scholarly journals A review and comparison of breast tumor cell nuclei segmentation performances using deep convolutional neural networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Andrew Lagree ◽  
Majidreza Mohebpour ◽  
Nicholas Meti ◽  
Khadijeh Saednia ◽  
Fang-I. Lu ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Breast Tissue ◽  
Superior Performance ◽  
Histopathological Analysis ◽  
Cell Nuclei ◽  
Deep Convolutional Neural Networks ◽  
Nuclei Segmentation ◽  
Histopathological Images ◽  
Biopsy Examination

AbstractBreast cancer is currently the second most common cause of cancer-related death in women. Presently, the clinical benchmark in cancer diagnosis is tissue biopsy examination. However, the manual process of histopathological analysis is laborious, time-consuming, and limited by the quality of the specimen and the experience of the pathologist. This study's objective was to determine if deep convolutional neural networks can be trained, with transfer learning, on a set of histopathological images independent of breast tissue to segment tumor nuclei of the breast. Various deep convolutional neural networks were evaluated for the study, including U-Net, Mask R-CNN, and a novel network (GB U-Net). The networks were trained on a set of Hematoxylin and Eosin (H&E)-stained images of eight diverse types of tissues. GB U-Net demonstrated superior performance in segmenting sites of invasive diseases (AJI = 0.53, mAP = 0.39 & AJI = 0.54, mAP = 0.38), validated on two hold-out datasets exclusively containing breast tissue images of approximately 7,582 annotated cells. The results of the networks, trained on images independent of breast tissue, demonstrated that tumor nuclei of the breast could be accurately segmented.

scholarly journals Deep convolutional neural networks for cardiovascular vulnerable plaque detection

2019 ◽  
Vol 277 ◽  
pp. 02024 ◽  
Author(s):  
Lincan Li ◽  
Tong Jia ◽  
Tianqi Meng ◽  
Yizhe Liu
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Vulnerable Plaque ◽  
Recall Rate ◽  
Superior Performance ◽  
Learning Approaches ◽  
Deep Convolutional Neural Networks ◽  
Vulnerable Plaques ◽  
Plaque Detection

In this paper, an accurate two-stage deep learning method is proposed to detect vulnerable plaques in ultrasonic images of cardiovascular. Firstly, a Fully Convonutional Neural Network (FCN) named U-Net is used to segment the original Intravascular Optical Coherence Tomography (IVOCT) cardiovascular images. We experiment on different threshold values to find the best threshold for removing noise and background in the original images. Secondly, a modified Faster RCNN is adopted to do precise detection. The modified Faster R-CNN utilize six-scale anchors (122,162,322,642,1282,2562) instead of the conventional one scale or three scale approaches. First, we present three problems in cardiovascular vulnerable plaque diagnosis, then we demonstrate how our method solve these problems. The proposed method in this paper apply deep convolutional neural networks to the whole diagnostic procedure. Test results show the Recall rate, Precision rate, IoU (Intersection-over-Union) rate and Total score are 0.94, 0.885, 0.913 and 0.913 respectively, higher than the 1st team of CCCV2017 Cardiovascular OCT Vulnerable Plaque Detection Challenge. AP of the designed Faster RCNN is 83.4%, higher than conventional approaches which use one-scale or three-scale anchors. These results demonstrate the superior performance of our proposed method and the power of deep learning approaches in diagnose cardiovascular vulnerable plaques.

scholarly journals Classification for Avian Malaria Parasite Plasmodium Gallinaceum Blood Stages by Using Deep Convolutional Neural Networks

2021 ◽  
Author(s):  
Veerayuth Kittichai ◽  
Morakot Kaewthamasorn ◽  
Suchansa Thanee ◽  
Rangsan Jomtarak ◽  
Kamonpob Klanboot ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Malaria Parasite ◽  
Avian Malaria ◽  
Superior Performance ◽  
Rapid Screening ◽  
Plasmodium Gallinaceum ◽  
Deep Convolutional Neural Networks ◽  
Avian Malaria Parasite

Abstract Background: The infections of an avian malaria parasite (Plasmodium gallinaceum) in domestic chickens presents a major threat to poultry industry because it cause economical loss in both quality and quantity of meat and egg productions. Deep learning algorithms have been developed to identify avian malaria infections and classify its blood stage development. Methods: In this study, four types of deep convolutional neural networks namely Darknet, Darknet19, darknet19_448x448 and Densenet 201 are used to classify P. gallinaceum blood stages. We randomly collected dataset of 10,548 single-cell images consisting of four parasite stages from ten-infected blood films stained by Giemsa. All images were confirmed by three well-trained examiners. Results: In the model-wise comparison, the four neural network models gave us high values in the mean average precision at least 95%. Darknet can reproduce a superior performance in classification of the P. gallinaceum development stages across any other model architectures. In addition, Darknet also has best performance in multiple class-wise classification, scoring the average values of greater than 99% in accuracy, specificity, sensitivity, precision, and F1-score.Conclusions: Therefore, Darknet model is more suitable in the classification of P. gallinaceum blood stages than the other three models. The result may contribute us to develop the rapid screening tool for further assist non-expert in filed study where is lack of specific instrument for avian malaria diagnostic.

scholarly journals Handwritten Bangla Character Recognition Using the State-of-the-Art Deep Convolutional Neural Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Md Zahangir Alom ◽  
Paheding Sidike ◽  
Mahmudul Hasan ◽  
Tarek M. Taha ◽  
Vijayan K. Asari
Keyword(s):  
Neural Networks ◽  
Object Recognition ◽  
Convolutional Neural Networks ◽  
Character Recognition ◽  
State Of The Art ◽  
The State ◽  
Superior Performance ◽  
Deep Convolutional Neural Networks ◽  
Practical Applications ◽  
High Degree

In spite of advances in object recognition technology, handwritten Bangla character recognition (HBCR) remains largely unsolved due to the presence of many ambiguous handwritten characters and excessively cursive Bangla handwritings. Even many advanced existing methods do not lead to satisfactory performance in practice that related to HBCR. In this paper, a set of the state-of-the-art deep convolutional neural networks (DCNNs) is discussed and their performance on the application of HBCR is systematically evaluated. The main advantage of DCNN approaches is that they can extract discriminative features from raw data and represent them with a high degree of invariance to object distortions. The experimental results show the superior performance of DCNN models compared with the other popular object recognition approaches, which implies DCNN can be a good candidate for building an automatic HBCR system for practical applications.

scholarly journals Rectified Binary Convolutional Networks for Enhancing the Performance of 1-bit DCNNs

2019 ◽  
Author(s):  
Chunlei Liu ◽  
Wenrui Ding ◽  
Xin Xia ◽  
Yuan Hu ◽  
Baochang Zhang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Superior Performance ◽  
Feature Maps ◽  
Unified Framework ◽  
Deep Convolutional Neural Networks ◽  
Convolutional Networks ◽  
Computation Efficiency ◽  
Significant Performance ◽  
Binary Network

Binarized  convolutional neural networks (BCNNs) are widely used to improve memory and computation efficiency of deep convolutional neural networks (DCNNs) for mobile and AI chips based applications. However, current BCNNs are not able to fully explore their corresponding full-precision models, causing a significant performance gap between them. In this paper, we propose rectified binary convolutional networks (RBCNs), towards optimized BCNNs, by combining full-precision kernels and feature maps to rectify the binarization process in a unified framework. In particular, we use a GAN to train the 1-bit binary network with the guidance of its corresponding full-precision model, which significantly improves the performance of BCNNs. The rectified convolutional layers are generic and flexible, and can be easily incorporated into existing DCNNs such as WideResNets and ResNets. Extensive experiments demonstrate the superior performance of the proposed RBCNs over state-of-the-art BCNNs. In particular, our method shows strong generalization on the object tracking task.

scholarly journals An automatic nuclei segmentation method based on deep convolutional neural networks for histopathology images

2019 ◽  
Vol 1 (1) ◽  
Author(s):  
Hwejin Jung ◽  
Bilal Lodhi ◽  
Jaewoo Kang
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
State Of The Art ◽  
Gaussian Mixture ◽  
Color Variation ◽  
Post Processing ◽  
Segmentation Method ◽  
Deep Convolutional Neural Networks ◽  
Nuclei Segmentation ◽  
Histopathology Images

Abstract Background Since nuclei segmentation in histopathology images can provide key information for identifying the presence or stage of a disease, the images need to be assessed carefully. However, color variation in histopathology images, and various structures of nuclei are two major obstacles in accurately segmenting and analyzing histopathology images. Several machine learning methods heavily rely on hand-crafted features which have limitations due to manual thresholding. Results To obtain robust results, deep learning based methods have been proposed. Deep convolutional neural networks (DCNN) used for automatically extracting features from raw image data have been proven to achieve great performance. Inspired by such achievements, we propose a nuclei segmentation method based on DCNNs. To normalize the color of histopathology images, we use a deep convolutional Gaussian mixture color normalization model which is able to cluster pixels while considering the structures of nuclei. To segment nuclei, we use Mask R-CNN which achieves state-of-the-art object segmentation performance in the field of computer vision. In addition, we perform multiple inference as a post-processing step to boost segmentation performance. We evaluate our segmentation method on two different datasets. The first dataset consists of histopathology images of various organ while the other consists histopathology images of the same organ. Performance of our segmentation method is measured in various experimental setups at the object-level and the pixel-level. In addition, we compare the performance of our method with that of existing state-of-the-art methods. The experimental results show that our nuclei segmentation method outperforms the existing methods. Conclusions We propose a nuclei segmentation method based on DCNNs for histopathology images. The proposed method which uses Mask R-CNN with color normalization and multiple inference post-processing provides robust nuclei segmentation results. Our method also can facilitate downstream nuclei morphological analyses as it provides high-quality features extracted from histopathology images.

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