Search Results for author:
Found 39 papers, 6 papers with code
U-WNO:U-Net-enhanced Wavelet Neural Operator for fetal head segmentation
This article describes the development of a novel U-Net-enhanced Wavelet Neural Operator (U-WNO), which combines wavelet decomposition, operator learning, and an encoder-decoder mechanism.
Leveraging Auxiliary Classification for Rib Fracture Segmentation
Thoracic trauma often results in rib fractures, which demand swift and accurate diagnosis for effective treatment.
RibCageImp: A Deep Learning Framework for 3D Ribcage Implant Generation
To the best of our knowledge, this is the first investigation into automated thoracic implant generation using deep learning approaches.
Client Contribution Normalization for Enhanced Federated Learning
Federated Learning (FL) offers a promising alternative by enabling collaborative training of a global model across decentralized devices without data sharing.
Enhanced Survival Prediction in Head and Neck Cancer Using Convolutional Block Attention and Multimodal Data Fusion
Accurate survival prediction in head and neck cancer (HNC) is essential for guiding clinical decision-making and optimizing treatment strategies.
Survival Prediction in Lung Cancer through Multi-Modal Representation Learning
This paper presents a novel approach to survival prediction by harnessing comprehensive information from CT and PET scans, along with associated Genomic data.
Smart CSI Processing for Accruate Commodity WiFi-based Humidity Sensing
Our empirical investigation shows that our enhanced framework can improve the accuracy of humidity sensing to 97%.
Current Symmetry Group Equivariant Convolution Frameworks for Representation Learning
Euclidean deep learning is often inadequate for addressing real-world signals where the representation space is irregular and curved with complex topologies.
F2former: When Fractional Fourier Meets Deep Wiener Deconvolution and Selective Frequency Transformer for Image Deblurring
We design F2TB consisting of a fractional frequency aware self-attention (F2SA) to estimate element-wise product attention based on important frequency components and a novel feed-forward network based on frequency division multiplexing (FM-FFN) to refine high and low frequency features separately for efficient latent clear image restoration.
CoBooM: Codebook Guided Bootstrapping for Medical Image Representation Learning
In this context, we propose CoBooM, a novel framework for self-supervised medical image learning by integrating continuous and discrete representations.
Translating Imaging to Genomics: Leveraging Transformers for Predictive Modeling
This work paves the way for the use of non-invasive imaging modalities for precise and personalized healthcare, allowing for a better understanding of diseases and treatment.
Securing V2I Backscattering from Eavesdropper
This study proposes a secure framework for vehicle-to-infrastructure (V2I) backscattering near an eavesdropping vehicle to maximize the sum secrecy rate of V2I backscatter communication over multiple coherence slots.
OPTiML: Dense Semantic Invariance Using Optimal Transport for Self-Supervised Medical Image Representation
In response to these constraints, we introduce a novel SSL framework OPTiML, employing optimal transport (OT), to capture the dense semantic invariance and fine-grained details, thereby enhancing the overall effectiveness of SSL in medical image representation learning.
MLVICX: Multi-Level Variance-Covariance Exploration for Chest X-ray Self-Supervised Representation Learning
The performance enhancements we observe across various downstream tasks highlight the significance of the proposed approach in enhancing the utility of chest X-ray embeddings for precision medical diagnosis and comprehensive image analysis.
Multi-Agent Reinforcement Learning for Offloading Cellular Communications with Cooperating UAVs
Effective solutions for intelligent data collection in terrestrial cellular networks are crucial, especially in the context of Internet of Things applications.
Jointly Optimal RIS Placement and Power Allocation for Underlay D2D Communications: An Outage Probability Minimization Approach
In the transformed problem, an expression for the average value of the signal-to-interference-noise ratio (SINR) at the D2D receiver is derived in closed-form.
On the Opportunities and Challenges of Foundation Models for Geospatial Artificial Intelligence
In this work, we explore the promises and challenges of developing multimodal foundation models for GeoAI.
Distilling Calibrated Student from an Uncalibrated Teacher
Knowledge distillation is a common technique for improving the performance of a shallow student network by transferring information from a teacher network, which in general, is comparatively large and deep.
GITz: Graphene-assisted IRS Design for THz Communication
Graphene-based intelligent reflecting surface (GIRS) has been proved to provide a promising propagation environment to enhance the quality of high frequency terahertz (THz) wireless communication.
Large Scale Time-Series Representation Learning via Simultaneous Low and High Frequency Feature Bootstrapping
To tackle these problems, we propose a non-contrastive self-supervised learning approach efficiently captures low and high-frequency time-varying features in a cost-effective manner.
Circuit Characterization of IRS to Control Beamforming Design for Efficient Wireless Communication
We have obtained closed-form expressions of PS, RA and $C$ in terms of transmission frequency of signal incident to IRS and various electrical parameters of IRS circuit, with a novel touch towards an accurate analytical model for a better beamforming design perspective.
Pose Invariant Person Re-Identification using Robust Pose-transformation GAN
The given instance of the person is modelled in varying poses and these features are effectively combined through the Feature Fusion Network.
Domain Adaptive Egocentric Person Re-identification
Machine learning models trained on the publicly available large scale re-ID datasets cannot be applied to egocentric re-ID due to the dataset bias problem.
Probabilistic Trust Intervals for Out of Distribution Detection
In this paper, we propose a very simple approach for enhancing the ability of a pretrained network to detect OOD inputs without even altering the original parameter values.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Indirect Supervision to Mitigate Perturbations
We propose to model this problem in indirect supervision framework, where we assume that the gold standard data is missing, however, a variable dependent on it is available and the dependency of the observed variable is stated by the considered downstream DNN.
Neural Pooling for Graph Neural Networks
Our proposed methods have the ability to handle variable number of nodes in different graphs, and are also invariant to the isomorphic structures of graphs.
Target-Independent Domain Adaptation for WBC Classification using Generative Latent Search
We prove the existence of such a clone given that infinite number of data points can be sampled from the source distribution.
Effect of The Latent Structure on Clustering with GANs
We derive from first principles, the necessary and sufficient conditions needed to achieve faithful clustering in the GAN framework: (i) presence of a multimodal latent space with adjustable priors, (ii) existence of a latent space inversion mechanism and (iii) imposition of the desired cluster priors on the latent space.
A Robust Pose Transformational GAN for Pose Guided Person Image Synthesis
Generating photorealistic images of human subjects in any unseen pose have crucial applications in generating a complete appearance model of the subject.
Unsupervised Anomalous Trajectory Detection for Crowded Scenes
The proposed work is based on four major steps, namely, extraction of trajectories from crowded scene video, extraction of several features from these trajectories, independent mean-shift clustering and anomaly detection.
Learning Rotation Adaptive Correlation Filters in Robust Visual Object Tracking
Correlation Filter (CF) trackers are one of the most widely used categories in tracking.
Variational Inference with Latent Space Quantization for Adversarial Resilience
Specifically, we simultaneously auto-encode the data manifold and its perturbations implicitly through the perturbations of the regularized and quantized generative latent space, realized using variational inference.
How You See Me
Convolution Neural Networks is one of the most powerful tools in the present era of science.
Mode matching in GANs through latent space learning and inversion
Generative adversarial networks (GANs) have shown remarkable success in generation of unstructured data, such as, natural images.
Unsupervised Conditional Generation using noise engineered mode matching GAN
Conditional generation refers to the process of sampling from an unknown distribution conditioned on semantics of the data.
Stellar Cluster Detection using GMM with Deep Variational Autoencoder
Detecting stellar clusters have always been an important research problem in Astronomy.
Localization: A Missing Link in the Pipeline of Object Matching and Registration
These approaches require object-centric images to perform matching.
Unsupervised Despeckling
Contrast and quality of ultrasound images are adversely affected by the excessive presence of speckle.
Rotation Adaptive Visual Object Tracking with Motion Consistency
Therefore, one of the major aspects of this paper is to investigate the outcome of rotation adaptiveness in visual object tracking.
