Search Results for author:
Found 18 papers, 6 papers with code
Enhancing Generative Class Incremental Learning Performance with Model Forgetting Approach
This study presents a novel approach to Generative Class Incremental Learning (GCIL) by introducing the forgetting mechanism, aimed at dynamically managing class information for better adaptation to streaming data.
Prompt-based Personalized Federated Learning for Medical Visual Question Answering
We present a novel prompt-based personalized federated learning (pFL) method to address data heterogeneity and privacy concerns in traditional medical visual question answering (VQA) methods.
Medical Visual Question Answering
Personalized Federated Learning
+2
Importance-Aware Adaptive Dataset Distillation
Based on this observation, we propose an importance-aware adaptive dataset distillation (IADD) method that can improve distillation performance by automatically assigning importance weights to different network parameters during distillation, thereby synthesizing more robust distilled datasets.
Few-shot Personalized Saliency Prediction Based on Inter-personnel Gaze Patterns
To efficiently treat the PSMs of other persons, this paper focuses on the selection of images to acquire eye-tracking data and the preservation of structural information of PSMs of other persons.
Interpretable Visual Question Answering Referring to Outside Knowledge
We present a novel multimodal interpretable VQA model that can answer the question more accurately and generate diverse explanations.
Boosting Automatic COVID-19 Detection Performance with Self-Supervised Learning and Batch Knowledge Ensembling
On the other hand, batch knowledge ensembling-based fine-tuning can utilize category knowledge of images in a batch according to their visual feature similarities to improve detection performance.
COVID-19 Detection Based on Self-Supervised Transfer Learning Using Chest X-Ray Images
We compared six self-supervised learning (SSL) methods (Cross, BYOL, SimSiam, SimCLR, PIRL-jigsaw, and PIRL-rotation) with the proposed method.
Union-set Multi-source Model Adaptation for Semantic Segmentation
Model adaptation is proposed as a new domain adaptation problem which requires access to a pre-trained model instead of data for the source domain.
RGMIM: Region-Guided Masked Image Modeling for Learning Meaningful Representation from X-Ray Images
To address this issue, this work aims to improve MIM for medical images and evaluate its effectiveness in an open X-ray image dataset.
Dataset Distillation for Medical Dataset Sharing
Sharing medical datasets between hospitals is challenging because of the privacy-protection problem and the massive cost of transmitting and storing many high-resolution medical images.
Dataset Complexity Assessment Based on Cumulative Maximum Scaled Area Under Laplacian Spectrum
Dataset complexity assessment aims to predict classification performance on a dataset with complexity calculation before training a classifier, which can also be used for classifier selection and dataset reduction.
Compressed Gastric Image Generation Based on Soft-Label Dataset Distillation for Medical Data Sharing
Furthermore, our method can extract essential weights of DCNN models to reduce the memory required to save trained models for efficient medical data sharing.
Dataset Distillation Using Parameter Pruning
In this study, we propose a novel dataset distillation method based on parameter pruning.
Gromov-Wasserstein Autoencoders
The GW metric measures the distance structure-oriented discrepancy between distributions even with different dimensionalities, which provides a direct measure between the latent and data spaces.
Self-Knowledge Distillation based Self-Supervised Learning for Covid-19 Detection from Chest X-Ray Images
The global outbreak of the Coronavirus 2019 (COVID-19) has overloaded worldwide healthcare systems.
TriBYOL: Triplet BYOL for Self-Supervised Representation Learning
Our method provides a feasible solution for self-supervised learning with real-world high-resolution images that uses small batch sizes.
Self-Supervised Learning for Gastritis Detection with Gastric X-ray Images
The effectiveness of the proposed self-supervised learning method in gastritis detection is verified using a few annotated gastric X-ray images.
Soft-Label Anonymous Gastric X-ray Image Distillation
The idea of our distillation method is to extract the valid information of the medical dataset and generate a tiny distilled dataset that has a different data distribution.
