Breast Cancer Diagnosis with Transfer Learning and Global Pooling

Kassani, Sara Hosseinzadeh; Kassani, Peyman Hosseinzadeh; Wesolowski, Michal J.; Schneider, Kevin A.; Deters, Ralph

doi:10.1109/ictc46691.2019.8939878

Cited by 76 publications

(46 citation statements)

References 39 publications

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“…We first apply the U-D pipeline to the regular and neural ion images. As can be seen in Table 1, DBSCAN finds significantly more clusters in the neural (40) pipeline than the regular (21) one. Furthermore, similarly to the lymph node data, the RIR is significantly higher for the neural pipeline than the regular one, indicating that the 16/24 retrieved clusters in the neural pipeline succeed in clustering together more isotopic ion images than the regular one, meaning that we not only get more clusters, but that these are also more relevant.…”

Section: Umap-dbscan Clustering Resultsmentioning

confidence: 96%

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Spatially-Aware Clustering of Ion Images in Mass Spectrometry Imaging Data Using Deep Learning

Zhang

Claesen

Moerman

et al. 2020

Preprint

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Computational analysis is crucial to capitalize on the wealth of spatio-molecular information generated by mass spectrometry imaging (MSI) experiments. Currently, the spatial information available in MSI data is often under-utilized, due to the challenges of in-depth spatial pattern extraction.The advent of deep learning has greatly facilitated such complex spatial analysis. In this work, we use a pre-trained neural network to extract high-level features from ion images in MSI data, and test whether this improves downstream data analysis. The resulting neural network interpretation of ion images, coined neural ion images, are used to cluster ion images based on spatial expressions.We evaluate the impact of neural ion images on two ion image clustering pipelines, namely DBSCAN clustering, combined with UMAP-based dimensionality reduction, and k-means clustering. In both pipelines, we compare regular and neural ion images from two different MSI datasets. All tested pipelines could extract underlying spatial patterns, but the neural network-based pipelines provided better assignment of ion images, with more fine-grained clusters, and greater consistency in the spatial structures assigned to individual clusters.Additionally, we introduce the Relative Isotope Ratio metric to quantitatively evaluate clustering quality. The resulting scores show that isotopical m/z values are more often clustered together in the neural network-based pipeline, indicating improved clustering outcomes.The usefulness of neural ion images extends beyond clustering towards a generic framework to incorporate spatial information into any MSI-focused machine learning pipeline, both supervised and unsupervised.

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Section: Umap-dbscan Clustering Resultsmentioning

confidence: 96%

“…Furthermore, the researchers noted that the way the neural networks interpreted the images was reminiscent of a human operator. A number of other studies have shown similar potential for 3/24 re-purposing of neural networks for various downstream tasks [21,25].…”

Section: Introductionmentioning

confidence: 87%

Spatially-Aware Clustering of Ion Images in Mass Spectrometry Imaging Data Using Deep Learning

Zhang

Claesen

Moerman

et al. 2020

Preprint

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“…Image-Wise (%) Nawaz W., et al [28] 81.25 Awan R, et al [67] 83.33 Guo Y., et al [68] 87.5 Vang Y.S., et al [69] 87.5 Sarker M., I et al [70] 89 Ferreira C.A., et al [66] 90 Kassani S. H., et al [41] 92.5 Wang, Z., et al [42] 93 Our model with Experiment 4 (same domain-transfer Learning) 96.1…”

Section: Methodsmentioning

confidence: 99%

“…Recently, researchers have implemented CNN models on ICIAR 2018 dataset images [16] to identify four different classes of hematoxylin-eosin-stained breast biopsy images, namely, invasive carcinoma, in-situ carcinoma, benign tumor and normal tissue, using the fine-tuned deep network fusion and hybrid Convolutional Neural Networks [41,42]. These methods accomplished the highest image-wise accuracies of 92.5% and 93%, respectively.…”

Section: Application Of ML To Breast Cancer Diagnosticmentioning

confidence: 99%

Optimizing the Performance of Breast Cancer Classification by Employing the Same Domain Transfer Learning from Hybrid Deep Convolutional Neural Network Model

et al. 2020

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Breast cancer is a significant factor in female mortality. An early cancer diagnosis leads to a reduction in the breast cancer death rate. With the help of a computer-aided diagnosis system, the efficiency increased, and the cost was reduced for the cancer diagnosis. Traditional breast cancer classification techniques are based on handcrafted features techniques, and their performance relies upon the chosen features. They also are very sensitive to different sizes and complex shapes. However, histopathological breast cancer images are very complex in shape. Currently, deep learning models have become an alternative solution for diagnosis, and have overcome the drawbacks of classical classification techniques. Although deep learning has performed well in various tasks of computer vision and pattern recognition, it still has some challenges. One of the main challenges is the lack of training data. To address this challenge and optimize the performance, we have utilized a transfer learning technique which is where the deep learning models train on a task, and then fine-tune the models for another task. We have employed transfer learning in two ways: Training our proposed model first on the same domain dataset, then on the target dataset, and training our model on a different domain dataset, then on the target dataset. We have empirically proven that the same domain transfer learning optimized the performance. Our hybrid model of parallel convolutional layers and residual links is utilized to classify hematoxylin–eosin-stained breast biopsy images into four classes: invasive carcinoma, in-situ carcinoma, benign tumor and normal tissue. To reduce the effect of overfitting, we have augmented the images with different image processing techniques. The proposed model achieved state-of-the-art performance, and it outperformed the latest methods by achieving a patch-wise classification accuracy of 90.5%, and an image-wise classification accuracy of 97.4% on the validation set. Moreover, we have achieved an image-wise classification accuracy of 96.1% on the test set of the microscopy ICIAR-2018 dataset.

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“…In the test set, APOD technology achieves accuracy of 90% in 4-class classification and 92.5% in 2-class classification. In [112], a method for the diagnosis of breast cancer histopathology images based on transfer learning and global pooling is proposed. Five DCNN architectures are used as feature extractors, namely Inception-V3, InceptionResNet-V2, Xception, VGG-16, and VGG-19.…”

Section: ) ''Bach'' Tasksmentioning

confidence: 99%

A Comprehensive Review for Breast Histopathology Image Analysis Using Classical and Deep Neural Networks

et al. 2020

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Breast cancer is one of the most common and deadliest cancers among women. Since histopathological images contain sufficient phenotypic information, they play an indispensable role in the diagnosis and treatment of breast cancers. To improve the accuracy and objectivity of Breast Histopathological Image Analysis (BHIA), Artificial Neural Network (ANN) approaches are widely used in the segmentation and classification tasks of breast histopathological images. In this review, we present a comprehensive overview of the BHIA techniques based on ANNs. First of all, we categorize the BHIA systems into classical and deep neural networks for in-depth investigation. Then, the relevant studies based on BHIA systems are presented. After that, we analyze the existing models to discover the most suitable algorithms. Finally, publicly accessible datasets, along with their download links, are provided for the convenience of future researchers.

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Breast Cancer Diagnosis with Transfer Learning and Global Pooling

Cited by 76 publications

References 39 publications

Spatially-Aware Clustering of Ion Images in Mass Spectrometry Imaging Data Using Deep Learning

Spatially-Aware Clustering of Ion Images in Mass Spectrometry Imaging Data Using Deep Learning

Optimizing the Performance of Breast Cancer Classification by Employing the Same Domain Transfer Learning from Hybrid Deep Convolutional Neural Network Model

A Comprehensive Review for Breast Histopathology Image Analysis Using Classical and Deep Neural Networks

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