cuFE: High Performance Privacy Preserving Support Vector Machine with Inner-Product Functional Encryption

Privacy preservation is a sensitive issue in our modern society. It is becoming increasingly important in many applications in this ever-growing and highly connected digital era. Functional encryption is a computation on encrypted data paradigm that allows users to retrieve the evaluation of a function on encrypted data without revealing the data, thus effectively protecting users\u27 privacy. However, existing functional encryption implementations are still very time-consuming for practical deployment, especially when applied to machine learning applications that involve a huge amount of data. In this paper, we present a high-performance implementation of inner-product functional encryption (IPFE) based on ring-learning with errors on graphics processing units. We propose novel techniques to parallelize the Gaussian sampling, which is one of the most time-consuming operations in the IPFE scheme. We further execute a systematic investigation to select the best strategy for implementing number theoretic transform and inverse number theoretic transform for different security levels. Compared to the existing AVX2 implementation of IPFE, our implementation on a RTX 2060 GPU device can achieve 34.24x, 40.02x, 156.30x, and 18.76x speed-up for Setup, Encrypt, KeyGen, and Decrypt respectively. Finally, we propose a fast privacy-preserving Support Vector Machine (SVM) application to classify data securely using our GPU-accelerated IPFE scheme. Experimental results show that our implementation can classify 100 inputs with 591 support vectors in 688 ms (less than a second), which is 33.12x faster than the AVX2 version which takes 23 seconds

Case report: Investigation of genetic mutations in a case of schistosomus reflexus in a Holstein dairy cattle fetus in Korea

Schistosomus reflexus (SR) is one of the most common congenital anomalies found in cases of cattle dystocia; this disorder occurs mostly in cattle. Congenital anomalies such as SR are caused by various genetic and environmental factors, but no specific cause has been elucidated for SR. This study reports a case of SR in a Holstein dairy cattle fetus with congenital anomalies in Korea. Grossly, a distinct spine curvature was observed between the thoracic and lumbar vertebrae, accompanied by a consequential malformation from the sacrum to the occipital bone. Furthermore, the thoracic and abdominal organs were exposed. In computed tomography (CT) images, mild and severe kyphoscoliosis was observed in T1~11 and L1~6, respectively. Additionally, vertebral dysplasia was observed in S1~5 and Cd 1~5. To pinpoint the causal genes and mutations, we leveraged a custom 50K Hanwoo SNP-Chip and the Online Mendelian Inheritance in Animals (OMIA) database. As a result, we identified a nonsense mutation in apoptotic protease activating factor 1 (APAF1) within HH1 that was associated with a decrease in conception rate and an increase in abortion in Holstein dairy cattle. The genotype of the SR case was A/A, and most of the 1,142 normal Holstein dairy cattle tested as a control group had the genotype G/G. In addition, the A/A genotype did not exist in the control group. Based on the pathological, genetic, and radiological findings, the congenital abnormalities observed were diagnosed as SR

Retrospective quantification of clinical abdominal DCE-MRI using pharmacokinetics-informed deep learning: a proof-of-concept study

IntroductionDynamic contrast-enhanced (DCE) MRI has important clinical value for early detection, accurate staging, and therapeutic monitoring of cancers. However, conventional multi-phasic abdominal DCE-MRI has limited temporal resolution and provides qualitative or semi-quantitative assessments of tissue vascularity. In this study, the feasibility of retrospectively quantifying multi-phasic abdominal DCE-MRI by using pharmacokinetics-informed deep learning to improve temporal resolution was investigated.MethodForty-five subjects consisting of healthy controls, pancreatic ductal adenocarcinoma (PDAC), and chronic pancreatitis (CP) were imaged with a 2-s temporal-resolution quantitative DCE sequence, from which 30-s temporal-resolution multi-phasic DCE-MRI was synthesized based on clinical protocol. A pharmacokinetics-informed neural network was trained to improve the temporal resolution of the multi-phasic DCE before the quantification of pharmacokinetic parameters. Through ten-fold cross-validation, the agreement between pharmacokinetic parameters estimated from synthesized multi-phasic DCE after deep learning inference was assessed against reference parameters from the corresponding quantitative DCE-MRI images. The ability of the deep learning estimated parameters to differentiate abnormal from normal tissues was assessed as well.ResultsThe pharmacokinetic parameters estimated after deep learning have a high level of agreement with the reference values. In the cross-validation, all three pharmacokinetic parameters (transfer constant Ktrans, fractional extravascular extracellular volume ve, and rate constant kep) achieved intraclass correlation coefficient and R2 between 0.84–0.94, and low coefficients of variation (10.1%, 12.3%, and 5.6%, respectively) relative to the reference values. Significant differences were found between healthy pancreas, PDAC tumor and non-tumor, and CP pancreas.DiscussionRetrospective quantification (RoQ) of clinical multi-phasic DCE-MRI is possible by deep learning. This technique has the potential to derive quantitative pharmacokinetic parameters from clinical multi-phasic DCE data for a more objective and precise assessment of cancer

Free-breathing liver fat quantification using a multiecho 3D stack-of-radial technique:Free-Breathing Radial Liver Fat Quantification

PurposeThe diagnostic gold standard for nonalcoholic fatty liver disease is an invasive biopsy. Noninvasive Cartesian MRI fat quantification remains limited to a breath-hold (BH). In this work, a novel free-breathing 3D stack-of-radial (FB radial) liver fat quantification technique is developed and evaluated in a preliminary study.MethodsPhantoms and healthy subjects (n = 11) were imaged at 3 Tesla. The proton-density fat fraction (PDFF) determined using FB radial (with and without scan acceleration) was compared to BH single-voxel MR spectroscopy (SVS) and BH 3D Cartesian MRI using linear regression (correlation coefficient ρ and concordance coefficient ρc ) and Bland-Altman analysis.ResultsIn phantoms, PDFF showed significant correlation (ρ > 0.998, ρc  > 0.995) and absolute mean differences < 2.2% between FB radial and BH SVS, as well as significant correlation (ρ > 0.999, ρc  > 0.998) and absolute mean differences < 0.6% between FB radial and BH Cartesian. In the liver and abdomen, PDFF showed significant correlation (ρ > 0.986, ρc  > 0.985) and absolute mean differences < 1% between FB radial and BH SVS, as well as significant correlation (ρ > 0.996, ρc  > 0.995) and absolute mean differences < 0.9% between FB radial and BH Cartesian.ConclusionAccurate 3D liver fat quantification can be performed in 1 to 2 min using a novel FB radial technique. Magn Reson Med 79:370-382, 2018. © 2017 International Society for Magnetic Resonance in Medicine

Non-Zero Grid for Accurate 2-Bit Additive Power-of-Two CNN Quantization

Quantization is an effective technique to reduce the memory and computational complexity of CNNs. Recent advances utilize additive powers-of-two to perform non-uniform quantization, which resembles a normal distribution and shows better performance than uniform quantization. With powers-of-two quantization, the computational complexity is also largely reduced because the slow multiplication operations are replaced with lightweight shift operations. However, there are serious problems in the previously proposed grid formulation for 2-bit quantization. In particular, these powers-of-two schemes produce zero values, generating significant training error and causing low accuracy. In addition, due to improper grid formulation, they also fallback to uniform quantization when the quantization level reaches 2-bit. Due to these reasons, on large CNN like ResNet-110, these powers-of-two schemes may not even train properly. To resolve these issues, we propose a new non-zero grid formulation that enables 2-bit non-uniform quantization and allow the CNN to be trained successfully in every attempt, even for a large network. The proposed technique quantizes weight as power-of-two values and projects it close to the mean area through a simple constant product on the exponential part. This allows our quantization scheme to closely resemble a non-uniform quantization at 2-bit, enabling successful training at 2-bit quantization, which is not found in the previous work. The proposed technique achieves 70.57% accuracy on the CIFAR-100 dataset trained with ResNet-110. This result is 6.24% higher than the additive powers-of-two scheme which only achieves 64.33% accuracy. Beside achieving higher accuracy, our work also maintains the same memory and computational efficiency with the original additive powers-of-two scheme

Cardiac balanced steady-state free precession MRI at 0.35 T: a comparison study with 1.5 T

BackgroundWhile low-field MRI is disadvantaged by a reduced signal-to-noise ratio (SNR) compared to higher fields, it has a number of useful features such as decreased SAR and shorter T1, and has shown promise for diagnostic imaging. This study demonstrates the feasibility of cardiac balanced steady-state free precession (bSSFP) MRI at 0.35 T and compares cardiac bSSFP MRI images at 0.35 T with those at 1.5 T.MethodsCardiac images were acquired in 7 healthy volunteers using an ECG-gated bSSFP cine sequence on a 0.35 T superconducting MR system as well as a clinical 1.5 T system. Blood and myocardium SNR and contrast-to-noise ratio (CNR) were computed. Subjective image scoring was used to compare the image quality between 0.35 and 1.5 T.ResultsCardiac images at 0.35 T were successfully acquired in all volunteers. While the 0.35 T images were noisier than those at 1.5 T, blood, myocardium and papillary muscles could be clearly delineated. At 0.35 T, bSSFP images were acquired at flip angles as high as 150°. Maximum CNR was achieved at 130°. Image quality scoring showed that while at lower flip angles, the 0.35 T images had poorer quality than the 1.5 T, but with flip angles of 110 and 130, the image quality at 0.35 T had scores similar to those at 1.5 T.ConclusionsThis study demonstrates that cardiac bSSFP imaging is highly feasible at 0.35 T

Free-breathing liver fat quantification using a multiecho 3D stack-of-radial technique.

PurposeThe diagnostic gold standard for nonalcoholic fatty liver disease is an invasive biopsy. Noninvasive Cartesian MRI fat quantification remains limited to a breath-hold (BH). In this work, a novel free-breathing 3D stack-of-radial (FB radial) liver fat quantification technique is developed and evaluated in a preliminary study.MethodsPhantoms and healthy subjects (n = 11) were imaged at 3 Tesla. The proton-density fat fraction (PDFF) determined using FB radial (with and without scan acceleration) was compared to BH single-voxel MR spectroscopy (SVS) and BH 3D Cartesian MRI using linear regression (correlation coefficient ρ and concordance coefficient ρc ) and Bland-Altman analysis.ResultsIn phantoms, PDFF showed significant correlation (ρ > 0.998, ρc  > 0.995) and absolute mean differences < 2.2% between FB radial and BH SVS, as well as significant correlation (ρ > 0.999, ρc  > 0.998) and absolute mean differences < 0.6% between FB radial and BH Cartesian. In the liver and abdomen, PDFF showed significant correlation (ρ > 0.986, ρc  > 0.985) and absolute mean differences < 1% between FB radial and BH SVS, as well as significant correlation (ρ > 0.996, ρc  > 0.995) and absolute mean differences < 0.9% between FB radial and BH Cartesian.ConclusionAccurate 3D liver fat quantification can be performed in 1 to 2 min using a novel FB radial technique. Magn Reson Med 79:370-382, 2018. © 2017 International Society for Magnetic Resonance in Medicine