Open access database of raw ultrasonic signals acquired from malignant and benign breast lesions

Piotrzkowska-Wróblewska, Hanna; Dobruch‐Sobczak, Katarzyna; Byra, Michał; Nowicki, Andrzej

doi:10.1002/mp.12538

Cited by 77 publications

(38 citation statements)

References 19 publications

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“…The OASBUD was originally used to assess the statistical properties of backscattered ultrasound echoes in breast tissue and to differentiate breast masses using transfer learning with CNNs . Detailed descriptions of both datasets can be found in the original papers …”

Section: Methodsmentioning

confidence: 99%

“…The B‐mode images from the UDIAT dataset were preprocessed and augmented in a similar way as in the case of our dataset. Raw ultrasonic echoes from the OASBUD dataset were used to reconstruct B‐mode images following the scheme proposed in the original paper . Echo amplitude was computed with the Hilbert transform and logarithmically compressed to reconstruct the B‐mode image.…”

Section: Methodsmentioning

confidence: 99%

“…To show the usefulness of the methods proposed in this paper, we also employed two publicly available breast mass datasets. 26,32 The first one, named UDIAT, consists of 163 B-mode images of breast masses (53 malignant and 110 benign) collected using Siemens ACUSON scanner from the UDIAT Diagnostic Centre of the Parc Tauli Corporation, Sabadell (Spain). This dataset was used by the authors to develop deep learning-based algorithms for the breast mass detection 26 and segmentation.…”

Section: A Datasetsmentioning

confidence: 99%

“…Raw ultrasonic echoes from the OASBUD dataset were used to reconstruct B-mode images following the scheme proposed in the original paper. 32 Echo amplitude was computed with the Hilbert transform and logarithmically compressed to reconstruct the B-mode image. Next, the data were preprocessed and augmented as in the case of the other datasets.…”

Section: B Transfer Learningmentioning

confidence: 99%

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Breast mass classification in sonography with transfer learning using a deep convolutional neural network and color conversion

et al. 2019

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Purpose We propose a deep learning‐based approach to breast mass classification in sonography and compare it with the assessment of four experienced radiologists employing breast imaging reporting and data system 4th edition lexicon and assessment protocol. Methods Several transfer learning techniques are employed to develop classifiers based on a set of 882 ultrasound images of breast masses. Additionally, we introduce the concept of a matching layer. The aim of this layer is to rescale pixel intensities of the grayscale ultrasound images and convert those images to red, green, blue (RGB) to more efficiently utilize the discriminative power of the convolutional neural network pretrained on the ImageNet dataset. We present how this conversion can be determined during fine‐tuning using back‐propagation. Next, we compare the performance of the transfer learning techniques with and without the color conversion. To show the usefulness of our approach, we additionally evaluate it using two publicly available datasets. Results Color conversion increased the areas under the receiver operating curve for each transfer learning method. For the better‐performing approach utilizing the fine‐tuning and the matching layer, the area under the curve was equal to 0.936 on a test set of 150 cases. The areas under the curves for the radiologists reading the same set of cases ranged from 0.806 to 0.882. In the case of the two separate datasets, utilizing the proposed approach we achieved areas under the curve of around 0.890. Conclusions The concept of the matching layer is generalizable and can be used to improve the overall performance of the transfer learning techniques using deep convolutional neural networks. When fully developed as a clinical tool, the methods proposed in this paper have the potential to help radiologists with breast mass classification in ultrasound.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: A Datasetsmentioning

confidence: 99%

Section: B Transfer Learningmentioning

confidence: 99%

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Breast mass classification in sonography with transfer learning using a deep convolutional neural network and color conversion

et al. 2019

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“…This study was approved by the Institutional Review Board. More details regarding the dataset can be found in the original paper [11].…”

Section: A Datasetmentioning

confidence: 99%

Impact of Ultrasound Image Reconstruction Method on Breast Lesion Classification with Deep Learning

Byra

Sznajder

Koržinek

et al. 2019

Pattern Recognition and Image Analysis

Self Cite

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Deep learning algorithms, especially convolutional neural networks, have become a methodology of choice in medical image analysis. However, recent studies in computer vision show that even a small modification of input image intensities may cause a deep learning model to classify the image differently. In medical imaging, the distribution of image intensities is related to applied image reconstruction algorithm. In this paper we investigate the impact of ultrasound image reconstruction method on breast lesion classification with neural transfer learning. Due to high dynamic range raw ultrasonic signals are commonly compressed in order to reconstruct B-mode images. Based on raw data acquired from breast lesions, we reconstruct B-mode images using different compression levels. Next, transfer learning is applied for classification. Differently reconstructed images are employed for training and evaluation. We show that the modification of the reconstruction algorithm leads to decrease of classification performance. As a remedy, we propose a method of data augmentation. We show that the augmentation of the training set with differently reconstructed B-mode images leads to a more robust and efficient classification. Our study suggests that it is important to take into account image reconstruction algorithms implemented in medical scanners during development of computer aided diagnosis systems.

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Functional Materials for Memristor‐Based Reservoir Computing: Dynamics and Applications

Zhang

Qin

Zhang

et al. 2023

Adv Funct Materials

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The booming development of artificial intelligence (AI) requires faster physical processing units as well as more efficient algorithms. Recently, reservoir computing (RC) has emerged as an alternative brain‐inspired framework for fast learning with low training cost, since only the weights associated with the output layers should be trained. Physical RC becomes one of the leading paradigms for computation using high‐dimensional, nonlinear, dynamic substrates. Among them, memristor appears to be a simple, adaptable, and efficient framework for constructing physical RC since they exhibit nonlinear features and memory behavior, while memristor‐implemented artificial neural networks display increasing popularity towards neuromorphic computing. In this review, the memristor‐implemented RC systems from the following aspects: architectures, materials, and applications are summarized. It starts with an introduction to the RC structures that can be simulated with memristor blocks. Specific interest then focuses on the dynamic memory behaviors of memristors based on various material systems, optimizing the understanding of the relationship between the relaxation behaviors and materials, which provides guidance and references for building RC systems coped with on‐demand application scenarios. Furthermore, recent advances in the application of memristor‐based physical RC systems are surveyed. In the end, the further prospects of memristor‐implemented RC system in a material view are envisaged.

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Open access database of raw ultrasonic signals acquired from malignant and benign breast lesions

Abstract: The database can be used to test quantitative ultrasound techniques and ultrasound image processing algorithms, or to develop computer-aided diagnosis systems.

Cited by 77 publications

References 19 publications

Breast mass classification in sonography with transfer learning using a deep convolutional neural network and color conversion

Breast mass classification in sonography with transfer learning using a deep convolutional neural network and color conversion

Impact of Ultrasound Image Reconstruction Method on Breast Lesion Classification with Deep Learning

Functional Materials for Memristor‐Based Reservoir Computing: Dynamics and Applications

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