Sürdürülebilir Kalkınma Amaçları
Yayınlar
- MakaleAçık Erişim2023Scopus SJRQ2Extracting low dimensional representations from large size whole slide images using deep convolutional autoencoders
Çelik, Yusuf; Karabatak, Murat
Background and objective: Artificial intelligence-based analysis of medical images has recently become a trendy field of study. The most significant effect on these systems to produce reliable and high-performance results is the amount of accessed data. Although whole-slide images (WSI), one of the current imaging techniques used in histopathology, storage costs are very expensive. Therefore, low-dimensional visuals representing of WSIs is an essential field of study. Methods: In this study, deep auto-encoder-based models are designed to create low dimensional representations for WSI. The size reduction was performed for the input images in different sizes, at the ratios of 1: 3, 1: 6, and 1: 12, respectively. Results: Similarity index measure values were obtained as high as 0.957 and 0.938, respectively, using 128 × 128 × 3 dimensional patches in the size reduction process of 1: 3 and 1:12 on test WSI. Conclusion: The proposed model has a structure that can be applied in compression of WSIs and secure data transfer. By using these representations, both storage costs and the high-level hardware costs required by deep learning algorithms can be significantly reduced.
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İncele - MakaleAçık Erişim2020Scopus SJRQ1Automated invasive ductal carcinoma detection based using deep transfer learning with whole-slide images
Celik, Yusuf; Talo, Muhammed; Yildirim, Ozal; Karabatak, Murat; Acharya, U Rajendra
Advances in artificial intelligence technologies have made it possible to obtain more accurate and reliable results using digital images. Due to the advances in digital histopathological images obtained using whole slide image (WSI) scanners, automated analysis of digital images by computer support systems has become interesting. In particular, deep learning architectures, are one of the preferred approaches in the analysis of digital histopathology images. The deeper networks trained on large amounts of image data are adapted for different tasks using transfer learning technique. In this study, automated detection of invasive ductal carcinoma (IDC), which is the most common subtype of breast cancers, is proposed using deep transfer learning technique. We have used deep learning pre-trained models, ResNet-50 and DenseNet-161 for the IDC detection task. The public histopathology dataset containing 277,524 image patches were used in our experimental studies. As a result of training on the last layers of pre-trained deep networks, DenseNet-161 model has yielded F-sore of 92.38% and balanced accuracy value of 91.57%. Similarly, we have obtained F-score of 94.11% and balanced accuracy value of 90.96% using ResNet-50 architecture. In addition, our developed model is validated using the publicly available BreakHis breast cancer dataset and obtained promising results in classifying magnification independent histopathology images into benign and malignant classes. Our developed system obtained the highest classification performance as compared to the state-of-art techniques and is ready to be tested with more diverse huge databases.
AtıfScopus297OpenAlex318İncele