Novel concept for a broadband co-propagative stationary Fourier transform spectrometer integrated on a Si3N4 waveguide platform

We present a novel concept for a stationary Fourier transform spectrometer integrated on a Si3N4 waveguide platform. This spectrometer can reach high resolution (similar to 1 nm) over a broad spectral band (similar to 100 nm) within an area of 0.1 mm(2)

Spectroscopic sensing and applications in Silicon Photonics

We report on miniaturized spectroscopic sensors that are realized using Silicon Photonics technology. This technology relies on CMOS compatible processes to fabricate both Silicon and Silicon-Nitride based photonics integrated circuits. Various spectroscopic sensor designs and applications are discussed

CMOS-compatible silicon nitride spectrometers for lab-on-a-chip spectral sensing

We report on miniaturized optical spectrometers integrated on a photonic integrated circuit (PIC) platform based on silicon nitride waveguides and fabricated in a CMOS-compatible approach. As compared to a silicon on -insulator PIC-platform, the usage of silicon nitride allows for operation in the visible and near infrared. Furthermore, the moderately high refractive index contrast in silicon -nitride photonic wire waveguides provides a valuable compromise between compactness, optical loss and sensitivity to phase error. Three generic types of on -chip spectrometers are discussed: the arrayed waveguide grating (AWG) spectrometer, the echelle grating or planar concave grating (PCG) spectrometer and the stationary Fourier transform spectrometer (FTS) spectrometer. Both the design as well as experimental results are presented and discussed. For the FTS spectrometer a specific design is described in detail leading to an ultra -small (0.1 mm2) footprint device with a resolution of 1 nm and a spectral range of 100nm. Examples are given of the usage of these spectrometers in refractive index biosensing, absorption spectroscopy and Raman spectroscopy

Diagnosis of architectural distortion on digital breast tomosynthesis using radiomics and deep learning.

PURPOSE: To implement two Artificial Intelligence (AI) methods, radiomics and deep learning, to build diagnostic models for patients presenting with architectural distortion on Digital Breast Tomosynthesis (DBT) images. MATERIALS AND METHODS: A total of 298 patients were identified from a retrospective review, and all of them had confirmed pathological diagnoses, 175 malignant and 123 benign. The BI-RADS scores of DBT were obtained from the radiology reports, classified into 2, 3, 4A, 4B, 4C, and 5. The architectural distortion areas on craniocaudal (CC) and mediolateral oblique (MLO) views were manually outlined as the region of interest (ROI) for the radiomics analysis. Features were extracted using PyRadiomics, and then the support vector machine (SVM) was applied to select important features and build the classification model. Deep learning was performed using the ResNet50 algorithm, with the binary output of malignancy and benignity. The Gradient-weighted Class Activation Mapping (Grad-CAM) method was utilized to localize the suspicious areas. The predicted malignancy probability was used to construct the ROC curves, compared by the DeLong test. The binary diagnosis was made using the threshold of ≥ 0.5 as malignant. RESULTS: The majority of malignant lesions had BI-RADS scores of 4B, 4C, and 5 (148/175 = 84.6%). In the benign group, a substantial number of patients also had high BI-RADS ≥ 4B (56/123 = 45.5%), and the majority had BI-RADS ≥ 4A (102/123 = 82.9%). The radiomics model built using the combined CC+MLO features yielded an area under curve (AUC) of 0.82, the sensitivity of 0.78, specificity of 0.68, and accuracy of 0.74. If only features from CC were used, the AUC was 0.77, and if only features from MLO were used, the AUC was 0.72. The deep-learning model yielded an AUC of 0.61, significantly lower than all radiomics models (p<0.01), which was presumably due to the use of the entire image as input. The Grad-CAM could localize the architectural distortion areas. CONCLUSION: The radiomics model can achieve a satisfactory diagnostic accuracy, and the high specificity in the benign group can be used to avoid unnecessary biopsies. Deep learning can be used to localize the architectural distortion areas, which may provide an automatic method for ROI delineation to facilitate the development of a fully-automatic computer-aided diagnosis system using combined AI strategies

1 D Hierarchical MnCo2O4 Nanowire@MnO2 Sheet Core–Shell Arrays on Graphite Paper as Superior Electrodes for Asymmetric Supercapacitors

Heterostructured metal oxide core–shell architectures have attracted considerable attention owing to their superior electrochemical performance in supercapacitors compared to a single structure. Here, we report a simple and effective synthesis of hierarchical MnCo2O4 nanowire@MnO2 sheet core–shell nanostructures anchored on graphite paper for use in supercapacitors. The proposed electrode exhibits a specific capacitance of 2262 F g−1 at 1 A g−1. In addition, good rate capability and excellent cycling performance are observed. An asymmetric supercapacitor with operating potential at 1.6 V is demonstrated using MnCo2O4@MnO2 as cathode and graphene/nickel foam (NF) as anode. The MnCo2O4@MnO2//graphene/NF asymmetric device shows a high energy density of 85.7 Wh kg−1 at a power density of 800 W kg−1 while maintaining a high energy density of 34.7 Wh kg−1 at 24 kW kg−1. Moreover, the device demonstrates a long-term cycling stability of 81.6 % retention of its initial specific capacitance

Diagnosis of architectural distortion on digital breast tomosynthesis using radiomics and deep learning

PurposeTo implement two Artificial Intelligence (AI) methods, radiomics and deep learning, to build diagnostic models for patients presenting with architectural distortion on Digital Breast Tomosynthesis (DBT) images.Materials and MethodsA total of 298 patients were identified from a retrospective review, and all of them had confirmed pathological diagnoses, 175 malignant and 123 benign. The BI-RADS scores of DBT were obtained from the radiology reports, classified into 2, 3, 4A, 4B, 4C, and 5. The architectural distortion areas on craniocaudal (CC) and mediolateral oblique (MLO) views were manually outlined as the region of interest (ROI) for the radiomics analysis. Features were extracted using PyRadiomics, and then the support vector machine (SVM) was applied to select important features and build the classification model. Deep learning was performed using the ResNet50 algorithm, with the binary output of malignancy and benignity. The Gradient-weighted Class Activation Mapping (Grad-CAM) method was utilized to localize the suspicious areas. The predicted malignancy probability was used to construct the ROC curves, compared by the DeLong test. The binary diagnosis was made using the threshold of ≥ 0.5 as malignant.ResultsThe majority of malignant lesions had BI-RADS scores of 4B, 4C, and 5 (148/175 = 84.6%). In the benign group, a substantial number of patients also had high BI-RADS ≥ 4B (56/123 = 45.5%), and the majority had BI-RADS ≥ 4A (102/123 = 82.9%). The radiomics model built using the combined CC+MLO features yielded an area under curve (AUC) of 0.82, the sensitivity of 0.78, specificity of 0.68, and accuracy of 0.74. If only features from CC were used, the AUC was 0.77, and if only features from MLO were used, the AUC was 0.72. The deep-learning model yielded an AUC of 0.61, significantly lower than all radiomics models (p<0.01), which was presumably due to the use of the entire image as input. The Grad-CAM could localize the architectural distortion areas.ConclusionThe radiomics model can achieve a satisfactory diagnostic accuracy, and the high specificity in the benign group can be used to avoid unnecessary biopsies. Deep learning can be used to localize the architectural distortion areas, which may provide an automatic method for ROI delineation to facilitate the development of a fully-automatic computer-aided diagnosis system using combined AI strategies