A Comprehensive Review for Breast Histopathology Image Analysis Using Classical and Deep Neural Networks

Xiao-min, Zhou; Li, Chen; Rahaman, Md Mamunur; Yao, Yu-Dong; Ai, Shiliang; Sun, Changhao; Wang, Qian; Zhang, Yong; Li, Mo; Li, Xiaoyan; Jiang, Tao; Dong, Xue; Qi, Shouliang; Teng, Yueyang

doi:10.1109/access.2020.2993788

Cited by 160 publications

(88 citation statements)

References 155 publications

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“…19 An example of CIN region clustering results. The blue color denotes the CIN lesion regions possible to be used for other microscopic image analysis tasks, such as histopathology image analysis [36,37,49] and cell image analysis [20,21].…”

Section: Discussionmentioning

confidence: 99%

A Cervical Histopathology Image Clustering Approach Using Graph Based Features

Chen

et al. 2021

SN COMPUT. SCI.

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To apply topological information to solve a cervical histopathology image clustering (CHIC) problem, a graph based unsupervised learning (GBUL) approach is proposed in this paper. First, the GBUL method applies color features and k-means clustering to carry out a first-stage "coarse" clustering. Then, a skeletonization based node generation (SBNG) approach is introduced to approximate the distribution of cervical cell nuclei. Thirdly, based on the SBNG nodes, multiple graphs are constructed. Next, graph features are extracted based on the constructed graphs. Finally, k-means clustering is used again for the second-stage clustering. In the experiment, a practical Hematoxylin-eosin staining cervical histopathology image dataset with 40 whole-slide imaging images is tested, obtaining a promising CHIC result and showing a huge potential in the cancer risk prediction field.

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Section: Discussionmentioning

confidence: 99%

A Cervical Histopathology Image Clustering Approach Using Graph Based Features

Chen

et al. 2021

SN COMPUT. SCI.

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“…For broader and faster diagnosis of medical images, engineers have tried to adapt deep learning models to medical image analysis [20], [21]. The U-Net [22], which consists of contracting and expanding paths, has shown outstanding biomedical image segmentation performance.…”

Section: Medical Image Segmentationmentioning

confidence: 99%

Histological Image Segmentation and Classification Using Entropy-Based Convolutional Module

Kim

Lim

et al. 2021

IEEE Access

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As the powerful performance of deep learning has been proven, many computer vision researchers have applied deep learning methods to their works as a breakthrough that could not be achieved with conventional computer vision algorithms. Particularly in pathological image analysis, deep learning plays an important role because some diagnosis requires a considerable cost or much time. In a recent, convolutional neural network (CNN)-based deep learning models have shown meaningful results in pathological image analysis, reducing time and cost. However, existing CNN-based segmentation models perform the same convolution operation for all channels of a feature map. It could be an inefficient operation according to information theory. We propose (Shannon) entropy-based convolutional module (ECM) for efficient convolutional operation in terms of a communication system. The fundamental coding manner of a communication system based on information theory is to allocate fewer bits for data showing the high probability of occurrence, and vice versa. Following up this coding manner, a feature is divided into dominant and recessive features according to the channel importance calculated from the channel attention module, and a heavy operation is conducted on the recessive feature and a light operation is conducted on the dominant feature. This operating manner can make a network perform efficient calculations and improve its performance. Furthermore, our proposed module is a portable unit, thus it can be a replacement of any convolution without modification of the whole architecture. To the best of our knowledge, our proposed module is the first trial to mimic the coding manner of information theory. The models equipped with our proposed module outperform the original models achieving 0.855 of F1 score and 0.832 of Jaccard score on colorectal cancer (CRC) image data-set.INDEX TERMS Segmentation, deep learning, information theory, Shannon entropy, colorectal cancer image.

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“…Technology and AI applications can be applied in many different sectors and industries to generate maximum production from the operational front. Artificial neural networks are one of the recent trends used in AI applications such as communication [38], wind power prediction [39], [40], text classification [41], civil engineering [42], health [43], image processing [44], climate prediction [45], [46], [47], and power load forecasting [48]. One of the most important features of ANN is its ability to recognize time series data and predict data with high efficiency compared to other methods, especially nonlinear relationships.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Rainfall Yearly Prediction based on Autoregressive Artificial Neural Networks Model in Central Jordan using Data Records: 1938-2018

Sharadqah¹,

Mohammad²,

Marbello³

et al. 2021

IJACSA

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Jordan is suffering a chronicle water resources shortage. Rainfall is the real input for all water resources in the country. Acceptable accuracy of rainfall prediction is of great importance in order to manage water resources and climate change issues. The actual study include the analysis of time series trends of climate change regards to rainfall parameter. Available rainfall data for five stations from central Jordan where obtained from the Ministry of water and irrigation that cover the interval 1938-2018. Data have been analyzed using Nonlinear Autoregressive Artificial Neural Networks NAR-ANN) based on Levenberg-Marquardt algorithm. The NAR model tested the rainfall data using one input layer, one hidden layer and one output layer with a different combinations of number of neuron in hidden layer and epochs. The best combination was using 25 neurons and 12 epochs. The classification performance or the quality of result is measured by mean square error (MSE). For all the meteorological stations, the MSE values were negligible ranging between 4.32*10 -4 and 1.83*10 -5 . The rainfall prediction result show that forecasting rainfall values in the base of calendar year are almost identical with those estimated for seasonal year when dealing with long record of years. The average predicted rainfall values for the coming ten-year in comparison with long-term rainfall average show; strong decline for Dana station, some decrees for Rashadia station, huge increase in Abur station, and relatively limited change between predicted and long-term average for Busira and Muhai Stations.

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A Comprehensive Review for Breast Histopathology Image Analysis Using Classical and Deep Neural Networks

Cited by 160 publications

References 155 publications

A Cervical Histopathology Image Clustering Approach Using Graph Based Features

A Cervical Histopathology Image Clustering Approach Using Graph Based Features

Histological Image Segmentation and Classification Using Entropy-Based Convolutional Module

Nonlinear Rainfall Yearly Prediction based on Autoregressive Artificial Neural Networks Model in Central Jordan using Data Records: 1938-2018

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