Resizing and cleaning of histopathological images using generative adversarial networks

Çelik, Gaffari; Talu, Muhammed Fatih

doi:10.1016/j.physa.2019.122652

Cited by 20 publications

(9 citation statements)

References 20 publications

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“…However, the high cost of equipment, such as cutting-edge scanners and data storage, represents the challenges in acquiring high-resolution images [100]. To solve this challenge, super-resolution methods like super-resolution generative networks (SRGAN) [101] have been examined and found to successfully improve the resolution of the breast cancer histopathological images [26], [102]. Thus, future research should focus on investigating the performance of the deep learning models after employing SRGAN models for the pathology images.…”

Section: Discussionmentioning

confidence: 99%

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Breast Cancer Classification Using Deep Learning Approaches and Histopathology Image: A Comparison Study

et al. 2020

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Convolutional Neural Network (CNN) models are a type of deep learning architecture introduced to achieve the correct classification of breast cancer. This paper has a twofold purpose. The first aim is to investigate the various deep learning models in classifying breast cancer histopathology images. This study identified the most accurate models in terms of the binary, four, and eight classifications of breast cancer histopathology image databases. The different accuracy scores obtained for the deep learning models on the same database showed that other factors such as pre-processing, data augmentation, and transfer learning methods can impact the ability of the models to achieve higher accuracy. The second purpose of our manuscript is to investigate the latest models that have no or limited examination done in previous studies. The models like ResNeXt, Dual Path Net, SENet, and NASNet had been identified with the most cuttingedge results for the ImageNet database. These models were examined for the binary, and eight classifications on BreakHis, a breast cancer histopathology image database. Furthermore, the BACH database was used to investigate these models for four classifications. Then, these models were compared with the previous studies to find and propose the most state-of-the-art models for each classification. Since the Inception-ResNet-V2 architecture achieved the best results for binary and eight classifications, we have examined this model in our study as well to provide a better comparison result. In short, this paper provides an extensive evaluation and discussion about the experimental settings for each study that had been conducted on the breast cancer histopathology images. INDEX TERMS Breast cancer, histopathology medical images, deep learning, transfer learning, data augmentation, pre-processing, classification.

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Section: Discussionmentioning

confidence: 99%

“…Thus, the generated images were all colorful with three channels. However, each database had different magnification factors as different hardware equipment was used [26]. Thus, the images were different in terms of the resolution, possibly impacting the diagnosis.…”

Section: ) Pathology Databasesmentioning

confidence: 99%

Breast Cancer Classification Using Deep Learning Approaches and Histopathology Image: A Comparison Study

et al. 2020

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“…Another major challenge is the presence of artifacts and color variation [ 8 , 11 , 36 , 59 , 63 , 64 ]. Histopathology images are captured through several stages as previously mentioned.…”

Section: Histopathology Images Backgroundmentioning

confidence: 99%

“…Large variations in light are considered an important factor for the precise prostate cancer diagnosis. These variations need to be handled earlier before employing image processing techniques [ 63 , 64 ].…”

Section: Histopathology Images Backgroundmentioning

confidence: 99%

Role of AI and Histopathological Images in Detecting Prostate Cancer: A Survey

Ayyad

Shehata

Shalaby

et al. 2021

Sensors

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Prostate cancer is one of the most identified cancers and second most prevalent among cancer-related deaths of men worldwide. Early diagnosis and treatment are substantial to stop or handle the increase and spread of cancer cells in the body. Histopathological image diagnosis is a gold standard for detecting prostate cancer as it has different visual characteristics but interpreting those type of images needs a high level of expertise and takes too much time. One of the ways to accelerate such an analysis is by employing artificial intelligence (AI) through the use of computer-aided diagnosis (CAD) systems. The recent developments in artificial intelligence along with its sub-fields of conventional machine learning and deep learning provide new insights to clinicians and researchers, and an abundance of research is presented specifically for histopathology images tailored for prostate cancer. However, there is a lack of comprehensive surveys that focus on prostate cancer using histopathology images. In this paper, we provide a very comprehensive review of most, if not all, studies that handled the prostate cancer diagnosis using histopathological images. The survey begins with an overview of histopathological image preparation and its challenges. We also briefly review the computing techniques that are commonly applied in image processing, segmentation, feature selection, and classification that can help in detecting prostate malignancies in histopathological images.

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“…For instance, Mukherjee et al, [26] embraced the convolutional neural network (CNN) to convert low-resolution slide scanner images of cancer data into a high-resolution image. Then, the SRGAN model was applied by Çelik and Talu [27] to increase the resolution of breast cancer histopathology images. Moreover, this model was enhanced by the researchers.…”

Section: Srgan For Medical Histopathology Breast Cancer Imagingmentioning

confidence: 99%

Breast Cancer Histopathology Image Super-Resolution Using Wide-Attention GAN With Improved Wasserstein Gradient Penalty and Perceptual Loss

Shahidi

2021

IEEE Access

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In the realm of image processing, enhancing the quality of the images is known as a superresolution problem (SR). Among SR methods, a super-resolution generative adversarial network, or SRGAN, has been introduced to generate SR images from low-resolution images. As it is of the utmost importance to keep the size and the shape of the images, while enlarging the medical images, we propose a novel superresolution model with a generative adversarial network to generate SR images with finer details and higher quality to encourage less blurring. By widening residual blocks and using a self-attention layer, our model becomes robust and generalizable as it is able to extract the most important part of the images before upsampling. We named our proposed model as wide-attention SRGAN (WA-SRGAN). Moreover, we have applied improved Wasserstein with a Gradient penalty to stabilize the model while training. To train our model, we have applied images from Camylon 16 database and enlarged them by 2×, 4×, 8×, and 16× upscale factors with the ground truth of the size of 256×256×3. Furthermore, two normalization methods, including batch normalization, and weight normalization have been applied and we observed that weight normalization is an enabling factor to improve metric performance in terms of SSIM. Moreover, several evaluation metrics, such as PSNR, MSE, SSIM, MS-SSIM, and QILV have been applied for having a comprehensive objective comparison with other methods, including SRGAN, A-SRGAN, and bicubial. Also, we performed the job of classification by using a deep learning model called ResNeXt-101 (32x8d) for super-resolution, highresolution, and low-resolution images and compared the outcomes in terms of accuracy score. Finally, the results on breast cancer histopathology images show the superiority of our model by using weight normalization and a batch size of one in terms of restoration of the color and the texture details.

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Resizing and cleaning of histopathological images using generative adversarial networks

Cited by 20 publications

References 20 publications

Breast Cancer Classification Using Deep Learning Approaches and Histopathology Image: A Comparison Study

Breast Cancer Classification Using Deep Learning Approaches and Histopathology Image: A Comparison Study

Role of AI and Histopathological Images in Detecting Prostate Cancer: A Survey

Breast Cancer Histopathology Image Super-Resolution Using Wide-Attention GAN With Improved Wasserstein Gradient Penalty and Perceptual Loss

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