Deep transfer with minority data augmentation for imbalanced breast cancer dataset

Saini, Manisha; Susan, Seba

doi:10.1016/j.asoc.2020.106759

Cited by 102 publications

(39 citation statements)

References 26 publications

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“…Bi-directional Long-Short Term Memory model [ 162 ] approach was also proposed for the classification using context-based patch modeling. A deep transfer network [ 130 ] using Deep Convolutional Generative Adversarial Network (DCGAN) as a data augmentation technique was proposed to tackle the data imbalance problem.…”

Section: Image Processing Approachesmentioning

confidence: 99%

Breast histopathological image analysis using image processing techniques for diagnostic purposes: A methodological review

2021

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Breast cancer in women is the second most common cancer worldwide. Early detection of breast cancer can reduce the risk of human life. Non-invasive techniques such as mammograms and ultrasound imaging are popularly used to detect the tumour. However, histopathological analysis is necessary to determine the malignancy of the tumour as it analyses the image at the cellular level. Manual analysis of these slides is time consuming, tedious, subjective and are susceptible to human errors. Also, at times the interpretation of these images are inconsistent between laboratories. Hence, a Computer-Aided Diagnostic system that can act as a decision support system is need of the hour. Moreover, recent developments in computational power and memory capacity led to the application of computer tools and medical image processing techniques to process and analyze breast cancer histopathological images. This review paper summarizes various traditional and deep learning based methods developed to analyze breast cancer histopathological images. Initially, the characteristics of breast cancer histopathological images are discussed. A detailed discussion on the various potential regions of interest is presented which is crucial for the development of Computer-Aided Diagnostic systems. We summarize the recent trends and choices made during the selection of medical image processing techniques. Finally, a detailed discussion on the various challenges involved in the analysis of BCHI is presented along with the future scope.

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Section: Image Processing Approachesmentioning

confidence: 99%

Breast histopathological image analysis using image processing techniques for diagnostic purposes: A methodological review

2021

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“…The algorithm was classified with an average accuracy of 89.23% for disease classification. Saini et al [15] The class imbalanced distribution results in a deterioration in the efficiency of the classification models owing to a bias in the category toward this dominant class to tackle this issue. The author has suggested a different learning technique that includes a deep transfer network in conjunction with the deep convolution generative adversarial network.…”

Section: Literature Surveymentioning

confidence: 99%

A Comparative Study on Augmented Analytics Using Deep Learning Techniques

Anusha

Kiruthika

2021

Smart Innovation, Systems and Technologies

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Image augmentation is the most recognized type of data augmentation and intrinsic development for transforming image diversities in the training dataset that belongs to a similar class as the novel image. In the area of image augmentation handling, a collection of operations is shifting, flipping, zooming, cropping, rotation, and transformation in color space. A wide range of applications frequently used the aspects of deep learning are industry, science, and government domain, namely adaptive testing, image classification, computer vision, object detection, and face recognition and has achieved substantial development and accomplishment of deep learning. This study concentrates on the most important challenges present in the image estimation level that have a significant effect on dimension reduction, pooling, and edge detection. The deep learning methods involved here are convolution neural network (CNN), generative adversarial network (GAN), and deep convolution neural network (DCNN). Finally, a comparative study has performed a massive literature survey on various deep learning models.

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“…It refers to the datasets with skewed classes when dealing with multiple disease classes. With class-imbalanced datasets, deep neural networks train better on the classes with a large number of images rather than the class with a limited number of images [8]. Data augmentation is one of the potential solutions to address the class imbalance, as well as data limitation problems [9].…”

Section: Introductionmentioning

confidence: 99%

A Survey on Training Challenges in Generative Adversarial Networks for Biomedical Image Analysis

Saad¹,

O’Reilly²,

Rehmani³

2022

Preprint

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In biomedical image analysis, the applicability of deep learning methods is directly impacted by the quantity of image data available. This is due to deep learning models requiring large image datasets to provide high-level performance. Generative Adversarial Networks (GANs) have been widely utilized to address data limitations through the generation of synthetic biomedical images. GANs consist of two models. The generator, a model that learns how to produce synthetic images based on the feedback it receives. The discriminator, a model that classifies an image as synthetic or real and provides feedback to the generator. Throughout the training process, a GAN can experience several technical challenges that impede the generation of suitable synthetic imagery. First, the mode collapse problem whereby the generator either produces an identical image or produces a uniform image from distinct input features. Second, the non-convergence problem whereby the gradient descent optimizer fails to reach a Nash equilibrium. Thirdly, the vanishing gradient problem whereby unstable training behavior occurs due to the discriminator achieving optimal classification performance resulting in no meaningful feedback being provided to the generator. These problems result in the production of synthetic imagery that is blurry, unrealistic, and less diverse. To date, there has been no survey article outlining the impact of these technical challenges in the context of the biomedical imagery domain. This work presents a review and taxonomy based on solutions to the training problems of GANs in the biomedical imaging domain. This survey highlights important challenges and outlines future research directions about the training of GANs in the domain of biomedical imagery.

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Deep transfer with minority data augmentation for imbalanced breast cancer dataset

Cited by 102 publications

References 26 publications

Breast histopathological image analysis using image processing techniques for diagnostic purposes: A methodological review

Breast histopathological image analysis using image processing techniques for diagnostic purposes: A methodological review

A Comparative Study on Augmented Analytics Using Deep Learning Techniques

A Survey on Training Challenges in Generative Adversarial Networks for Biomedical Image Analysis

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