Multitask Classification of Breast Cancer Pathological Images Using SE-DenseNet

Ye, Guanglu; Ruan, Jing; Wu, Chenchen; Zhou, Jingfan; He, Simin; Wang, Jianlian; Zhu, Zhikui; Yue, Junqiu; Zhang, Yanggeling

doi:10.1109/icaci.2019.8778592

Cited by 8 publications

(3 citation statements)

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“…Different from focusing on the feature map The channel refinement model is a model of attention acting on the channel domain of the feature map. It is proposed in SENet [ 10 ] designed by the ILSVRC17 competition classification task champion. The channel recalibration model weights the input features by channel, so that the network's attention is focused on useful features, and the channel weights can be learned through training.…”

Section: Related Workmentioning

confidence: 99%

“…To improve the classification model's effectiveness, maximize the few available samples. The channel recalibration model [ 10 ] focuses on feature channels. It suppresses superfluous features through learned channel weights and improves classification model performance.…”

Section: Introductionmentioning

confidence: 99%

“…e channel recalibration model [10] focuses on feature channels. It suppresses superfluous features through learned channel weights and improves classification model performance.…”

Section: Introductionmentioning

confidence: 99%

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Breast Cancer Pathological Image Classification Based on the Multiscale CNN Squeeze Model

Alqahtani

Mandawkar

Sharma

et al. 2022

Computational Intelligence and Neuroscience

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The use of an automatic histopathological image identification system is essential for expediting diagnoses and lowering mistake rates. Although it is of enormous clinical importance, computerized breast cancer multiclassification using histological pictures has rarely been investigated. A deep learning-based classification strategy is suggested to solve the challenge of automated categorization of breast cancer pathology pictures. The attention model that acts on the feature channel is the channel refinement model. The learned channel weight may be used to reduce superfluous features when implementing the feature channel. To increase classification accuracy, calibration is necessary. To increase the accuracy of channel recalibration findings, a multiscale channel recalibration model is provided, and the msSE-ResNet convolutional neural network is built. The multiscale properties flow through the network’s highest pooling layer. The channel weights obtained at different scales are delivered into line fusion and used as input to the next channel recalibration model, which may improve the results of channel recalibration. The experimental findings reveal that the spatial recalibration model fares poorly on the job of classifying breast cancer pathology pictures when applied to the semantic segmentation of brain MRI images. The public BreakHis dataset is used to conduct the experiment. The network performs benign/malignant breast pathology picture classiﬁcation collected at various magnifications with a classification accuracy of 88.87 percent, according to experimental data. The diseased images are also more resilient. Experiments on pathological pictures at various magnifications show that msSE-ResNet34 is capable of performing well when used to classify pathological images at various magnifications.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%