Classification of breast cancer histopathological images using interleaved DenseNet with SENet (IDSNet)

Li, Xia; Shen, Xi; Zhou, Yongxia; Wang, Xiuhui; Li, Tie‐Qiang

doi:10.1371/journal.pone.0232127

Cited by 119 publications

(78 citation statements)

References 14 publications

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“…DenseNet-161 has demonstrated a superb performance for ILSVRC ImageNet classification task [ 43 ]. Moreover, DenseNet-161 has shown a great success in several histopathological image analysis pipelines [ 10 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 ]. In order to supply the patch-wise feature extractor network with image patches, we extract a number of patches k based on the following equation [ 7 ]:

where W and H are width and height dimensions of the input image, respectively.…”

Section: Proposed 3e-net Modelmentioning

confidence: 99%

3E-Net: Entropy-Based Elastic Ensemble of Deep Convolutional Neural Networks for Grading of Invasive Breast Carcinoma Histopathological Microscopic Images

Senousy

Abdelsamea

Mohamed

et al. 2021

Entropy

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Automated grading systems using deep convolution neural networks (DCNNs) have proven their capability and potential to distinguish between different breast cancer grades using digitized histopathological images. In digital breast pathology, it is vital to measure how confident a DCNN is in grading using a machine-confidence metric, especially with the presence of major computer vision challenging problems such as the high visual variability of the images. Such a quantitative metric can be employed not only to improve the robustness of automated systems, but also to assist medical professionals in identifying complex cases. In this paper, we propose Entropy-based Elastic Ensemble of DCNN models (3E-Net) for grading invasive breast carcinoma microscopy images which provides an initial stage of explainability (using an uncertainty-aware mechanism adopting entropy). Our proposed model has been designed in a way to (1) exclude images that are less sensitive and highly uncertain to our ensemble model and (2) dynamically grade the non-excluded images using the certain models in the ensemble architecture. We evaluated two variations of 3E-Net on an invasive breast carcinoma dataset and we achieved grading accuracy of 96.15% and 99.50%.

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where W and H are width and height dimensions of the input image, respectively.…”

Section: Proposed 3e-net Modelmentioning

confidence: 99%

3E-Net: Entropy-Based Elastic Ensemble of Deep Convolutional Neural Networks for Grading of Invasive Breast Carcinoma Histopathological Microscopic Images

Senousy

Abdelsamea

Mohamed

et al. 2021

Entropy

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“…In addition, through the comparison experiments, we found that adding the SENet block can achieve a clear performance improvement and the EER decreases from 3.78% to 3.48%. The role of the SE block is that it can significantly improve the discrimination of local features, which has also been confirmed in other tasks [53,54,53].…”

Section: Ablation Studymentioning

confidence: 53%

Deep multi-metric learning for text-independent speaker verification

Wang

Feng

et al. 2020

Neurocomputing

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Text-independent speaker verification is an important artificial intelligence problem that has a wide spectrum of applications, such as criminal investigation, payment certification, and interest-based customer services. The purpose of text-independent speaker verification is to determine whether two given uncontrolled utterances originate from the same speaker or not. Extracting speech features for each speaker using deep neural networks is a promising direction to explore and a straightforward solution is to train the discriminative feature extraction network by using a metric learning loss function. However, a single loss function often has certain limitations. Thus, we use deep multi-metric learning to address the problem and introduce three different losses for this problem, i.e., triplet loss, n-pair loss and angular loss. The three loss functions work in a cooperative way to train a feature extraction network equipped with Residual connections and squeeze-and-excitation attention. We conduct experiments on the large-scale VoxCeleb2 dataset, which contains over a million utterances from over 6, 000 speakers, and the proposed deep neural network obtains an equal error rate of 3.48%, which is a very competitive result. Codes for both training and testing and pretrained models are available at https://github.com/GreatJiweix/DmmlTiSV, which is the first publicly available code repository for large-scale text-independent speaker verification with performance on par with the state-of-the-art systems.

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“…DenseNet [22] further deepened the idea of ResNet, applied a shortcut to the entire network, realized the dense connection of the network, and strengthened the connection between features of each layer. Image classification technology based on DenseNet has recently been applied to various fields [23][24][25]. However, as the model continues to be expanded in depth, width, and base, the amount of its calculation is also increasing.…”

Section: Structure Of the Cnnsmentioning

confidence: 99%

A Novel Malware Detection and Family Classification Scheme for IoT Based on DEAM and DenseNet

Wang

Zhao

Wang

et al. 2021

Security and Communication Networks

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With the rapid increase in the amount and type of malware, traditional methods of malware detection and family classification for IoT applications through static and dynamic analysis have been greatly challenged. In this paper, a new simple and effective attention module of Convolutional Neural Networks (CNNs), named as Depthwise Efficient Attention Module (DEAM), is proposed and combined with a DenseNet to propose a new malware detection and family classification model. Based on the good effect of the DenseNet in the field of image classification and the visual similarity of the malware family on images, the gray-scale image transformed from malware is input into the model combined with the DEAM and DenseNet for malware detection, and then the family classification is carried out. The DEAM is a general lightweight attention module improved based on the Convolutional Block Attention Module (CBAM), which can strengthen the attention to the characteristics of malware and improve the model effect. We use the MalImg dataset, Microsoft malware classification challenge dataset (BIG 2015), and our dataset constructed by the two above-mentioned datasets to verify the effectiveness of the proposed model in family classification and malware detection. Experimental results show that the proposed model achieves 99.3% in terms of accuracy for malware detection on our dataset and achieves 98.5% and 97.3% in terms of accuracy for family classification on the MalImg dataset and BIG 2015 dataset, respectively. The model can reliably detect IoT malware and classify its families.

show abstract

Classification of breast cancer histopathological images using interleaved DenseNet with SENet (IDSNet)

Cited by 119 publications

References 14 publications

3E-Net: Entropy-Based Elastic Ensemble of Deep Convolutional Neural Networks for Grading of Invasive Breast Carcinoma Histopathological Microscopic Images

3E-Net: Entropy-Based Elastic Ensemble of Deep Convolutional Neural Networks for Grading of Invasive Breast Carcinoma Histopathological Microscopic Images

Deep multi-metric learning for text-independent speaker verification

A Novel Malware Detection and Family Classification Scheme for IoT Based on DEAM and DenseNet

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