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Background: Digital breast tomosynthesis (DBT) has been widely adopted as a supplemental imaging modality for diagnostic evaluation of breast cancer and confirmation studies. In this study, a deep learning-based method for characterizing breast tissue patterns in DBT data is presented., Methods: A set of 5388 2D image patches was produced from 230 right mediolateral oblique, 259 left mediolateral oblique, 18 right craniocaudal, and 15 left craniocaudal single-breast DBT studies, using slice-wise annotations of abnormalities and normal tissue. We implemented a patch classifier to predict samples according to two differing scenarios and train it using the patch dataset. First, tissue samples were classified into the following classes: malignant, benign, and normal breast tissue. Second, tissue samples were classified into the following classes: malignant mass, benign mass, benign architectural distortion, malignant architectural distortion, and normal breast tissue. We employed transfer learning and initialized the model base layers with existing pre-trained weights obtained from Globally-Aware Multiple Instance Classifier., Results: High class-wise recall values of 0.8906, 0.8541 and 0.7345 and specificities 0.9558, 0.9575 and 0.8830 were obtained for normal, benign, and malignant classification, respectively. More intricate classification yielded class-wise recall values of 0.8708, 0.8299, 0.9444 and 0.5723 and specificities 0.9406, 0.9833, 0.8943 and 0.9652 for benign mass, normal, malignant architectural distortion, and malignant mass, respectively. However, benign architectural distortion was confused with benign mass and malignant architectural distortion., Conclusions: Combining the proposed phenotype classifier with the commonly used malignant-benign-normal classification enables a more detailed assessment of digital breast tomosynthesis images., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy coupled with machine learning-based partial least squares discriminant analysis (PLS-DA) was applied to study if severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) could be detected from nasopharyngeal swab samples originally collected for polymerase chain reaction (PCR) analysis. Our retrospective study included 558 positive and 558 negative samples collected from Northern Finland. Overall, we found moderate diagnostic performance for ATR-FTIR when PCR analysis was used as the gold standard: the average area under the receiver operating characteristics curve (AUROC) was 0.67-0.68 (min. 0.65, max. 0.69) with 20, 10 and 5 k-fold cross validations. Mean accuracy, sensitivity and specificity was 0.62-0.63 (min. 0.60, max. 0.65), 0.61 (min. 0.58, max. 0.65) and 0.64 (min. 0.59, max. 0.67) with 20, 10 and 5 k-fold cross validations. As a conclusion, our study with relatively large sample set clearly indicate that measured ATR-FTIR spectrum contains specific information for SARS-CoV-2 infection (P < 0.001 for AUROC in label permutation test). However, the diagnostic performance of ATR-FTIR remained only moderate, potentially due to low concentration of viral particles in the transport medium. Further studies are needed before ATR-FTIR can be recommended for fast screening of SARS-CoV-2 from nasopharyngeal swab samples., (© 2022. The Author(s).)

In interior computed tomography (CT), the x-ray beam is collimated to a limited field-of-view (FOV) (e.g. the volume of the heart) to decrease exposure to adjacent organs, but the resulting image has a severe truncation artifact when reconstructed with traditional filtered back-projection (FBP) type algorithms. In some examinations, such as cardiac or dentomaxillofacial imaging, interior CT could be used to achieve further dose reductions. In this work, we describe a deep learning (DL) method to obtain artifact-free images from interior CT angiography. Our method employs the Pix2Pix generative adversarial network (GAN) in a two-stage process: (1) An extended sinogram is computed from a truncated sinogram with one GAN model, and (2) the FBP reconstruction obtained from that extended sinogram is used as an input to another GAN model that improves the quality of the interior reconstruction. Our double GAN (DGAN) model was trained with 10 000 truncated sinograms simulated from real computed tomography angiography slice images. Truncated sinograms (input) were used with original slice images (target) in training to yield an improved reconstruction (output). DGAN performance was compared with the adaptive de-truncation method, total variation regularization, and two reference DL methods: FBPConvNet, and U-Net-based sinogram extension (ES-UNet). Our DGAN method and ES-UNet yielded the best root-mean-squared error (RMSE) (0.03 ± 0.01), and structural similarity index (SSIM) (0.92 ± 0.02) values, and reference DL methods also yielded good results. Furthermore, we performed an extended FOV analysis by increasing the reconstruction area by 10% and 20%. In both cases, the DGAN approach yielded best results at RMSE (0.03 ± 0.01 and 0.04 ± 0.01 for the 10% and 20% cases, respectively), peak signal-to-noise ratio (PSNR) (30.5 ± 2.6 dB and 28.6 ± 2.6 dB), and SSIM (0.90 ± 0.02 and 0.87 ± 0.02). In conclusion, our method was able to not only reconstruct the interior region with improved image quality, but also extend the reconstructed FOV by 20%., (Creative Commons Attribution license.)