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Found 23 papers, 0 papers with code
Learning using granularity statistical invariants for classification
Learning using statistical invariants (LUSI) is a new learning paradigm, which adopts weak convergence mechanism, and can be applied to a wider range of classification problems.
The Neglected Tails of Vision-Language Models
We address this by using large language models (LLMs) to count the number of pretraining texts that contain synonyms of these concepts.
Technical Report: On the Convergence of Gossip Learning in the Presence of Node Inaccessibility
In this paper, we formulate and investigate the impact of inaccessible nodes to GL under a dynamic network topology.
Cross-Shaped Windows Transformer with Self-supervised Pretraining for Clinically Significant Prostate Cancer Detection in Bi-parametric MRI
We introduce a novel end-to-end Cross-Shaped windows (CSwin) transformer UNet model, CSwin UNet, to detect clinically significant prostate cancer (csPCa) in prostate bi-parametric MR imaging (bpMRI) and demonstrate the effectiveness of our proposed self-supervised pre-training framework.
Cycle-guided Denoising Diffusion Probability Model for 3D Cross-modality MRI Synthesis
The two DDPMs exchange random latent noise in the reverse processes, which helps to regularize both DDPMs and generate matching images in two modalities.
Deep Learning-based Multi-Organ CT Segmentation with Adversarial Data Augmentation
In this work, we propose an adversarial attack-based data augmentation method to improve the deep-learning-based segmentation algorithm for the delineation of Organs-At-Risk (OAR) in abdominal Computed Tomography (CT) to facilitate radiation therapy.
Landmark Tracking in Liver US images Using Cascade Convolutional Neural Networks with Long Short-Term Memory
We further tested the proposed model on 69 landmarks from the testing dataset that has a similar image pattern to the training pattern, resulting in a mean tracking error of 0. 94+/-0. 83 mm.
Deformable Image Registration using Unsupervised Deep Learning for CBCT-guided Abdominal Radiotherapy
The STN consists of a global generative adversarial network (GlobalGAN) and a local GAN (LocalGAN) to predict the coarse- and fine-scale motions, respectively.
Technical Report: Assisting Backdoor Federated Learning with Whole Population Knowledge Alignment
Single-shot backdoor attack achieves high accuracy on both the main task and backdoor sub-task when injected at the FL model convergence.
Reinforcement Learning in Medical Image Analysis: Concepts, Applications, Challenges, and Future Directions
Compared to the enormous deployments of supervised and unsupervised learning models, attempts to use reinforcement learning in medical image analysis are scarce.
FedCAT: Towards Accurate Federated Learning via Device Concatenation
As a promising distributed machine learning paradigm, Federated Learning (FL) enables all the involved devices to train a global model collaboratively without exposing their local data privacy.
Towards Fast and Accurate Federated Learning with non-IID Data for Cloud-Based IoT Applications
However, since our grouping method is based on the similarity of extracted feature maps from IoT devices, it may incur additional risks of privacy exposure.
Efficient Federated Learning for AIoT Applications Using Knowledge Distillation
Inspired by Knowledge Distillation (KD) that can increase the model accuracy, our approach adds the soft targets used by KD to the FL model training, which occupies negligible network resources.
Deep Learning Based Antenna-time Domain Channel Extrapolation for Hybrid mmWave Massive MIMO
We design a latent ordinary differential equation (ODE)-based network under the variational auto-encoder (VAE) framework to learn the mapping function from the partial uplink channels to the full downlink ones at the BS side.
Artificial Intelligence in Tumor Subregion Analysis Based on Medical Imaging: A Review
We summarize the latest AI-based methods for tumor subregion analysis and their applications.
Synthetic MRI-aided Head-and-Neck Organs-at-Risk Auto-Delineation for CBCT-guided Adaptive Radiotherapy
The proposed method was evaluated on a cohort of 65 HN cancer patients.
Medical Physics Image and Video Processing
FDA3 : Federated Defense Against Adversarial Attacks for Cloud-Based IIoT Applications
Along with the proliferation of Artificial Intelligence (AI) and Internet of Things (IoT) techniques, various kinds of adversarial attacks are increasingly emerging to fool Deep Neural Networks (DNNs) used by Industrial IoT (IIoT) applications.
Deep Learning in Multi-organ Segmentation
This paper presents a review of deep learning (DL) in multi-organ segmentation.
Machine Learning in Quantitative PET Imaging
This paper reviewed the machine learning-based studies for quantitative positron emission tomography (PET).
Deep Learning in Medical Image Registration: A Review
Lastly, we analyzed the statistics of all the cited works from various aspects, revealing the popularity and future trend of development in medical image registration using deep learning.
Feature Fusion Use Unsupervised Prior Knowledge to Let Small Object Represent
Fusing low level and high level features is a widely used strategy to provide details that might be missing during convolution and pooling.
Adversarial FDI Attack against AC State Estimation with ANN
Artificial neural network (ANN) provides superior accuracy for nonlinear alternating current (AC) state estimation (SE) in smart grid over traditional methods.
A New Probabilistic Algorithm for Approximate Model Counting
Constrained counting is important in domains ranging from artificial intelligence to software analysis.
