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

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 diagnosi...

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Bibliographic Details
Published in:Journal of imaging 2023-01, Vol.9 (2), p.25
Main Authors: Cinar, Umut, Cetin Atalay, Rengul, Cetin, Yasemin Yardimci
Format: Article
Language:English
Subjects:
Accuracy
Artificial neural networks
Automatic classification
Cameras
Classification
Cost function
Deep learning
Diagnosis
Hepatitis
Hepatoma
Histology, Pathological
Histopathology
Hyperspectral imaging
Laboratories
Lasers
Light
Liver cancer
Lung cancer
Medical imaging
Methods
Microscopy
Neural networks
Technology application
Tissues
Training
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Summary: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.
ISSN:2313-433X
2313-433X
DOI:10.3390/jimaging9020025