KEYWORDS: Machine learning, Breast cancer, Feature extraction, Image classification, Education and training, Data modeling, Performance modeling, Tumor growth modeling, Deep learning, Cancer
Breast cancer, as the most common malignant tumor in women, poses great harm to women's health. Among breast cancer patients, there is a relatively high proportion of HER2-positive cases, making the prediction of HER2 status extremely important. Currently, many deep learning methods based on Whole Slide Images (WSIs) have been proposed, but these methods still face the problem of imbalanced datasets, with significant differences in the number of samples in each class, causing the model to overfit categories with an advantage in quantity while ignoring minority categories. Therefore, to address the above issues, this paper proposes a weakly supervised Dual-tier Cluster-based Multiple Instance Learning (DCMIL) framework for the classification of breast cancer images. This framework uses ResNet-101 as the backbone and optimizes the pseudo bag partitioning process by introducing clustering mechanisms in the DTFD algorithm. A series of experiments were conducted on the HER2 dataset. The results showed that our model outperforms the latest algorithms in terms of performance.
Classification algorithm based on deep learning is the main technology for computer-aided intelligent diagnosis of breast pathological images. The existing deep learning algorithms rarely pay attention to multi-scale information in breast cancer pathological images, and can not extract key regions from pathological images for auxiliary diagnosis. To address this issue, this paper proposes a novel method for classifying breast cancer pathological images which incorporates a fine-grained region location mechanism. This method is realized by a dual-branch architecture with global network and local network. Global and local features are extracted from the whole image and local key image, respectively. The final classification results can be obtained by integrating both the global and local network analysis. In order to locate the most predictive area in the whole image, this paper designs utilizes a fine-grained region localization mechanism and combines the above two branch networks. Extensive experiments on the BreakHis data set are conducted to verify the effectiveness of the proposed algorithm. The empirical results show that this method improves the classification accuracy by comparing the performance with that of several typical convolutional neural network and state-of-the-art algorithms.
Data is the most valuable resource of the Internet, attackers often use SQL injection attacks to destroy the database in order to obtain important data information in the database, and today's attack scene is complex, dynamic, multi-channel, non-linear, the existing defense detection technology cannot cope with unknown attacks, the existing instruction set randomization method may be broken by force. Aiming at the above problems, an active defense system of SQL injection attack based on randomization method pool is proposed. The randomization method pool and parallel executor are introduced to build the system framework. The result is decided whether to forward to the database after the decision maker votes, which no longer depends on prior knowledge. The attacker cannot use the system information obtained before to carry out the next effective attack. The formal representation and experimental results show that this method can effectively defend against SQL injection attacks.
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