Progress of Pathological Image Analysis Based on deep Learning
DOI:
https://doi.org/10.62051/y27v8m74Keywords:
Deep learning, pathological image analysis, convolutional neural network.Abstract
Pathological image analysis plays a crucial role in the diagnosis and treatment of diseases. However, due to its complexity and diversity, traditional methods are difficult to meet the growing demand. A novel method for analyzing pathological images has emerged in recent years because of advancements in deep learning technology, particularly the usage of convolutional neural networks (CNNs). This study compared the performance of various models through a systematic literature review and investigated the application status of deep learning in pathological image analysis, including pathological image classification, object detection and segmentation, and image generation and enhancement. According to studies, deep learning techniques greatly increase the precision and effectiveness of pathological picture analysis. This work not only emphasizes the significant potential of deep learning technology in enhancing the efficiency of pathological image processing but also brings out its practical application value in clinical practice. Furthermore, to further optimize the integration of deep learning into workflows for pathological image analysis, this study also analyzes the remaining obstacles and future objectives.
Downloads
References
[1] Echle, A., Rindtorff, N. T., Brinker, T. J., et al.: 'Deep learning in cancer pathology: A new generation of clinical biomarkers', British Journal of Cancer, 2021, 124(4), pp. 686-696.
[2] Suganyadevi, S., Seethalakshmi, V., Balasamy, K.: 'A review on deep learning in medical image analysis', International Journal of Multimedia Information Retrieval, 2022, 11(1), pp. 19-38.
[3] Van der Laak, J., Litjens, G., Ciompi, F.: 'Deep learning in histopathology: the path to the clinic', Nature Medicine, 2021, 27(5), pp. 775-784.
[4] Kather, J. N., et al.: 'Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer', Nature Medicine, 2019, 25(7), 1054-1056.
[5] Madabhushi, A., Lee, G.: 'Image analysis and machine learning in digital pathology: Challenges and opportunities', Medical Image Analysis, 2016, 33, pp. 170-175.
[6] Sullivan, A.C., Hunt, J.P., Oldenburg, A.L.: 'Fractal analysis for classification of breast carcinoma in optical coherence tomography', Journal of Biomedical Optics, 2011, 16(6), pp. 066010.
[7] Litjens, G., et al.: 'A survey on deep learning in medical image analysis', Medical Image Analysis, 2017, 42, pp. 60-88.
[8] Cruz-Roa, A., et al.: 'Automatic detection of invasive ductal carcinoma in whole slide images with convolutional neural networks', Proc. SPIE Medical Imaging, 2014, 9041, 904103.
[9] Veta, M., Pluim, J.P., van Diest, P.J., Viergever, M.A.: 'Breast cancer histopathology image analysis: a review', IEEE Trans Biomed Eng, 2014, 61(5), pp. 1400-1411.
[10] Gurcan, M. N., Boucheron, L. E., Can, A., Madabhushi, A., Rajpoot, N. M., Yener, B.: 'Histopathological image analysis: a review', IEEE Rev Biomed Eng, 2009, 2, pp. 147-171.
[11] Wang, W. T., Zheng, Y.: 'Breast cancer image classification based on deep transfer learning', Data Mining, 2022, 12(2), pp. 192-202.
[12] Haralick, R. M., Shanmugam, K., Dinstein, I.: 'Textural Features for Image Classification', IEEE Transactions on Systems, Man, and Cybernetics, 1973, 3(6), pp. 610-621.
[13] Li, L., Pan, X., Yang, H., et al.: 'Multi-task deep learning for fine-grained classification and grading in breast cancer histopathological images', Multimedia Tools and Applications, 2020, 79, pp. 14509–14528.
[14] Oktay, O., et al.: 'Attention U-Net: Learning Where to Look for the Pancreas', Medical Image Analysis, 2020, 62, 101663.
[15] Li, Y., Song, P.: 'Research progress of transfer learning in medical image classification', Journal of Image and Graphics, 2012, 27(3), pp. 672-686.
[16] Xia, F., et al.: 'Graph Learning: A Survey', IEEE Transactions on Artificial Intelligence, 2021, 2(2), pp. 109-127.
[17] Chen, J., et al.: 'TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation', arXiv preprint arXiv:2102.04306, 2021.
[18] Wang, Y., et al.: 'Enhanced pathology image quality using GAN-based super-resolution techniques', Journal of Digital Imaging, 2021, 34(5), pp. 1076-1087.
Downloads
Published
Conference Proceedings Volume
Section
License
Copyright (c) 2025 Transactions on Computer Science and Intelligent Systems Research
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
How to Cite
