Breast Cancer Histopathological Image Classification with Adversarial Image Synthesis

Gheshlaghi, Saba Heidari; Kan, Chi Nok Enoch; Ye, Dong Hye

doi:10.1109/embc46164.2021.9630678

Cited by 19 publications

(12 citation statements)

References 12 publications

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“…A transfer learning approach with block-wise fine-tuning was utilized to learn the best features from the images to handle magnification dependent and magnification independent binary and eight class classification problems [5]. Recently, generate adversarial network gained lots of attention for addressing the problem of data imbalance [16]. In [33], the authors developed a model based on GAN to tackle the problem of data imbalance.…”

Section: Deep Learning Methodsmentioning

confidence: 99%

Region-based feature enhancement using channel-wise attention for classification of breast histopathological images

Rashmi

Prasad

Udupa

2022

Neural Comput & Applic

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Breast histopathological image analysis at 400x magnification is essential for the determination of malignant breast tumours. But manual analysis of these images is tedious, subjective, error-prone and requires domain knowledge. To this end, computer-aided tools are gaining much attention in the recent past as it aids pathologists and save time. Furthermore, advances in computational power have leveraged the usage of computer tools. Yet, usage of computer-aided tools to analyse these images is challenging due to various reasons such as heterogeneity of malignant tumours, colour variations and presence of artefacts. Moreover, these images are captured at high resolutions which pose a major challenge to designing deep learning models as it demands high computational requirements. In this context, the present work proposes a new approach to efficiently and effectively extract features from these high-resolution images. In addition, at 400x magnification, the characteristics and structure of nuclei play a prominent role in the decision of malignancy. In this regard, the study introduces a novel CNN architecture called as CWA-Net that uses a colour channel attention module to enhance the features of the potential regions of interest such as nuclei. The developed model is qualitatively and quantitatively evaluated on private and public datasets and achieved an accuracy of 0.95% and 0.96%, respectively. The experimental evaluation demonstrates that the proposed method outperforms state-of-the-art methods on both datasets.

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Section: Deep Learning Methodsmentioning

confidence: 99%

Region-based feature enhancement using channel-wise attention for classification of breast histopathological images

Rashmi

Prasad

Udupa

2022

Neural Comput & Applic

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“…al. successfully used transfer learning approach with XGBoost in breast cancer histopathological image classification [14]. We describe the details of each component of the method in the following.…”

Section: Methodsmentioning

confidence: 99%

Deep Learning From Imaging Genetics for Schizophrenia Classification

Florian

Calhoun

et al. 2022

2022 IEEE International Conference on Image Processing (ICIP)

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Schizophrenia (SZ) is a serious psychiatric disorder, causing substantial socioeconomic burden. Since an SZ patients' brain may have structural changes including reduced hippocampal and thalamic volume [1], brain MRI is becoming a popular imaging method studying SZ. In addition, as a genetic disorder, genetic information such as single nucleotide polymorphisms (SNP) plays an important role in distinguishing SZ. However, the structural and genetic changes in SZ patients are too subtle to be identified by human vision, so it is necessary to develop an automated method to find the nonlinear patterns associated with disease progression. Toward this, we propose a novel multi-modal deep learning approach where we combine both features from structural MRI (sMRI) and single-nucleotide polymorphisms (SNPs) for SZ classification. For sMRI, we extract convolutional features from a pre-trained deep neural network to capture morphological characteristics. For SNPs, we apply a layer-wise relevance propagation (LRP) method on a pre-trained 1-D convolutional network to identify SZ-linked SNPs. We then feed the combined features to a tree-based classifier for SZ diagnosis. Experimental results on clinical dataset showed classification accuracy was increased by 5.3% compared to the state of the art DenseNet using only sMRI data.

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“…Recently, generative adversarial networks (17) (GANs) are increasingly active and widely used in medical data synthesis because of their excellent data generation capabilities without explicitly modeling probability density functions (16,(18)(19)(20)(21). Augmenting existing medical images can significantly increase the sample size of the training set.…”

Section: Introductionmentioning

confidence: 99%

An improved multi-scale gradient generative adversarial network for enhancing classification of colorectal cancer histological images

Jiang,

Huang,

Luo

et al. 2023

Front. Oncol.

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IntroductionDeep learning-based solutions for histological image classification have gained attention in recent years due to their potential for objective evaluation of histological images. However, these methods often require a large number of expert annotations, which are both time-consuming and labor-intensive to obtain. Several scholars have proposed generative models to augment labeled data, but these often result in label uncertainty due to incomplete learning of the data distribution.MethodsTo alleviate these issues, a method called InceptionV3-SMSG-GAN has been proposed to enhance classification performance by generating high-quality images. Specifically, images synthesized by Multi-Scale Gradients Generative Adversarial Network (MSG-GAN) are selectively added to the training set through a selection mechanism utilizing a trained model to choose generated images with higher class probabilities. The selection mechanism filters the synthetic images that contain ambiguous category information, thus alleviating label uncertainty.ResultsExperimental results show that compared with the baseline method which uses InceptionV3, the proposed method can significantly improve the performance of pathological image classification from 86.87% to 89.54% for overall accuracy. Additionally, the quality of generated images is evaluated quantitatively using various commonly used evaluation metrics.DiscussionThe proposed InceptionV3-SMSG-GAN method exhibited good classification ability, where histological image could be divided into nine categories. Future work could focus on further refining the image generation and selection processes to optimize classification performance.

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Breast Cancer Histopathological Image Classification with Adversarial Image Synthesis

Cited by 19 publications

References 12 publications

Region-based feature enhancement using channel-wise attention for classification of breast histopathological images

Region-based feature enhancement using channel-wise attention for classification of breast histopathological images

Deep Learning From Imaging Genetics for Schizophrenia Classification

An improved multi-scale gradient generative adversarial network for enhancing classification of colorectal cancer histological images

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