Histology image analysis for carcinoma detection and grading

By Lei He, L. Rodney Long, Sameer Kiran Antani, George R. Thoma. Comput. Methods Programs Biomed., 2012

381 citations recorded by the source corpus.

This paper presents an overview of the image analysis techniques in the domain of histopathology, specifically, for the objective of automated carcinoma detection and classification. As in other biomedical imaging areas such as radiology, many computer assisted diagnosis (CAD) systems have been implemented to aid histopathologists and clinicians in cancer diagnosis and research, which have been attempted to significantly reduce the labor and subjectivity of traditional manual intervention with histology images. The task of automated histology image analysis is usually not simple due to the unique characteristics of histology imaging, including the variability in image preparation techniques, clinical interpretation protocols, and the complex structures and very large size of the images themselves. In this paper we discuss those characteristics, provide relevant background information about slide preparation and interpretation, and review the application of digital image processing techniques to the field of histology image analysis. In particular, emphasis is given to state-of-the-art image segmentation methods for feature extraction and disease classification. Four major carcinomas of cervix, prostate, breast, and lung are selected to illustrate the functions and capabilities of existing CAD systems.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/23d8f9eb15e6bd995c93b48c92d4f178f08090d2

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In Artificial Intelligence

1. Yalin Zheng
2. Kun Zhang
3. Dalal Bardou
4. Sayed Mohammad Ahmad

In Computer Vision and Pattern Recognition

1. Yalin Zheng
2. Yanda Meng
3. Kun Zhang
4. Yitian Zhao
5. Dalal Bardou
6. Sayed Mohammad Ahmad
7. Xiaoyun Yang

Citations in the corpus

• Classification of Breast Cancer Based on Histology Images Using Convolutional Neural Networks (2018, methodology): “Breast cancer can be diagnosed using medical images testing, like histology and radiology images [4]–[6].”
• A Deep Learning Architecture for Image Representation, Visual Interpretability and Automated Basal-Cell Carcinoma Cancer Detection (2013, neutral): “Example of BCC histopathology images (both cancer and non-cancer) stained with H&E at 10X There is an extensive literature in automatic histopathology image analysis where different strategies for image representation have been tried: discrete cosine transform (DCT), wavelet coefficients, Gabor desc”
• DTFD-MIL: Double-Tier Feature Distillation Multiple Instance Learning for Histopathology Whole Slide Image Classification (2022, background): “Given the huge size of a WSI, the units to be directly processed are the much smaller patches cropped from WSIs [12].”