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

Download

index.pdf

Date

2023-02-01

Author

Çinar, Umut
Cetin Atalay, Rengul
Çetin, Yasemin

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

139
views

52
downloads

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

Suggestions

OpenMETU
Core

Classification of Human Carcinoma Cells Using Multispectral Imagery Çinar, Umut; Çetin, Yasemin; Atalay, Rengül; Cetin, Enis (2016-03-03) In this paper, we present a technique for automatically classifying human carcinoma cell images using textural features. An image dataset containing microscopy biopsy images from different patients for 14 distinct cancer cell line type is studied. The images are captured using a RGB camera attached to an inverted microscopy device. Texture based Gabor features are extracted from multispectral input images. SVM classifier is used to generate a descriptive model for the purpose of cell line classification. Th...
The use of gold and silver nanoparticles for surface enhanced fluorescence (SEF) of Dyes Öztürk, Tacettin; Volkan, Mürvet; Department of Chemistry (2010) This study focuses on preparing surface enhanced fluorescence (SEF) substrates for use in the enhancement of the emission signal of rhodamine B and fluorescein dyes. Fluorescence spectroscopy has been widely utilized owing to its high sensitivity. SEF is a process where the interactions of fluorophores with the localized surface plasmons of metal nanoparticles results in fluorescence enhancement, increased photostability and rates of system radiative decay which leads to a decreased lifetime. One of the mos...
Abnormal Crowd Behavior Detection Using Novel Optical Flow-Based Features Direkoglu, Cem; Sah, Melike; O'Connor, Noel E. (2017-09-01) In this paper, we propose a novel optical flow based features for abnormal crowd behaviour detection. The proposed feature is mainly based on the angle difference computed between the optical flow vectors in the current frame and in the previous frame at each pixel location. The angle difference information is also combined with the optical flow magnitude to produce new, effective and direction invariant event features. A one-class SVM is utilized to learn normal crowd behavior. If a test sample deviates si...
Prediction of multiphase flow properties from nuclear magnetic resonance imaging Karaman, Türker; Akın, Serhat; Department of Petroleum and Natural Gas Engineering (2009) In this study a hybrid Pore Network (PN) model that simulates two-phase (water-oil) drainage and imbibition mechanisms is developed. The developed model produces Nuclear Magnetic Resonance (NMR) T2 relaxation times using correlations available in the literature. The developed PN was calibrated using experimental relative permeability data obtained for Berea Sandstone, Kuzey Marmara Limestone, Yeniköy Dolostone and Dolomitic Limestone core plugs. Pore network body and throat parameters were obtained from ser...
Automatic segmentation of cristae membranes in 3d electron microscopy tomography images using artificial neural networks Karadeniz, Merih Alphan; Mumcuoğlu, Ünal Erkan; Department of Medical Informatics (2016) Electron Microscopy Tomography (EMT) technique produces 3D images of cells comprising hundreds of slices of high resolution frames. Segmentation of membranes in these images are necessary in order to reveal the relations between the structural components of the cell and its behaviour. The physical shape of the crista which is a membrane of the mitochondria has been hypostatized for being an early indicator for many diseases or mitochondrial dysfunctions. Automatic segmentation of cristae in EMT images are n...

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.