Human Hepatocellular Carcinoma Classification from H&E Stained Histopathology Images with 3D Convolutional Neural Networks and Focal Loss Function

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2023-02-01

Author

Çinar, Umut
Cetin Atalay, Rengul
Çetin, Yasemin

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This paper proposes a new Hepatocellular Carcinoma (HCC) classification method utilizing a hyperspectral imaging system (HSI) integrated with a light microscope. Using our custom imaging system, we have captured 270 bands of hyperspectral images of healthy and cancer tissue samples with HCC diagnosis from a liver microarray slide. Convolutional Neural Networks with 3D convolutions (3D-CNN) have been used to build an accurate classification model. With the help of 3D convolutions, spectral and spatial features within the hyperspectral cube are incorporated to train a strong classifier. Unlike 2D convolutions, 3D convolutions take the spectral dimension into account while automatically collecting distinctive features during the CNN training stage. As a result, we have avoided manual feature engineering on hyperspectral data and proposed a compact method for HSI medical applications. Moreover, the focal loss function, utilized as a CNN cost function, enables our model to tackle the class imbalance problem residing in the dataset effectively. The focal loss function emphasizes the hard examples to learn and prevents overfitting due to the lack of inter-class balancing. Our empirical results demonstrate the superiority of hyperspectral data over RGB data for liver cancer tissue classification. We have observed that increased spectral dimension results in higher classification accuracy. Both spectral and spatial features are essential in training an accurate learner for cancer tissue classification.

Subject Keywords

3D convolutions, 3D-CNN, cancerous cell classification, convolutional neural networks, deep learning, focal loss function, hepatocellular carcinoma detection, human carcinoma detection, hyperspectral imaging, hyperspectral microscopy

URI

https://hdl.handle.net/11511/102758

Journal

Journal of Imaging

DOI

https://doi.org/10.3390/jimaging9020025

Collections

Graduate School of Informatics, Article

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Citation Formats

U. Çinar, R. Cetin Atalay, and Y. Çetin, “Human Hepatocellular Carcinoma Classification from H&E Stained Histopathology Images with 3D Convolutional Neural Networks and Focal Loss Function,” Journal of Imaging, vol. 9, no. 2, pp. 0–0, 2023, Accessed: 00, 2023. [Online]. Available: https://hdl.handle.net/11511/102758.