型对细胞检测的准确率、召回率以及综合评价指标的对比表明,本研究提出的方法分别为71.5%,82.3%和76.5%,具有更高的准确率。展开更多
基金supported by the National Natural Science Foundation of China(Grant No.61806047).
文摘Background Breast cancer has the highest prevalence among all cancers in women globally.The classification of histopathological images in the diagnosis of breast cancers is an area of clinical concern.In computer-aided diagnosis,most traditional classification models use a single network to extract features,although this approach has significant limitations.Moreover,many networks are trained and optimized on patient-level datasets,ignoring lower-level data labels.Methods This paper proposed a deep ensemble model based on image-level labels for the binary classification of breast histopathological images of benign and malignant lesions.First,the BreaKHis dataset was randomly divided into training,validation,and test sets.Then,data augmentation techniques were used to balance the numbers of benign and malignant samples.Third,based on their transfer learning performance and the complementarity between networks,VGG16,Xception,ResNet50,and DenseNet201 were selected as base classifiers.Results In a ensemble network model with accuracy as the weight,the image-level binary classification achieved an accuracy of 98.90%.To verify the capabilities of our method,it was experimentally compared with the latest transformer and multilayer perception(MLP)models on the same dataset.Our ensemble model showed a 5%-20%advantage,emphasizing its far-reaching abilities in classification tasks.Conclusions This research focuses on improving the performance of a classification model with an ensemble algorithm.Transfer learning has an essential role in classification of small datasets,improving training speed and accuracy.Our model may outperform many existing approaches with respect to accuracy and has applications in the field of auxiliary medical diagnosis.
文摘This research work develops new and better prognostic markers for predicting Childhood MedulloBlastoma(CMB)using a well-defined deep learning architecture.A deep learning architecture could be designed using ideas from image processing and neural networks to predict CMB using histopathological images.First,a convolution process transforms the histopathological image into deep features that uniquely describe it using different two-dimensional filters of various sizes.A 10-layer deep learning architecture is designed to extract deep features.The introduction of pooling layers in the architecture reduces the feature dimension.The extracted and dimension-reduced deep features from the arrangement of convolution layers and pooling layers are used to classify histopathological images using a neural network classifier.The performance of the CMB classification system is evaluated using 1414(10×magnification)and 1071(100×magnification)augmented histopathological images with five classes of CMB such as desmoplastic,nodular,large cell,classic,and normal.Experimental results show that the average classification accuracy of 99.38%(10×)and 99.07%(100×)is attained by the proposed CNB classification system.
文摘Early diagnosis of breast cancer,the most common disease among women around the world,increases the chance of treatment and is highly important.Nuclear atypia grading in histopathological images plays an important role in the final diagnosis and grading ofbreast cancer.Grading images by pathologists is a time consuming and subjective task.Therefore,the existence of a computer-aided system for nuclear atypia grading is very useful and necessary;In this stud%two automatic systems for grading nuclear atypia in breast cancer histopathological images based on deep learning methods are proposed.A patch-based approach is introduced due to the large size of the histopathological images and restriction of the training data.In the proposed system I,the most important patches in the image are detected first and then a three-hidden-layer convolutional neural network(CNN)is designed and trained for feature extraction and to classify the patches individually.The proposed system II is based on a combination of the CNN for feature extraction and a two-layer Long short-term memoty(LSTM)network for classification.The LSTM network is utilised to consider all patches of an image simultaneously for image grading.The simulation results show the efficiency of the proposed systems for automatic nuclear atypia grading and outperform the current related studies in the literature.
