Multi-classification of Breast Cancer Histology Images by Using a Fine-Tuning Strategy

Frucci, Maria; Riccio, Daniel

doi:10.1007/978-3-319-93000-8_87

Cited by 49 publications

(24 citation statements)

References 15 publications

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“…Then, for inference, each model in the ensemble predicts the cancer grade in the input image and a majority voting scheme is posteriorly used for assigning the class associated with the input. (Brancati et al, 2018) proposed a deep learning approach based on a fine-tuning strategy by exploiting transfer learning on an ensemble of ResNet (He et al, 2016) models. ResNet was preferred to other deep network architectures because it has a small number of parameters and shows a relatively low complexity in comparison to other models.…”

Section: Partmentioning

confidence: 99%

BACH: Grand challenge on breast cancer histology images

Aresta

Araújo

Kwok³

et al. 2019

Medical Image Analysis

Self Cite

469

301

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A B S T R A C TBreast cancer is the most common invasive cancer in women, affecting more than 10% of women worldwide. Microscopic analysis of a biopsy remains one of the most important methods to diagnose the type of breast cancer. This requires specialized analysis by pathologists, in a task that i) is highly time-and cost-consuming and ii) often leads to nonconsensual results. The relevance and potential of automatic classification algorithms using hematoxylin-eosin stained histopathological images has already been demonstrated, but the reported results are still sub-optimal for clinical use. With the goal of advancing the state-of-the-art in automatic classification, the Grand Challenge on BreAst Cancer Histology images (BACH) was organized in conjunction with the 15th International Conference on Image Analysis and Recognition (ICIAR 2018). BACH aimed at the classification and localization of clinically relevant histopathological classes in microscopy and whole-slide images from a large annotated dataset, specifically compiled and made publicly available for the challenge. Following a positive response from the scientific community, a total of 64 submissions, out of 677 registrations, effectively entered the competition. The submitted algorithms improved the state-of-the-art in automatic classification of breast cancer with microscopy images to an accuracy of 87%. Convolutional neuronal networks were the most successful methodology in the BACH challenge. Detailed analysis of the collective results allowed the identification of remaining challenges in the field and recommendations for future developments. The BACH dataset remains publicly available as to promote further improvements to the field of automatic classification in digital pathology.

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Section: Partmentioning

confidence: 99%

BACH: Grand challenge on breast cancer histology images

Aresta

Araújo

Kwok³

et al. 2019

Medical Image Analysis

Self Cite

469

301

View full text Add to dashboard Cite

show abstract

“…Regarding the ICIAR dataset, Table 13 verifies the competence of Histo-CADx compared to other recent related studies. The accuracy achieved by Histo-CADx is higher than Mahbod et al (2018) , Rakhlin et al (2018) , Nazeri, Aminpour & Ebrahimi (2018) , Kausar et al (2019) , Roy et al (2019) , Sheikh, Lee & Cho (2020) , Wang et al (2020) , and Brancati, Frucci & Riccio (2018) . This is because some of the previous techniques used one to three CNNs networks individually to construct their CADx.…”

Section: Resultsmentioning

confidence: 77%

“…Regarding the BreakHis dataset, it is clear from Table 12 that the accuracy of Histo-CADx is higher than , Sudharshan et al (2019), Jiang et al (2019), Wang et al (2020), andBrancati, Frucci &Riccio (2018). This is because some of the previous techniques used one to three CNNs networks individually to construct their CADx.…”

Section: Comparison With Related Studiesmentioning

confidence: 98%

Histo-CADx: duo cascaded fusion stages for breast cancer diagnosis from histopathological images

Attallah

Anwar

Ghanem

et al. 2021

PeerJ Computer Science

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Breast cancer (BC) is one of the most common types of cancer that affects females worldwide. It may lead to irreversible complications and even death due to late diagnosis and treatment. The pathological analysis is considered the gold standard for BC detection, but it is a challenging task. Automatic diagnosis of BC could reduce death rates, by creating a computer aided diagnosis (CADx) system capable of accurately identifying BC at an early stage and decreasing the time consumed by pathologists during examinations. This paper proposes a novel CADx system named Histo-CADx for the automatic diagnosis of BC. Most related studies were based on individual deep learning methods. Also, studies did not examine the influence of fusing features from multiple CNNs and handcrafted features. In addition, related studies did not investigate the best combination of fused features that influence the performance of the CADx. Therefore, Histo-CADx is based on two stages of fusion. The first fusion stage involves the investigation of the impact of fusing several deep learning (DL) techniques with handcrafted feature extraction methods using the auto-encoder DL method. This stage also examines and searches for a suitable set of fused features that could improve the performance of Histo-CADx. The second fusion stage constructs a multiple classifier system (MCS) for fusing outputs from three classifiers, to further improve the accuracy of the proposed Histo-CADx. The performance of Histo-CADx is evaluated using two public datasets; specifically, the BreakHis and the ICIAR 2018 datasets. The results from the analysis of both datasets verified that the two fusion stages of Histo-CADx successfully improved the accuracy of the CADx compared to CADx constructed with individual features. Furthermore, using the auto-encoder for the fusion process has reduced the computation cost of the system. Moreover, the results after the two fusion stages confirmed that Histo-CADx is reliable and has the capacity of classifying BC more accurately compared to other latest studies. Consequently, it can be used by pathologists to help them in the accurate diagnosis of BC. In addition, it can decrease the time and effort needed by medical experts during the examination.

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“…It was concluded that CNN was the most successful method to classify these breast cancer images [33]. The top performers [34,35,36] in this challenge used the architecture of an existing network such as Resnet [37], Densenet [38], Inception [39], VGG16 [40], etc and pre-trained these networks using ImageNet [41]. Although CNN perform better for image classification than other machine learning techniques in terms of accuracy, their parameters are deterministic and thus can not provide any measure of uncertainty in predictions.…”

Section: State-of-the-art Machine Learning Algorithms Formentioning

confidence: 98%

Explanation and Use of Uncertainty Quantified by Bayesian Neural NetworkClassifiers for Breast Histopathology Images

Khairnar¹,

Thiagarajan²,

Ghosh³

2020

Preprint

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Convolutional neural network (CNN) based classification models have been successfully used on histopathological images for the detection of diseases. Despite its success, CNN may yield erroneous or overfitted results when the data is not sufficiently large or is biased. To overcome these limitations of CNN and to provide uncertainty quantification Bayesian CNN is recently proposed. However, we show that Bayesian-CNN still suffers from inaccuracies, especially in negative predictions. In the present work, we extend the Bayesian-CNN to improve accuracy and the rate of convergence. The proposed model is called modified Bayesian-CNN. The novelty of the proposed model lies in an adaptive activation function that contains a learnable parameter for each of the neurons. This adaptive activation function dynamically changes the loss function thereby providing faster convergence and better accuracy. The uncertainties associated with the predictions are obtained since the model learns a probability distribution on the network parameters. It reduces overfitting through an ensemble averaging over networks, which in turn improves accuracy on the unknown data. The proposed model demonstrates significant improvement by nearly eliminating overfitting and remarkably reducing (about 38%) the number of false-negative predictions. We found that the proposed model predicts higher uncertainty for images having features of both the classes. The uncertainty in the predictions of individual images can be used to decide when further human-expert intervention is needed. These findings have the potential to advance the state-of-the-art machine learning-based automatic classification for histopathological images.

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Multi-classification of Breast Cancer Histology Images by Using a Fine-Tuning Strategy

Cited by 49 publications

References 15 publications

BACH: Grand challenge on breast cancer histology images

BACH: Grand challenge on breast cancer histology images

Histo-CADx: duo cascaded fusion stages for breast cancer diagnosis from histopathological images

Explanation and Use of Uncertainty Quantified by Bayesian Neural NetworkClassifiers for Breast Histopathology Images

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