Tabela A z publikacjami w czasopismach wyróżnionych w Journal Citation Reports (JCR) 
Tabela B z publikacjami w czasopismach zagranicznych i krajowych, wyróżnionych na liście MNSzW
Publikacje konferencyjne indeksowane w bazie Web of Science Core Collection
Inne publikacje w pozostałych czasopismach i wydawnictwach konferencyjnych
Afiliacja IPPT PAN

1.Han A., Byra M., Heba E., Andre M.P., Erdman J.W.Jr., Loomba R., Sirlin C.B., O'Brien W.D.Jr., Noninvasive diagnosis of nonalcoholic fatty liver disease and quantification of liver fat with radiofrequency ultrasound data using one-dimensional convolutional neural networks, Radiology, ISSN: 0033-8419, DOI: 10.1148/radiol.2020191160, Vol.295, No.2, pp.342-350, 2020
Han A., Byra M., Heba E., Andre M.P., Erdman J.W.Jr., Loomba R., Sirlin C.B., O'Brien W.D.Jr., Noninvasive diagnosis of nonalcoholic fatty liver disease and quantification of liver fat with radiofrequency ultrasound data using one-dimensional convolutional neural networks, Radiology, ISSN: 0033-8419, DOI: 10.1148/radiol.2020191160, Vol.295, No.2, pp.342-350, 2020

Abstract:
Background: Radiofrequency ultrasound data from the liver contain rich information about liver microstructure and composition. Deep learning might exploit such information to assess nonalcoholic fatty liver disease (NAFLD). Purpose: To develop and evaluate deep learning algorithms that use radiofrequency data for NAFLD assessment, with MRI-derived proton density fat fraction (PDFF) as the reference. Materials and Methods: A HIPAA-compliant secondary analysis of a single-center prospective study was performed for adult participants with NAFLD and control participants without liver disease. Participants in the parent study were recruited between February 2012 and March 2014 and underwent same-day US and MRI of the liver. Participants were randomly divided into an equal number of training and test groups. The training group was used to develop two algorithms via cross-validation: a classifier to diagnose NAFLD (MRI PDFF ≥ 5%) and a fat fraction estimator to predict MRI PDFF. Both algorithms used one-dimensional convolutional neural networks. The test group was used to evaluate the classifier for sensitivity, specificity, positive predictive value, negative predictive value, and accuracy and to evaluate the estimator for correlation, bias, limits of agreements, and linearity between predicted fat fraction and MRI PDFF. Results: A total of 204 participants were analyzed, 140 had NAFLD (mean age, 52 years ± 14 [standard deviation]; 82 women) and 64 were control participants (mean age, 46 years ± 21; 42 women). In the test group, the classifier provided 96% (95% confidence interval [CI]: 90%, 99%) (98 of 102) accuracy for NAFLD diagnosis (sensitivity, 97% [95% CI: 90%, 100%], 68 of 70; specificity, 94% [95% CI: 79%, 99%], 30 of 32; positive predictive value, 97% [95% CI: 90%, 99%], 68 of 70; negative predictive value, 94% [95% CI: 79%, 98%], 30 of 32). The estimator-predicted fat fraction correlated with MRI PDFF (Pearson r = 0.85). The mean bias was 0.8% (P = .08), and 95% limits of agreement were -7.6% to 9.1%. The predicted fat fraction was linear with an MRI PDFF of 18% or less (r = 0.89, slope = 1.1, intercept = 1.3) and nonlinear with an MRI PDFF greater than 18%. Conclusion: Deep learning algorithms using radiofrequency ultrasound data are accurate for diagnosis of nonalcoholic fatty liver disease and hepatic fat fraction quantification when other causes of steatosis are excluded.

(200p.)
2.Byra M., Jarosik P., Szubert A., Galperine M., Ojeda-Fournier H., Olson L., O'Boyle M., Comstock Ch., Andre M., Breast mass segmentation in ultrasound with selective kernel U-Net convolutional neural network, Biomedical Signal Processing and Control, ISSN: 1746-8094, DOI: 10.1016/j.bspc.2020.102027, Vol.61, pp.102027-1-10, 2020
Byra M., Jarosik P., Szubert A., Galperine M., Ojeda-Fournier H., Olson L., O'Boyle M., Comstock Ch., Andre M., Breast mass segmentation in ultrasound with selective kernel U-Net convolutional neural network, Biomedical Signal Processing and Control, ISSN: 1746-8094, DOI: 10.1016/j.bspc.2020.102027, Vol.61, pp.102027-1-10, 2020

Abstract:
In this work, we propose a deep learning method for breast mass segmentation in ultrasound (US). Variations in breast mass size and image characteristics make the automatic segmentation difficult. To addressthis issue, we developed a selective kernel (SK) U-Net convolutional neural network. The aim of the SKswas to adjust network's receptive fields via an attention mechanism, and fuse feature maps extractedwith dilated and conventional convolutions. The proposed method was developed and evaluated usingUS images collected from 882 breast masses. Moreover, we used three datasets of US images collectedat different medical centers for testing (893 US images). On our test set of 150 US images, the SK-U-Netachieved mean Dice score of 0.826, and outperformed regular U-Net, Dice score of 0.778. When evaluatedon three separate datasets, the proposed method yielded mean Dice scores ranging from 0.646 to 0.780. Additional fine-tuning of our better-performing model with data collected at different centers improvedmean Dice scores by ~6%. SK-U-Net utilized both dilated and regular convolutions to process US images. We found strong correlation, Spearman's rank coefficient of 0.7, between the utilization of dilated convo-lutions and breast mass size in the case of network's expansion path. Our study shows the usefulness ofdeep learning methods for breast mass segmentation. SK-U-Net implementation and pre-trained weightscan be found at github.com/mbyr/bus_seg.

Keywords:
attention mechanism, breast mass segmentation, convolutional neural networks, deep learning, receptive field, ultrasound imaging

(140p.)
3.Byra M., Dobruch-Sobczak K., Klimonda Z., Piotrzkowska-Wróblewska H., Litniewski J., Early prediction of response to neoadjuvant chemotherapy in breast cancer sonography using Siamese convolutional neural networks, IEEE Journal of Biomedical and Health Informatics, ISSN: 2168-2208, DOI: 10.1109/JBHI.2020.3008040, pp.1-8, 2020
Byra M., Dobruch-Sobczak K., Klimonda Z., Piotrzkowska-Wróblewska H., Litniewski J., Early prediction of response to neoadjuvant chemotherapy in breast cancer sonography using Siamese convolutional neural networks, IEEE Journal of Biomedical and Health Informatics, ISSN: 2168-2208, DOI: 10.1109/JBHI.2020.3008040, pp.1-8, 2020

Abstract:
Early prediction of response to neoadjuvant chemotherapy (NAC) in breast cancer is crucial for guiding therapy decisions. In this work, we propose a deep learning based approach for the early NAC response prediction in ultrasound (US) imaging. We used transfer learning with deep convolutional neural networks (CNNs) to develop the response prediction models. The usefulness of two transfer learning techniques was examined. First, a CNN pre-trained on the ImageNet dataset was utilized. Second, we applied double transfer learning, the CNN pre-trained on the ImageNet dataset was additionally fine-tuned with breast mass US images to differentiate malignant and benign lesions. Two prediction tasks were investigated. First, a L1 regularized logistic regression prediction model was developed based on generic neural features extracted from US images collected before the chemotherapy (a priori prediction). Second, Siamese CNNs were used to quantify differences between US images collected before the treatment and after the first and second course of NAC. The proposed methods were evaluated using US data collected from 39 tumors. The better performing deep learning models achieved areas under the receiver operating characteristic curve of 0.797 and 0.847 in the case of the a priori prediction and the Siamese model, respectively. The proposed approach was compared with a
method based on handcrafted morphological features. Our study presents the feasibility of using transfer learning with CNNs for the NAC response prediction in US imaging.

Keywords:
breast cancer, deep learning, neoadjuvant chemotherapy, Siamese convolutional neural networks, ultrasound imaging

(140p.)
4.Zaszczyńska A., Sajkiewicz P.Ł., Gradys A., Tymkiewicz R., Urbanek O., Kołbuk D., Influence of process-material conditions on the structure and biological properties of electrospun polyvinylidene fluoride fibers, BULLETIN OF THE POLISH ACADEMY OF SCIENCES: TECHNICAL SCIENCES, ISSN: 0239-7528, DOI: 10.24425/bpasts.2020.133368, Vol.68, No.3, pp.627-633, 2020
Zaszczyńska A., Sajkiewicz P.Ł., Gradys A., Tymkiewicz R., Urbanek O., Kołbuk D., Influence of process-material conditions on the structure and biological properties of electrospun polyvinylidene fluoride fibers, BULLETIN OF THE POLISH ACADEMY OF SCIENCES: TECHNICAL SCIENCES, ISSN: 0239-7528, DOI: 10.24425/bpasts.2020.133368, Vol.68, No.3, pp.627-633, 2020

Abstract:
Polyvinylidene fluoride (PVDF) is one of the most important piezoelectric polymers. Piezoelectricity in PVDF appears in polar β and ɣ phases. Piezoelectric fibers obtained by means of electrospinning may be used in tissue engineering (TE) as a smart analogue of the natural extracellular matrix (ECM). We present results showing the effect of rotational speed of the collecting drum on morphology, phase content and in vitro biological properties of PVDF nonwovens. Morphology and phase composition were analyzed using scanning electron microscopy (SEM) and Fourier-transform infrared spectroscopy (FTIR), respectively. It was shown that increasing rotational speed of the collector leads to an increase in fiber orientation, reduction in fiber diameter and considerable increase of polar phase content, both b and g. In vitro cell culture experiments, carried out with the use of ultrasounds in order to generate electrical potential via piezoelectricity, indicate a positive effect of polar phases on fibroblasts. Our preliminary results demonstrate that piezoelectric PVDF scaffolds are promising materials for tissue engineering applications, particularly for neural tissue regeneration, where the electric potential is crucial.

Keywords:
scaffolds, electrospinning, polyvinylidene fluoride, tissue engineering

(100p.)
5.Jarosik P., Klimonda Z., Lewandowski M., Byra M., Breast lesion classification based on ultrasonic radio-frequency signals using convolutional neural networks, Biocybernetics and Biomedical Engineering, ISSN: 0208-5216, DOI: 10.1016/j.bbe.2020.04.002, Vol.40, No.3, pp.977-986, 2020
Jarosik P., Klimonda Z., Lewandowski M., Byra M., Breast lesion classification based on ultrasonic radio-frequency signals using convolutional neural networks, Biocybernetics and Biomedical Engineering, ISSN: 0208-5216, DOI: 10.1016/j.bbe.2020.04.002, Vol.40, No.3, pp.977-986, 2020

Abstract:
We propose a novel approach to breast mass classification based on deep learning models that utilize raw radio-frequency (RF) ultrasound (US) signals. US images, typically displayed by US scanners and used to develop computer-aided diagnosis systems, are reconstructed using raw RF data. However, information related to physical properties of tissues present in RF signals is partially lost due to the irreversible compression necessary to make raw data readable to the human eye. To utilize the information present in raw US data, we develop deep learning models that can automatically process small 2D patches of RF signals and their amplitude samples. We compare our approach with classification method based on the Nakagami parameter, a widely used quantitative US technique utilizing RF data amplitude samples. Our better performing deep learning model, trained using RF signals and their envelope samples, achieved good classification performance, with the area under the receiver attaining operating characteristic curve (AUC) and balanced accuracy of 0.772 and 0.710, respectively. The proposed method significantly outperformed the Nakagami parameter-based classifier, which achieved AUC and accuracy of 0.64 and 0.611, respectively. The developed deep learning models were used to generate parametric maps illustrating the level of mass malignancy. Our study presents the feasibility of using RF data for the development of deep learning breast mass classification models.

Keywords:
breast lesion classification, convolutional neural networks, deep learning, radio-frequency signals, ultrasound imaging

(100p.)