Realistic Adversarial Data Augmentation for MR Image Segmentation

Chen, Chen; Chen, Qin; Qiu, Huaqi; Ouyang, Cheng; Wang, Shuo; Chen, Liang; Tarroni, Giacomo; Bai, Wenjia; Rueckert, Daniel

doi:10.1007/978-3-030-59710-8_65

Cited by 71 publications

(48 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, methods requiring less labeled data have gained attention, such as SSL (29)(30)(31)(32). Some studies have proven that when labeled data is limited, SSL can achieve good results in some medical images (29)(30)(31)(32). However, this conclusion has not been evaluated on a large scale, especially for medical images with large amounts of noise, artifacts, and low contrast, for example, breast ultrasound.…”

Section: Discussionmentioning

confidence: 99%

“…If semi-supervised learning is as accurate as supervised learning in the medical domain, then medical AI trained by semi-supervised learning may be more economical and faster in development by reducing the amount of data annotations needed. Further exploration is needed to determine whether semi-supervised learning using a small number of labeled images and a large number of unlabeled images can achieve satisfactory performance in medical image analysis, such as based on semi-supervised detection (28), magnetic resonance imaging image segmentation (29,30), data augmentation (31), and histology image classification (32).…”

Section: Original Articlementioning

confidence: 99%

See 1 more Smart Citation

Detection and recognition of ultrasound breast nodules based on semi-supervised deep learning: a powerful alternative strategy

Gao¹,

Liu²,

Zhu³

et al. 2021

Quant Imaging Med Surg

View full text Add to dashboard Cite

Background: The successful recognition of benign and malignant breast nodules using ultrasound images is based mainly on supervised learning that requires a large number of labeled images. However, because high-quality labeling is expensive and time-consuming, we hypothesized that semi-supervised learning could provide a low-cost and powerful alternative approach. This study aimed to develop an accurate semisupervised recognition method and compared its performance with supervised methods and sonographers.Methods: The faster region-based convolutional neural network was used for nodule detection from ultrasound images. A semi-supervised classifier based on the mean teacher model was proposed to recognize benign and malignant nodule images. The general performance of the proposed method on two datasets (8,966 nodules) was reported.Results: The detection accuracy was 0.88±0.03 and 0.86±0.02, respectively, on two testing sets (1,350 and 2,220 nodules). When 800 labeled training nodules were available, the proposed semi-supervised model plus 4,396 unlabeled nodules performed better than the supervised learning model (area under the curve (AUC): 0.934±0.026 vs. 0.83±0.050; 0.916±0.022 vs. 0.815±0.049). The performance of the semi-supervised model trained on 800 labeled and 4,396 unlabeled nodules was close to that of the supervised learning model trained on a massive number of labeled nodules (n=5,196) (AUC: 0.934±0.026 vs. 0.952±0.027; 0.916±0.022 vs. 0.918±0.017). Moreover, the semi-supervised model was better than the average accuracy of five human sonographers (AUC: 0.922 vs. 0.889). Conclusions:The semi-supervised model can achieve excellent performance for nodule recognition and be useful for medical sciences. The method reduced the number of labeled images required for training, thus significantly alleviating the difficulty in data preparation of medical artificial intelligence.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Original Articlementioning

confidence: 99%

Detection and recognition of ultrasound breast nodules based on semi-supervised deep learning: a powerful alternative strategy

Gao¹,

Liu²,

Zhu³

et al. 2021

Quant Imaging Med Surg

View full text Add to dashboard Cite

show abstract

“…The sequential decision method for fixing the image reconstruction model is implemented using reinforcement learning [ 58 ]. The adversarial data augmentation approach proposed by [ 59 ] for medical image segmentation was designed for deep neural network (DNN) model training induced by a shared type of artifact in magnetic resonance imaging (MRI).…”

Section: Related Workmentioning

confidence: 99%

Adversarial Attack and Defence through Adversarial Training and Feature Fusion for Diabetic Retinopathy Recognition

Lal

Rehman

Shah

et al. 2021

Sensors

View full text Add to dashboard Cite

Due to the rapid growth in artificial intelligence (AI) and deep learning (DL) approaches, the security and robustness of the deployed algorithms need to be guaranteed. The security susceptibility of the DL algorithms to adversarial examples has been widely acknowledged. The artificially created examples will lead to different instances negatively identified by the DL models that are humanly considered benign. Practical application in actual physical scenarios with adversarial threats shows their features. Thus, adversarial attacks and defense, including machine learning and its reliability, have drawn growing interest and, in recent years, has been a hot topic of research. We introduce a framework that provides a defensive model against the adversarial speckle-noise attack, the adversarial training, and a feature fusion strategy, which preserves the classification with correct labelling. We evaluate and analyze the adversarial attacks and defenses on the retinal fundus images for the Diabetic Retinopathy recognition problem, which is considered a state-of-the-art endeavor. Results obtained on the retinal fundus images, which are prone to adversarial attacks, are 99% accurate and prove that the proposed defensive model is robust.

show abstract

“…The optimizers are the algorithms or methods used to change the attributes of a neural network, such as weights and learning rate to reduce the losses [97]- [99]. A generic form of an optimizer is depicted in (1).…”

Section: ) Optimizersmentioning

confidence: 99%

Deep Neural Architectures for Medical Image Semantic Segmentation: Review

et al. 2021

View full text Add to dashboard Cite

Deep learning has an enormous impact on medical image analysis. Many computer-aided diagnostic systems equipped with deep networks are rapidly reducing human intervention in healthcare. Among several applications, medical image semantic segmentation is one of the core areas of active research to delineate the anatomical structures and other regions of interest. It has a significant contribution to healthcare and provides guided interventions, radiotherapy, and improved radiological diagnostics. The underlying article provides a brief overview of deep convolutional neural architecture, the platforms and applications of deep neural networks, metrics used for empirical evaluation, state-of-the-art semantic segmentation architectures based on a foundational convolution concept, and a review of publicly available medical image datasets highlighting four distinct regions of interest. The article also analyzes the existing work and provides open-ended potential research directions in deep medical image semantic segmentation.

show abstract

Realistic Adversarial Data Augmentation for MR Image Segmentation

Cited by 71 publications

References 19 publications

Detection and recognition of ultrasound breast nodules based on semi-supervised deep learning: a powerful alternative strategy

Detection and recognition of ultrasound breast nodules based on semi-supervised deep learning: a powerful alternative strategy

Adversarial Attack and Defence through Adversarial Training and Feature Fusion for Diabetic Retinopathy Recognition

Deep Neural Architectures for Medical Image Semantic Segmentation: Review

Contact Info

Product

Resources

About