SmallMitosis: Small Size Mitotic Cells Detection in Breast Histopathology Images

Kausar, Tasleem; Wang, Mingjiang; Ashraf, Muhammad Adnan; Kausar, Adeeba

doi:10.1109/access.2020.3044625

Cited by 23 publications

(15 citation statements)

References 44 publications

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“…Deep learning techniques are gaining the interest of researchers these days. Mitosis countbased cancer detection from breast histopathology images has been proposed using the Atrous Fully Connected Neural Network (A-FCNN) for segmentation and multi-scale and region-based CNN (MS-RCNN) model for detection [12]. A multi-instant pooling layer-based CNN (MI-CNN) model [13] has been suggested for breast cancer detection from histopathology images.…”

Section: Literature Surveymentioning

confidence: 99%

Design of ensemble recurrent model with stacked fuzzy ARTMAP for breast cancer detection

Das

Mohanty

2022

ACI

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PurposeIn time and accurate detection of cancer can save the life of the person affected. According to the World Health Organization (WHO), breast cancer occupies the most frequent incidence among all the cancers whereas breast cancer takes fifth place in the case of mortality numbers. Out of many image processing techniques, certain works have focused on convolutional neural networks (CNNs) for processing these images. However, deep learning models are to be explored well.Design/methodology/approachIn this work, multivariate statistics-based kernel principal component analysis (KPCA) is used for essential features. KPCA is simultaneously helpful for denoising the data. These features are processed through a heterogeneous ensemble model that consists of three base models. The base models comprise recurrent neural network (RNN), long short-term memory (LSTM) and gated recurrent unit (GRU). The outcomes of these base learners are fed to fuzzy adaptive resonance theory mapping (ARTMAP) model for decision making as the nodes are added to the F_2ˆa layer if the winning criteria are fulfilled that makes the ARTMAP model more robust.FindingsThe proposed model is verified using breast histopathology image dataset publicly available at Kaggle. The model provides 99.36% training accuracy and 98.72% validation accuracy. The proposed model utilizes data processing in all aspects, i.e. image denoising to reduce the data redundancy, training by ensemble learning to provide higher results than that of single models. The final classification by a fuzzy ARTMAP model that controls the number of nodes depending upon the performance makes robust accurate classification.Research limitations/implicationsResearch in the field of medical applications is an ongoing method. More advanced algorithms are being developed for better classification. Still, the scope is there to design the models in terms of better performance, practicability and cost efficiency in the future. Also, the ensemble models may be chosen with different combinations and characteristics. Only signal instead of images may be verified for this proposed model. Experimental analysis shows the improved performance of the proposed model. This method needs to be verified using practical models. Also, the practical implementation will be carried out for its real-time performance and cost efficiency.Originality/valueThe proposed model is utilized for denoising and to reduce the data redundancy so that the feature selection is done using KPCA. Training and classification are performed using heterogeneous ensemble model designed using RNN, LSTM and GRU as base classifiers to provide higher results than that of single models. Use of adaptive fuzzy mapping model makes the final classification accurate. The effectiveness of combining these methods to a single model is analyzed in this work.

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Section: Literature Surveymentioning

confidence: 99%

Design of ensemble recurrent model with stacked fuzzy ARTMAP for breast cancer detection

Das

Mohanty

2022

ACI

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“…In addition, AI models can eliminate the need for a large number of RT-PCR test kits and the waiting time for test results. To this end, deep learning algorithms such as convolution neural networks (CNNs) [9] have become fascinating and optimized solutions in the medical, especially in radiology, and pathology fields to classify the level and type of disease [10][11][12]. The research on automated Covid-19 detection using deep learning techniques is continuously getting more attention [13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

SD-GAN: A Style Distribution Transfer Generative Adversarial Network for Covid-19 Detection Through X-Ray Images

Kausar

et al. 2023

IEEE Access

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The Covid-19 pandemic is a prevalent health concern around the world in recent times. Therefore, it is essential to screen the infected patients at the primary stage to prevent secondary infections from person to person. The reverse transcription polymerase chain reaction (RT-PCR) test is commonly performed for Covid-19 diagnosis, while it requires significant effort from health professionals. Automated Covid-19 diagnosis using chest X-ray images is one of the promising directions to screen infected patients quickly and effectively. Automatic diagnostic approaches are used with the assumption that data originating from different sources have the same feature distributions. However, the X-ray images generated in different laboratories using different devices experience style variations e.g., intensity and contrast which contradict the above assumption. The prediction performance of deep models trained on such heterogeneous images of different distributions with different noises is affected. To address this issue, we have designed an automatic end-to-end adaptive normalization-based model called style distribution transfer generative adversarial network (SD-GAN). The designed model is equipped with the generative adversarial network (GAN) and task-specific classifier to transform the style distribution of images between different datasets belonging to different race people and carried out Covid-19 detection effectively. Evaluated results on four different X-ray datasets show the superiority of the proposed model to state-ofthe-art methods in terms of the visual quality of style transferred images and the accuracy of Covid-19 infected patient detection. SD-GAN is publicly available at: https://github.com/tasleem-hello/SD-GAN/tree/SD-GAN.

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“…Thus, it is important to condense the intralaboratory variations between train and test parts of a dataset for training efficient deep models. In this context, so far, several automated stain normalization techniques [11][12][13][14] have been developed to standardize the staining inconsistencies in histopathology images. Such techniques could be used as pre-processing strategies for cancer classification [11,12] and detection [13,14] models to improve the accuracies.…”

Section: Introductionmentioning

confidence: 99%

“…In this context, so far, several automated stain normalization techniques [11][12][13][14] have been developed to standardize the staining inconsistencies in histopathology images. Such techniques could be used as pre-processing strategies for cancer classification [11,12] and detection [13,14] models to improve the accuracies. However, previously proposed stain normalization algorithms suffer from the errors induced by color channel independence assumption.…”

Section: Introductionmentioning

confidence: 99%

SA-GAN: Stain Acclimation Generative Adversarial Network for Histopathology Image Analysis

Kausar

Ashraf

et al. 2021

Applied Sciences

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Histopathological image analysis is an examination of tissue under a light microscope for cancerous disease diagnosis. Computer-assisted diagnosis (CAD) systems work well by diagnosing cancer from histopathology images. However, stain variability in histopathology images is inevitable due to the use of different staining processes, operator ability, and scanner specifications. These stain variations present in histopathology images affect the accuracy of the CAD systems. Various stain normalization techniques have been developed to cope with inter-variability issues, allowing standardizing the appearance of images. However, in stain normalization, these methods rely on the single reference image rather than incorporate color distributions of the entire dataset. In this paper, we design a novel machine learning-based model that takes advantage of whole dataset distributions as well as color statistics of a single target image instead of relying only on a single target image. The proposed deep model, called stain acclimation generative adversarial network (SA-GAN), consists of one generator and two discriminators. The generator maps the input images from the source domain to the target domain. Among discriminators, the first discriminator forces the generated images to maintain the color patterns as of target domain. While second discriminator forces the generated images to preserve the structure contents as of source domain. The proposed model is trained using a color attribute metric, extracted from a selected template image. Therefore, the designed model not only learns dataset-specific staining properties but also image-specific textural contents. Evaluated results on four different histopathology datasets show the efficacy of SA-GAN to acclimate stain contents and enhance the quality of normalization by obtaining the highest values of performance metrics. Additionally, the proposed method is also evaluated for multiclass cancer type classification task, showing a 6.9% improvement in accuracy on ICIAR 2018 hidden test data.

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SmallMitosis: Small Size Mitotic Cells Detection in Breast Histopathology Images

Cited by 23 publications

References 44 publications

Design of ensemble recurrent model with stacked fuzzy ARTMAP for breast cancer detection

Design of ensemble recurrent model with stacked fuzzy ARTMAP for breast cancer detection

SD-GAN: A Style Distribution Transfer Generative Adversarial Network for Covid-19 Detection Through X-Ray Images

SA-GAN: Stain Acclimation Generative Adversarial Network for Histopathology Image Analysis

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