A Review of Artificial Intelligence in Tumor Pathology Image Analysis

Saisai Feng; Mingchuan Zhang

doi:10.62051/ijcsit.v3n1.07

Authors

Saisai Feng
Mingchuan Zhang

DOI:

https://doi.org/10.62051/ijcsit.v3n1.07

Keywords:

Artificial intelligence, Image segmentation, Image classification, Pathology image analysis

Abstract

Accurate diagnosis of tumors is crucial to the treatment and prognosis of patients. Pathological diagnosis is regarded as the "gold standard" of tumor diagnosis, which helps to detect the disease at an early stage and formulate precise treatment plans for patients. However, traditional pathology diagnosis relies heavily on the expertise and diagnostic experience of physicians, making the quality and accuracy of pathology diagnosis largely dependent on their individual capabilities. With the popularization of Whole Slide Image (WSI) technology, the application of AI in pathology has gained significant momentum. With its powerful analyzing ability, AI has been widely used in computational pathology, especially in pathology-assisted diagnosis, showing great potential. This paper first explores two core tasks of AI in the field of pathology image analysis - image segmentation and image classification. Finally, it looks at the challenges and opportunities facing the field.

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References

Sun Z C, Liu L X. Progress and prospect of artificial intelligence in diagnosis and treatment of liver cancer [J]. Medical Review, 2020, 26(22): 4421-4424.

Xue K. Digital pathology+artificial intelligence to promote new development of precise diagnosis [N]. China Science News, 2017-11-23(006).

AlZubaidi A K, Sideseq F B, Faeq A, et al. Computer aided diagnosis in digital pathology application: Review and perspective approach in lung cancer classification [C]. 2017 annual conference on new trends in information & Communications technology applications (NTICT). IEEE, 2017: 219-224.

Nam S, Chong Y, Jung C K, et al. Introduction to digital pathology and computer-aided pathology [J]. Journal of Pathology and Translational Medicine, 2020, 54(2): 125.

Bengtsson E, Danielsen H, Treanor D, et al. Computer-aided diagnostics in digital pathology [J]. Cytometry Part A, 2017, 91(6): 551-554.

Shafi S, Parwani A V. Artificial intelligence in diagnostic pathology [J]. Diagnostic Pathology, 2023, 18(1): 109.

Farahani N, Parwani A V, Pantanowitz L. Whole slide imaging in pathology: advantages, limitations, and emerging perspectives [J]. Pathology and Laboratory Medicine International, 2015: 23-33.

Suda R, Sakai N, Matsushita K, et al. Prediction of mismatch repair deficient biliary tract cancer: Role of morphological features and host immune response detected by routine hematoxylin-eosin staining [J]. Journal of Hepato-Biliary-Pancreatic Sciences, 2021, 28(8): 680-691.

Wang Y, Crookes D, Eldin O S, et al. Assisted diagnosis of cervical intraepithelial neoplasia (CIN) [J]. IEEE Journal of Selected Topics in Signal Processing, 2009, 3(1): 112-121.

AlMubarak H A, Stanley J, Guo P, et al. A hybrid deep learning and handcrafted feature approach for cervical cancer digital histology image classification [J]. International Journal of Healthcare Information Systems and Informatics, 2019, 14(2): 66-87.

Araújo T, Aresta G, Castro E, et al. Classification of breast cancer histology images using convolutional neural networks [J]. PloS one, 2017, 12(6): e0177544.

Wu H S, Xu R, Harpaz N, et al. Segmentation of intestinal gland images with iterative region growing [J]. Journal of Microscopy, 2005, 220(3): 190-204.

Wang R, Chen S, Ji C, et al. Boundary-aware context neural network for medical image segmentation [J]. Medical Image Analysis, 2022, 78: 102395.

Lin J, Lin J, Lu C, et al. CKD-TransBTS: Clinical knowledge-driven hybrid transformer with modality-correlated cross-attention for brain tumor segmentation [J]. IEEE Transactions on Medical Imaging, 2023, 42(8): 2451-2461.

Feng R, Liu X, Chen J, et al. A deep learning approach for colonoscopy pathology WSI analysis: Accurate segmentation and classification [J]. IEEE Journal of Biomedical and Health Informatics, 2020, 25(10): 3700-3708.

Yu X, Li G, Lou W, et al. Diffusion-based data augmentation for nuclei image segmentation [C]. International Conference on Medical Image Computing and Computer-Assisted Intervention. Cham: Springer Nature Switzerland, 2023: 592-602.