Classification of breast lesions using cross-modal deep learning

Hadad, Omer; Bakalo, Ran; Ben-Ari, Rami; Hashoul, Sharbell Y.; Amit, Guy

doi:10.1109/isbi.2017.7950480

Cited by 55 publications

(38 citation statements)

References 7 publications

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“…Therefore, there are few publicly available large-scale labeled image datasets. However, transfer learning can overcome the problem of small datasets effectively [160]. Secondly, in the classification task of breast histopathology images, most of the pre-trained models are from the ImageNet Large Scale Visual Recognition Challenge [161].…”

Section: B Analysis Of Deep Ann Methodsmentioning

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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Section: B Analysis Of Deep Ann Methodsmentioning

confidence: 99%

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

et al. 2020

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“…Since natural images are RGB (three channels), whereas MRI is grayscale (single channel), this gives the option to input different pre- and postcontrast frames to different channels: to this aim, it is possible to adopt the 3TP method [ 97 ] or use the precontrast, first postcontrast, and second postcontrast frames, as shown in Figure 5 [ 100 ]. Fewer authors have evaluated multiple combination of sequences or multimodal including DCE-MRI, T2-weighted MR, and DWI [ 91 , 93 , 94 ]. Our findings are consistent with previous reviews which included also conventional ML methods [ 11 ].…”

Section: Perspectives Of Ai and Deep Learning In Breast Mrimentioning

confidence: 99%

Current Status and Future Perspectives of Artificial Intelligence in Magnetic Resonance Breast Imaging

Meyer-Bäse

Morra

Meyer-Bäse

et al. 2020

Contrast Media & Molecular Imaging

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Recent advances in artificial intelligence (AI) and deep learning (DL) have impacted many scientific fields including biomedical maging. Magnetic resonance imaging (MRI) is a well-established method in breast imaging with several indications including screening, staging, and therapy monitoring. The rapid development and subsequent implementation of AI into clinical breast MRI has the potential to affect clinical decision-making, guide treatment selection, and improve patient outcomes. The goal of this review is to provide a comprehensive picture of the current status and future perspectives of AI in breast MRI. We will review DL applications and compare them to standard data-driven techniques. We will emphasize the important aspect of developing quantitative imaging biomarkers for precision medicine and the potential of breast MRI and DL in this context. Finally, we will discuss future challenges of DL applications for breast MRI and an AI-augmented clinical decision strategy.

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“…In order to complete the proposed task, we followed the idea of using a data source as initialization for the framework, transferring some domain knowledge to the final training. This is a recent trend that has been applied to medical imaging processing for different purposes, such as cardiac structures segmentation [29], Alzheimer disease classification [30], radiological breast lesions classification [31] and even digital pathology classification/segmentation [32].…”

Section: Related Workmentioning

confidence: 99%

Weakly-Supervised Classification of HER2 Expression in Breast Cancer Haematoxylin and Eosin Stained Slides

et al. 2020

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Human epidermal growth factor receptor 2 (HER2) evaluation commonly requires immunohistochemistry (IHC) tests on breast cancer tissue, in addition to the standard haematoxylin and eosin (H&E) staining tests. Additional costs and time spent on further testing might be avoided if HER2 overexpression could be effectively inferred from H&E stained slides, as a preliminary indication of the IHC result. In this paper, we propose the first method that aims to achieve this goal. The proposed method is based on multiple instance learning (MIL), using a convolutional neural network (CNN) that separately processes H&E stained slide tiles and outputs an IHC label. This CNN is pretrained on IHC stained slide tiles but does not use these data during inference/testing. H&E tiles are extracted from invasive tumour areas segmented with the HASHI algorithm. The individual tile labels are then combined to obtain a single label for the whole slide. The network was trained on slides from the HER2 Scoring Contest dataset (HER2SC) and tested on two disjoint subsets of slides from the HER2SC database and the TCGA-TCIA-BRCA (BRCA) collection. The proposed method attained 83.3 % classification accuracy on the HER2SC test set and 53.8 % on the BRCA test set. Although further efforts should be devoted to achieving improved performance, the obtained results are promising, suggesting that it is possible to perform HER2 overexpression classification on H&E stained tissue slides.

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Classification of breast lesions using cross-modal deep learning

Cited by 55 publications

References 7 publications

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

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

Current Status and Future Perspectives of Artificial Intelligence in Magnetic Resonance Breast Imaging

Weakly-Supervised Classification of HER2 Expression in Breast Cancer Haematoxylin and Eosin Stained Slides

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