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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Ravishankar, Hariharan Paluru, Naveen Sudhakar, Prasad Yalavarthy, Phaneendra K. Indian Institute of Science Department of Computational and Data Sciences Bangalore India Ge HealthCare Science and Technology Organization Bangalore India

ISBN: (纸本)9798331520526

This work addresses the problem of deriving improved quantitative susceptibility mapping (QSM) from magnetic resonance (MR) acquisitions. Deep learning based models that map measured MR local phase field to QSM maps, have recently demonstrated satisfactory reconstruction quality. However, they suffer from poor generalization on acquisitions that deviate from training settings. To address this, this work proposes a patient-specific, test-time adaptation method for modifying pre-trained deep learning models, conditioned on individual subject data. The proposed method of uncertainty-aware test-time adaptation (UATTA-QSM), for the first-time introduces, lipschitz constraints on local phase field measurements and QSM reconstructions to jointly reduce uncertainty and adapt model weights during inference time with functional regularization. This framework was evaluated on multiple adaptation scenarios like changing field strength, limited data training and different architectures. On unseen datasets, the proposed method of UATTA-QSM consistently demonstrates performance improvement on all metrics of interest. © 2025 IEEE.

关键词： Inverse Problems Quantitative Susceptibility Mapping Test Time Adaptation Uncertainty Modeling

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Automated Quality Assessment Using Appearance-Based Simulations and Hippocampus Segmentation on Low-Field Paediatric Brain MR Images 1st

Automated Quality Assessment Using Appearance-Based Simulati...

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1st MICCAI Challenge on Low Field Pediatric Brain Magnetic Resonance Image Segmentation and Quality Assurance, LISA 2024, held in Conjunction with Medical Image Computing and Computer Assisted Intervention Conference, MICCAI 2024

作者： Sundaresan, Vaanathi Dinsdale, Nicola K Department of Computational and Data Sciences Indian Institute of Science Bangalore560012 India Oxford Machine Learning in NeuroImaging Lab Department of Computer Science University of Oxford Oxford United Kingdom

ISBN: (纸本)9783031830105

Understanding the structural growth of paediatric brains is a key step in the identification of various neuro-developmental disorders. However, our knowledge is limited by many factors, including the lack of automated image analysis tools, especially in Low and Middle Income Countries from the lack of high field MR images available. Low-field systems are being increasingly explored in these countries, and, therefore, there is a need to develop automated image analysis tools for these images. In this work, as a preliminary step, we consider two tasks: 1) automated quality assurance and 2) hippocampal segmentation, where we compare multiple approaches. For the automated quality assurance task a DenseNet combined with appearance-based transformations for synthesising artefacts produced the best performance, with a weighted accuracy of 82.3%, thus ranking in the 1st place in the LISA2024 Challenge. For the segmentation task, registration of an average atlas performed the best, with a final Dice score of 0.61. Our results show that although the images can provide understanding of large scale pathologies and gross scale anatomical development, there still remain barriers for their use for more granular analyses. © The Author(s) 2025.

关键词： Magnetic resonance imaging

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Inference Time Adaptation for Improved Retinal Disease Diagnosis Using Optical Coherence Tomography Images 22

Inference Time Adaptation for Improved Retinal Disease Diagn...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Ravishankar, Hariharan Paluru, Naveen Sudhakar, Prasad Yalavarthy, Phaneendra K. Indian Institute of Science Department of Computational and Data Sciences Bangalore India Science and Technology Organization Ge HealthCare Bangalore India

ISBN: (纸本)9798331520526

Spectral-Domain Optical Coherence Tomography (SDOCT) is the most common and cost-effective variant of OCT for diagnosing retinal diseases. Deep learning models that are utilized for automated diagnosis of these images often exhibit poor performance - across data-sets with varying acquisition settings, limiting their clinical utility. This work, for the first time proposes 'Inference Time Adaptation (ITA)' with lightweight deep learning models for SDOCT analysis, enhancing model deployability and generalizability. Furthermore, this work extends ITA, introducing 'Inference Time Adaptation with Domain Transforms (ITADT)' to improve the generalizability of deep learning models for retinal disease diagnosis. Domain specific augmentations are applied to test samples to enable regularized model adaptation, enhancing classification performance. The proposed approach is model agnostic and can be applied to any trained deep learning model. The proposed methods achieve significant performance improvement of > 10% on average in accuracy, compared with the un-adapted/original deep learning model on benchmark data-sets for classification of retinal diseases. © 2025 IEEE.

关键词： Deep Learning Generalizability Inference Time Adaptation Optical Coherence Tomography Retinal Diseases

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Display Field-of-View Agnostic Robust Ct Kernel Synthesis Using Model-Based Deep Learning 22

Display Field-of-View Agnostic Robust Ct Kernel Synthesis Us...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Aggarwal, Hemant Kumar Jerald, Antony Yalavarthy, Phaneendra K. Langoju, Rajesh Das, Bipul Science and Technology Organization Ge HealthCare Bangalore India Indian Institute of Science Department of Computational and Data Sciences Bangalore India

ISBN: (纸本)9798331520526

In X-ray computed tomography (CT) imaging, the choice of reconstruction kernel is crucial as it significantly impacts the quality of clinical images. Different kernels influence spatial resolution, image noise, and contrast in various ways. Clinical applications involving lung imaging often require images reconstructed with both soft and sharp kernels. The reconstruction of images with different kernels require raw sinogram data and storing images for all kernels increases processing time and storage requirements. The Display Field-of-View (DFOV) adds complexity to kernel synthesis, as data acquired at different DFOVs exhibit varying levels of sharpness and details. This work introduces an efficient, DFOV-agnostic solution for image-based kernel synthesis using model-based deep learning. The proposed method explicitly integrates CT kernel and DFOV characteristics into the forward model. Experimental results on clinical data, along with quantitative analysis of the estimated modulation transfer function using wire phantom data, clearly demonstrate the utility of the proposed method in real time. Additionally, a comparative study with a direct learning network, that lacks forward model information, shows that the proposed method is more robust to DFOV variations. © 2025 IEEE.

关键词： Kernel Synthesis Model-based Deep Learning X-ray Computed Tomography

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An Intelligent Machine Learning Approach to Detect Spam in Social Media 5

An Intelligent Machine Learning Approach to Detect Spam in S...

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5th International Conference on data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Mohandas, R. Prasanna, D.S. John Deva Meenakshi, N. Kaviyaraj, R. Nathiya, S. Arivazhagan, N. Srm Institute of Science and Technology School of Computing Department of Computational Intelligence Kattankulathur India Srm Institute of Science and Technology School of Computing Department of Data Science and Business Systems Kattankulathur India

ISBN: (纸本)9798350389609

Machine learning based intelligent approach is applied to finding spam in YouTube videos. Spam comments are ones that are promotional or unrelated. A growing number of users have been drawn to the idea of making money by placing advertisements on popular social networking platforms over time. These remarks damage a channel's reputation. YouTube has addressed the issue with a limited set of solutions that concentrate on preventing comments from including links. Such heuristics are now futile since spammers have found ways to get around them. Despite the fact that common machine learning classification techniques have demonstrated considerable success, new methods can still increase accuracy. Therefore, this research attempt to identify such statements using machine learning techniques like Random Forest, SVM, and Naive Bayes. In order to convey a clear concept, graphs are used to illustrate data cleaning, data analysis, text preparation, and model building in this research. © 2024 IEEE.

关键词： Adversarial machine learning

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Asynchrony-Tolerant Schemes to Enhance Scalability of High-Order Compressible Flow Solver 31

Asynchrony-Tolerant Schemes to Enhance Scalability of High-O...

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31st IEEE International Conference on High Performance Computing, data, and Analytics Workshops, HiPCW 2024

作者： Aswin Kumar, A. Vadlamani, Nagabhushana Rao Aditya, Konduri Department of Aerospace Engineering Indian Institute of Technology Madras Chennai India Department of Computational and Data Sciences Indian Institute of Science Bengaluru India

ISBN: (纸本)9798331509118

High-order asynchrony-tolerant (AT) schemes are used to solve the compressible Navier-Stokes equations. The AT schemes are validated for accuracy using canonical flow problems and are found to be in excellent agreement with the standard schemes. Strong and weak scaling analyses are performed, and significant speedups (up to 3.5×) are observed for the asynchronous algorithms compared to the standard synchronous algorithm. © 2024 IEEE

关键词： Navier Stokes equations

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Unusual metallic state in superconducting A15-type La4H23

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National science Review 2024年第12期11卷 94-103页

作者： Jianning Guo Dmitrii Semenok Grigoriy Shutov Di Zhou Su Chen Yulong Wang Kexin Zhang Xinyue Wu Sven Luther Toni Helm Xiaoli Huang Tian Cui State Key Laboratory of Superhard Materials College of Physics Jilin University Center for High Pressure Science and Technology Advanced Research (HPSTAR) Department of High-Temperature Superconductivity Skolkovo Institute of Science and Technology Skolkovo Innovation CenterComputational and Data Science and Engineering HochfeldMagnetlabor Dresden(HLD-EMFL) and Würzburg-Dresden Cluster of Excellence Helmholtz-Zentrum Dresden-Rossendorf(HZDR) School of Physical Science and Technology Ningbo University

Hydride superconductors continue to fascinate the communities of condensed matter physics and material scientists because they host the promising near room-temperature superconductivity. Current research has concentrated on the new hydride superconductors with the enhancement of the superconducting transition temperature（Tc）. The multiple extreme conditions（high pressure/temperature and magnetic field） will introduce new insights into hydride superconductors. The study of transport properties under very high magnetic fields facilitates the understanding of superconductivity in conventional hydride *** the present work, we report experimental evidence of an unusual metal state in a newly synthesized cubic A15-type La4H23that exhibits superconductivity with a Tc reaching 105 K at 118 GPa. A large negative magnetoresistance is observed in strong pulsed magnetic fields in the non-superconducting state of this compound below 40 K. Moreover, we construct the full magnetic phase diagram of La4H23up to 68 T at high pressure. The present work reveals anomalous electronic structural properties of A15-La4H23under high magnetic fields, and therefore has great importance with regard to advancing the understanding of quantum transport behaviors in hydride superconductors.

关键词： high-pressure superconductivity hydrides negative magnetoresistance

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Evaluating Multi-Instance DNN Inferencing on Multiple Accelerators of an Edge Device 31

Evaluating Multi-Instance DNN Inferencing on Multiple Accele...

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31st IEEE International Conference on High Performance Computing, data, and Analytics Workshops, HiPCW 2024

作者： Tayal, Mumuksh Simmhan, Yogesh Robert Bosch Center for Cyber Physical Systems Indian Institute of Science Bangalore560012 India Department of Computational and Data Sciences Indian Institute of Science Bangalore560012 India

ISBN: (纸本)9798331509118

Edge devices like Nvidia Jetson have started to have multiple on-board accelerators such as GPU CUDA cores, Tensor Cores and Deep Learning Accelerators (DLA). Maximizing the DNN inferencing performance of such devices requires us to concurrently use these col-located hardware components, but this has not yet been studied. We analyze the performance of accelerators present in Jetson AGX Orin, both independently and concurrently, using multiple instances of ResNet50 model. We assess the effects of using different combinations of the components and varying batch sizes on the inference throughput and latency. Our results indicate that using CUDA Core with Tensor Cores offers a higher throughput, while using them in conjunction with DLAs reduces the benefits. This paves the way to explore more intelligent configurations to maximize the performance of edge platforms for AI workloads. © 2024 IEEE

关键词： Tensors

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Fully-Decoupled and Second-Order Time-Accurate Scheme for the Cahn–Hilliard Ohta–Kawaski Phase-Field Model of Diblock Copolymer Melt Confined in Hele–Shaw Cell

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Communications in Mathematics and Statistics 2024年第3期12卷 479-504页

作者： Junying Cao Jun Zhang Xiaofeng Yang School of Data Science and Information Engineering Guizhou Minzu UniversityGuiyang 550025People’s Republic of China Guizhou Key Laboratory of Big Data Statistics Analysis Computational Mathematics Research CenterGuizhou University of Finance and EconomicsGuiyang 550025People’s Republic of China Department of Mathematics University of South CarolinaColumbiaSCUSA

In this work,we consider numerical approximations of the phase-field model of diblock copolymer melt confined in Hele–Shaw cell,which is a very complicated coupled nonlinear system consisting of the Darcy equations and the Cahn–Hilliard type equations with the Ohta–Kawaski *** the combination of a novel explicit-Invariant Energy Quadratization approach and the projection method,we develop the first full decoupling,energy stable,and second-order time-accurate numerical *** introduction of two auxiliary variables and the design of two auxiliary ODEs play a vital role in obtaining the full decoupling structure while maintaining energy *** scheme is also linear and unconditional energy stable,and the practical implementation efficiency is also very high because it only needs to solve a few elliptic equations with constant coefficients at each time *** strictly prove that the scheme satisfies the unconditional energy stability and give a detailed implementation *** experiments further verify the convergence rate,energy stability,and effectiveness of the developed algorithm.

关键词： Phase-field Explicit-IEQ Darcy Decoupled Energy Stability Diblock copolymer melt

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Transfer Learning and Fine-tuned CNN Architecture for Dog Breed Classification 3

Transfer Learning and Fine-tuned CNN Architecture for Dog Br...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Gunaranjan, K. Vijay, K. Naveen, P. St. Joseph's College of Engineering Department of Artificial Intelligence and Data Science Chennai India SRM institute of science and technology Department of computational intelligence Chennai India

ISBN: (纸本)9798350372120

This study presents a comprehensive approach to dog breed classification, leveraging transfer learning algorithms and Convolutional Neural Networks (CNNs). The dataset utilized for training and evaluation is sourced from the Dog Breed Identification dataset, encompassing 120 distinct breeds. A pre-trained CNN model is employed, and through the application of transfer learning, the proposed models achieve an impressive accuracy rate of 99%. The primary objective is to enable users to submit images of dogs, with the model accurately identifying the specific breed from the diverse dataset. The research underscores the efficacy of transfer learning and pretrained models in achieving exceptional accuracy for the nuanced task of dog breed classification, contributing to the advancements in image recognition within the context of diverse canine breeds. © 2024 IEEE.

关键词： Convolutional neural networks

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