DeepWSD: Projecting Degradations in Perceptual Space to Wasserstein Distance in Deep Feature Space

Existing deep learning-based full-reference IQA (FR-IQA) models usually predict the image quality in a deterministic way by explicitly comparing the features, gauging how severely distorted an image is by how far the corresponding feature lies from the space of the reference images. Herein, we look at this problem from a different viewpoint and propose to model the quality degradation in perceptual space from a statistical distribution perspective. As such, the quality is measured based upon the Wasserstein distance in the deep feature domain. More specifically, the 1DWasserstein distance at each stage of the pre-trained VGG network is measured, based on which the final quality score is performed. The deep Wasserstein distance (DeepWSD) performed on features from neural networks enjoys better interpretability of the quality contamination caused by various types of distortions and presents an advanced quality prediction capability. Extensive experiments and theoretical analysis show the superiority of the proposed DeepWSD in terms of both quality prediction and optimization.Comment: ACM Multimedia 2022 accepted thesi

Image quality and evaluation ability of magnetic resonance imaging techniques for thyroid-associated ophthalmopathy: Dixon fat-suppression technique vs. spectral attenuated inversion recovery

PurposeWe aimed to compare two magnetic resonance imaging (MRI) techniques, Dixon and spectral attenuated inversion recovery (SPAIR) fat-suppression, in terms of image quality and suitability for evaluating thyroid-associated ophthalmopathy (TAO) lesion characteristics.MethodsThis cross-sectional, retrospective study involved 70 patients with TAO (140 eyes) who underwent orbital coronal MRI examinations, including Dixon-transverse relaxation (T2)-weighted imaging (T2WI) and SPAIR-T2WI, between 2020 and 2022. We compared the fat-suppression quality and artifacts, noise (N), signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), signal intensity ratio (SIR) of extraocular muscles (SIR-EOM) and lacrimal glands (SIR-LG), and TAO activity evaluation efficiency.ResultsDixon-T2WI showed a higher frequency of better subjective image quality and suitability for evaluating the characteristics of TAO lesions (65.7% vs. 14.3%) than SPAIR-T2WI. Fat-suppression quality and artifact scores were lower for Dixon-T2WI than for SPAIR-T2WI (p < 0.001). The N, SNR, and CNR values, EOM-SIR, and LG-SIR were higher for orbital coronal Dixon-T2WI than for SPAIR-T2WI (all p < 0.001). Clinical activity scores (CASs) showed positive correlations with SIR. The correlation between EOM-SIR and LG-SIR of orbital coronal Dixon-T2WI with CAS was higher than that of SPAIR-T2WI (0.590 vs. 0.493, all p < 0.001; 0.340 vs. 0.295, all p < 0.01). EOM-SIR and LG-SIR of Dixon-T2WI yielded a higher area under the curve than SPAIR-T2WI for evaluating TAO activity (0.865 vs. 0.760, p < 0.001; 0.695 vs. 0.617, p = 0.017).ConclusionDixon-T2WI yields higher image quality than SPAIR-T2WI. Furthermore, it has a stronger ability to evaluate TAO inflammation than SPAIR, with higher sensitivity and specificity in active TAO staging

MEI Kodierung der frühesten Notation in linienlosen Neumen

Das Optical Neume Recognition Project (ONRP) hat die digitale Kodierung von musikalischen Notationszeichen aus dem Jahr um 1000 zum Ziel – ein ambitioniertes Vorhaben, das die Projektmitglieder veranlasste, verschiedenste methodische Ansätze zu evaluieren. Die Optical Music Recognition-Software soll eine linienlose Notation aus einem der ältesten erhaltenen Quellen mit Notationszeichen, dem Antiphonar Hartker aus der Benediktinerabtei St. Gallen (Schweiz), welches heute in zwei Bänden in der Stiftsbibliothek in St. Gallen aufbewahrt wird, erfassen. Aufgrund der handgeschriebenen, linienlosen Notation stellt dieser Gregorianische Gesang den Forscher vor viele Herausforderungen. Das Werk umfasst über 300 verschiedene Neumenzeichen und ihre Notation, die mit Hilfe der Music Encoding Initiative (MEI) erfasst und beschrieben werden sollen. Der folgende Artikel beschreibt den Prozess der Adaptierung, um die MEI auf die Notation von Neumen ohne Notenlinien anzuwenden. Beschrieben werden Eigenschaften der Neumennotation, um zu verdeutlichen, wo die Herausforderungen dieser Arbeit liegen sowie die Funktionsweise des Classifiers, einer Art digitalen Neumenwörterbuchs

Construction of an immunogenic cell death-based risk score prognosis model in breast cancer

Immunogenic cell death (ICD) is a form of regulated cell death that elicits immune response. Common inducers of ICD include cancer chemotherapy and radiation therapy. A better understanding of ICD might contribute to modify the current regimens of anti-cancer therapy, especially immunotherapy. This study aimed to identify ICD-related prognostic gene signatures in breast cancer (BC). An ICD-based gene prognostic signature was developed using Lasso-cox regression and Kaplan-Meier survival analysis based on datasets acquired from the Cancer Genome Atlas and Gene Expression Omnibus. A nomogram model was developed to predict the prognosis of BC patients. Gene Set Enrichment Analysis (GESA) and Gene Set Variation Analysis (GSVA) were used to explore the differentially expressed signaling pathways in high and low-risk groups. CIBERSORT and ESTIMATE algorithms were performed to investigate the difference of immune status in tumor microenvironment of different risk groups. Six genes (CALR, CLEC9A, BAX, TLR4, CXCR3, and PIK3CA) were selected for construction and validation of the prognosis model of BC based on public data. GSEA and GSVA analysis found that immune-related gene sets were enriched in low-risk group. Moreover, immune cell infiltration analysis showed that the immune features of the high-risk group were characterized by higher infiltration of tumor-associated macrophages and a lower proportion of CD8+ T cells, suggesting an immune evasive tumor microenvironment. We constructed and validated an ICD-based gene signature for predicting prognosis of breast cancer patients. Our model provides a tool with good discrimination and calibration abilities to predict the prognosis of BC, especially triple-negative breast cancer (TNBC)

A COVID-19 Risk Score Combining Chest CT Radiomics and Clinical Characteristics to Differentiate COVID-19 Pneumonia From Other Viral Pneumonias

With the continued transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) throughout the world, identification of highly suspected COVID-19 patients remains an urgent priority. In this study, we developed and validated COVID-19 risk scores to identify patients with COVID-19. In this study, for patient-wise analysis, three signatures, including the risk score using radiomic features only, the risk score using clinical factors only, and the risk score combining radiomic features and clinical variables, show an excellent performance in differentiating COVID-19 from other viral-induced pneumonias in the validation set. For lesion-wise analysis, the risk score using three radiomic features only also achieved an excellent AUC value. In contrast, the performance of 130 radiologists based on the chest CT images alone without the clinical characteristics included was moderate as compared to the risk scores developed. The risk scores depicting the correlation of CT radiomics and clinical factors with COVID-19 could be used to accurately identify patients with COVID-19, which would have clinically translatable diagnostic and therapeutic implications from a precision medicine perspective