In this paper, we introduce a database, called Brea, Brazil. [7] released the BreakHis dataset for beast histopathol-ogy. By incorporating the self-paced learning concept into the selection process, the model learns samples from both well- and less-represented classes, which tackles the issue of model bias when selecting samples. This analysis shows that independently, of the magnification factor, about 30% of errors of th, presented in Fig. The overall workflow of our method is illustrated in Figure 1. The approach adopted in this work parallels the works in [30, 37] in that a pseudolabel is generated for each unlabeled example but it differs from the work in [37] in that all unlabeled ones are pseudolabeled as opposed to only the majority high-confidence samples. by the pathologist. Tissue analysis using histopathological images is the most prevailing as well as a challenging task in the treatment of cancer. Feature space partitioned into three competence regions [36]. A various number of imaging modalities are available (e.g., magnetic resonance, x-ray, ultrasound, and biopsy) where each modality can reveal different structural aspects of tissues. The results show that our model achieves the accuracy between 98.87% and 99.34% for the binary classification and achieve the accuracy between 90.66% and 93.81% for the multi-class classification. To this end, this work proposes a novel semisupervised learning framework that uses self-training and self-paced learning (SPL) [38] to classify breast cancer histopathological images. Also, our semisupervised learning approach hinges on the concept self-training and self-paced learning, which distinguishes our approach from the one reported in our work. Breast cancer is one of the most common and deadly types of cancer that develops in the breast tissue of women worldwide. Background Deep convolutional neural networks (CNNs) have achieved tremendous successes in several disciplines including but not limited to object detection [9, 10], segmentation [11], and classification [12, 13]. Conclusions We obtain significant accuracy performance on the BreakHis dataset compared to the state-of-the-art approaches. In the current proposal, the study performed four experiments according to a magnification factor (40X, 100X, 200X and 400X). Sections 4–6 present a complete overview of all BreakHis related works that … Join ResearchGate to find the people and research you need to help your work. MIL methods: a brief overview Under the standard MIL assumption, positive bags contain The dataset contains a total of 7909 breast cancer histopathology image samples collected from 82 patients under four different magnification levels. Also, a recent report from the American Cancer Society’s forecast for 2019 predicts that there will be almost 286,600 new cases of invasive breast cancer, about 63,930 new noninvasive cases, and about 41,760 deaths among women in the United States [2]. In order to assess the difficulty of this task, we show some preliminary results obtained with state-of-the-art image classification systems. Data augmentation techniques are also employed to overcome the problem of overfitting. In this paper, we introduce a database, called BreaKHis, that is intended to mitigate this gap. Breast Cancer Detection classifier built from the The Breast Cancer Histopathological Image Classification (BreakHis) dataset composed of 7,909 microscopic images. En plus, elles permettent de combiner les prédictions de plusieurs modèles, et cela génère des décisions plus robustes et stables au changement de données. Since FAST features do not have an. ) The new method, named multi-scale graph wavelet neural network (MS-GWNN), leverages the localization property of spectral graph wavelet to perform multi-scale analysis. In average, a total of 24 images per pa, respectively. In this work, a publiclyavailable BreakHis dataset is used built in collaboration with the Prognostics and Diagnostics (P&D) Laboratory, Parana, Brazil [, ]. And thus, it is the key to design an accurate computer-aided detection (CAD) system to capture multi-scale contextual features in a cancerous tissue. Breast tissue biopsies help pathologists to histologically assess the microscopic structure and elements of breast tissues. 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