Conditional generative adversarial network for synthesizing hyperspectral images of breast cancer cells from digitized histology

Halicek, Martin; Ortega, Samuel; Fabelo, Himar; López, Carlos; Lejaune, Marylene; Fei, Baowei

doi:10.1117/12.2549994

Cited by 18 publications

(10 citation statements)

References 5 publications

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“…Gozes et al generated synthetic patches of mitosis for enhancing classification of cell images using GAN [ 20 ]. In another work, Halicek et al implemented a conditional GAN for the synthetic generation of hyperspectral cell im-ages of breast cancer obtained from digital histology [ 21 ]. DermGAN incorporates the pix2pix architecture to generate synthetic data for clinical skin images [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

Synthesis of Microscopic Cell Images Obtained from Bone Marrow Aspirate Smears through Generative Adversarial Networks

Hazra

Byun

Kim³

et al. 2022

Biology

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Every year approximately 1.24 million people are diagnosed with blood cancer. While the rate increases each year, the availability of data for each kind of blood cancer remains scarce. It is essential to produce enough data for each blood cell type obtained from bone marrow aspirate smears to diagnose rare types of cancer. Generating data would help easy and quick diagnosis, which are the most critical factors in cancer. Generative adversarial networks (GAN) are the latest emerging framework for generating synthetic images and time-series data. This paper takes microscopic cell images, preprocesses them, and uses a hybrid GAN architecture to generate synthetic images of the cell types containing fewer data. We prepared a single dataset with expert intervention by combining images from three different sources. The final dataset consists of 12 cell types and has 33,177 microscopic cell images. We use the discriminator architecture of auxiliary classifier GAN (AC-GAN) and combine it with the Wasserstein GAN with gradient penalty model (WGAN-GP). We name our model as WGAN-GP-AC. The discriminator in our proposed model works to identify real and generated images and classify every image with a cell type. We provide experimental results demonstrating that our proposed model performs better than existing individual and hybrid GAN models in generating microscopic cell images. We use the generated synthetic data with classification models, and the results prove that the classification rate increases significantly. Classification models achieved 0.95 precision and 0.96 recall value for synthetic data, which is higher than the original, augmented, or combined datasets.

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Section: Introductionmentioning

confidence: 99%

Synthesis of Microscopic Cell Images Obtained from Bone Marrow Aspirate Smears through Generative Adversarial Networks

Hazra

Byun

Kim³

et al. 2022

Biology

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“…More recently, several modern DL approaches with origins in computer-vision have been applied to medical HSI experimentally. In [76], a generative adversarial network (GAN) was applied to use DL to learn the association of RGB images and HS images to learn the ability to generate HS digital histology images from standard RGB digital histology images of breast cancer. Another modern approach is long-short-term-memory (LSTM) and recurrent neural networks (RNN) which can utilize spatial-spectral and time-based inputs to operate in real-time video approaches.…”

Section: Deep Learning Methodsmentioning

confidence: 99%

“…In-vivo brain tumor detection ‡ [27] Deep learning 2D-CNN and 1D-DNN In-vivo brain tumor detection ‡ [69] 2D-CNN (Inception v4) Head and neck cancer [71] Salivary gland cancer [72] 2D-CNN and 3D-CNN Head and neck cancer [73] 2D-CNN (U-Net) Tongue cancer detection [74] Breast cancer [75] GAN HS image generation from RGB [76] RNNs, 2D-CNN and 3D-CNN Head and neck cancer detection [77] Publicly available datasets are marked with ‡ .…”

Section: Spatial and Spectral Features In Supervised Classificationmentioning

confidence: 99%

Information Extraction Techniques in Hyperspectral Imaging Biomedical Applications

Ortega¹,

Halicek²,

Fabelo³

et al. 2021

Multimedia Information Retrieval

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Hyperspectral imaging (HSI) is a technology able to measure information about the spectral reflectance or transmission of light from the surface. The spectral data, usually within the ultraviolet and infrared regions of the electromagnetic spectrum, provide information about the interaction between light and different materials within the image. This fact enables the identification of different materials based on such spectral information. In recent years, this technology is being actively explored for clinical applications. One of the most relevant challenges in medical HSI is the information extraction, where image processing methods are used to extract useful information for disease detection and diagnosis. In this chapter, we provide an overview of the information extraction techniques for HSI. First, we introduce the background of HSI, and the main motivations of its usage for medical applications. Second, we present information extraction techniques based on both light propagation models within tissue and machine learning approaches. Then, we survey the usage of such information extraction techniques in HSI biomedical research applications. Finally, we discuss the main advantages and disadvantages of the most commonly used image processing approaches and the current challenges in HSI information extraction techniques in clinical applications.

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“…Better segmentation models, such as U-net, have provided cutting-edge segmentation findings in a variety of computer vision datasets [ 196 , 197 ]. Furthermore, applying this technique involving various imaging modalities may boost BrC classification results.…”

Section: Challenges and Research Directionsmentioning

confidence: 99%

A Comprehensive Survey on Deep-Learning-Based Breast Cancer Diagnosis

Mridha

Hamid

Monowar

et al. 2021

Cancers

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Breast cancer is now the most frequently diagnosed cancer in women, and its percentage is gradually increasing. Optimistically, there is a good chance of recovery from breast cancer if identified and treated at an early stage. Therefore, several researchers have established deep-learning-based automated methods for their efficiency and accuracy in predicting the growth of cancer cells utilizing medical imaging modalities. As of yet, few review studies on breast cancer diagnosis are available that summarize some existing studies. However, these studies were unable to address emerging architectures and modalities in breast cancer diagnosis. This review focuses on the evolving architectures of deep learning for breast cancer detection. In what follows, this survey presents existing deep-learning-based architectures, analyzes the strengths and limitations of the existing studies, examines the used datasets, and reviews image pre-processing techniques. Furthermore, a concrete review of diverse imaging modalities, performance metrics and results, challenges, and research directions for future researchers is presented.

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Conditional generative adversarial network for synthesizing hyperspectral images of breast cancer cells from digitized histology

Cited by 18 publications

References 5 publications

Synthesis of Microscopic Cell Images Obtained from Bone Marrow Aspirate Smears through Generative Adversarial Networks

Synthesis of Microscopic Cell Images Obtained from Bone Marrow Aspirate Smears through Generative Adversarial Networks

Information Extraction Techniques in Hyperspectral Imaging Biomedical Applications

A Comprehensive Survey on Deep-Learning-Based Breast Cancer Diagnosis

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