Classification of Breast Cancer Histology Image using Ensemble of Pre-trained Neural Networks

Chennamsetty, Sai Saketh; Safwan, Mohammed; Alex, Varghese

doi:10.1007/978-3-319-93000-8_91

Cited by 38 publications

(19 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Fine-tuning for classification is usually performed as follows: 1) the network is initialized with weights trained to solve a natural image classification problem such as the ImageNet classification task; 2) the classification head, usually a fully-connected layer, is replaced by a new one with randomly initialized parameters; 3) initially, the new classification head is trained for a fixed number of iterations by inputting the medical images and inhibiting the filters of the pre-trained model to change; 4) then, different blocks of the pre-trained model are progressively allowed to learn and adapt to the new features, allowing the model to move to new local optima and increase the overall performance of the network. (Chennamsetty et al, 2018) used an ensemble of Ima-geNet pre-trained CNNs to classify the images from Part A. Specifically, the algorithm is composed of a ResNet-101 (He et al, 2016) and two DenseNet-161 (Huang et al, 2017) networks fine-tunned with images from varying data normalization schemes.…”

Section: Teammentioning

confidence: 99%

BACH: Grand challenge on breast cancer histology images

Aresta

Araújo

Kwok³

et al. 2019

Medical Image Analysis

Self Cite

504

301

View full text Add to dashboard Cite

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.

show abstract

Section: Teammentioning

confidence: 99%

BACH: Grand challenge on breast cancer histology images

Aresta

Araújo

Kwok³

et al. 2019

Medical Image Analysis

Self Cite

504

301

View full text Add to dashboard Cite

show abstract

“…There are several state-of-the-art pre-trained deep learning models available. Because of the superior performance of deep learning models in computer vision and image processing, researchers also started using them in breast cancer diagnosis [1,5,7,12,12,15,18,19,21,23,24,26,28,37,38]. Initially, to leverage the benefit of deep learning with machine learning, Araujo et al [1] combined DLM and SVM to classify breast cancer histology images.…”

Section: Deep Learning-based Methodsmentioning

confidence: 99%

“…Similarly, Rakhlin et al [26] also extracted features using several DLMs and classified using gradient boosted trees algorithm [16]. Similarly, Chennamsetty et al [7] also designed an ensemble of three DLMs, each of which was trained on different configurations that won the BACH challenge [2]. Recently, Yang et al [38] presented an ensemble model, called EMS-Net, of DLMs using multi-scale features and fine-tuning of several pre-trained models.…”

Section: Deep Learning-based Methodsmentioning

confidence: 99%

Fusion of whole and part features for the classification of histopathological image of breast tissue

Sitaula

Aryal

2020

Health Inf Sci Syst

View full text Add to dashboard Cite

Purpose: Nowadays Computer-Aided Diagnosis (CAD) models, particularly those based on deep learning, have been widely used to analyze histopathological images in breast cancer diagnosis. However, due to the limited availability of such images, it is always tedious to train deep learning models that require a huge amount of training data. In this paper, we propose a new deep learning-based CAD framework that can work with less amount of training data.Methods: We use pre-trained models to extract image features that can then be used with any classifier. Our proposed features are extracted by the fusion of two different types of features (foreground and background) at two levels (whole-level and part-level). Foreground and background feature to capture information about different structures and their layout in microscopic images of breast tissues. Similarly, part-level and whole-level features capture are useful in detecting interesting regions scattered in high-resolution histopathological images at local and whole image levels. At each level, we use VGG16 models pre-trained on ImageNet and Places datasets to extract foreground and background features, respectively. All features are extracted from mid-level pooling layers of such models. Results:We show that proposed fused features with a Support Vector Classifier (SVM) produce better classification accuracy than recent methods on BACH dataset and our approach is orders of magnitude faster than the best performing recent method (EMS-Net). Conclusion:We believe that our method would be another alternative in the diagnosis of breast cancer because of performance and prediction time.

show abstract

“…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

View full text Add to dashboard Cite

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.

show abstract

Classification of Breast Cancer Histology Image using Ensemble of Pre-trained Neural Networks

Cited by 38 publications

References 7 publications

BACH: Grand challenge on breast cancer histology images

BACH: Grand challenge on breast cancer histology images

Fusion of whole and part features for the classification of histopathological image of breast tissue

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

Contact Info

Product

Resources

About