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Found 24 papers, 6 papers with code
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.
PainSeeker: An Automated Method for Assessing Pain in Rats Through Facial Expressions
In this letter, we aim to investigate whether laboratory rats' pain can be automatically assessed through their facial expressions.
The STOIC2021 COVID-19 AI challenge: applying reusable training methodologies to private data
STOIC2021 consisted of a Qualification phase, where participants developed challenge solutions using 2000 publicly available CT scans, and a Final phase, where participants submitted their training methodologies with which solutions were trained on CT scans of 9724 subjects.
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.
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.
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.
Unsupervised cross-user adaptation in taste sensation recognition based on surface electromyography with conformal prediction and domain regularized component analysis
The results show that DRCA improved the classification accuracy on six subjects (p < 0. 05), compared with the baseline models trained only with the source domain data;, while CPSC did not guarantee the accuracy improvement.
CPSC: Conformal prediction with shrunken centroids for efficient prediction reliability quantification and data augmentation, a case in alternative herbal medicine classification with electronic nose
As an effective framework to quantify the prediction reliability, conformal prediction (CP) was developed with the CPKNN (CP with kNN).
Classifying herbal medicine origins by temporal and spectral data mining of electronic nose
In this study, we classified different origins of three categories of herbal medicines with different feature extraction methods: manual feature extraction, mathematical transformation, deep learning algorithms.
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.
Boost AI Power: Data Augmentation Strategies with unlabelled Data and Conformal Prediction, a Case in Alternative Herbal Medicine Discrimination with Electronic Nose
Furthermore, this study provides a systematic analysis of different augmentation strategies.
A review of smartphones based indoor positioning: challenges and applications
The continual proliferation of mobile devices has encouraged much effort in using the smartphones for indoor positioning.
The Role of the Hercules Autonomous Vehicle During the COVID-19 Pandemic: An Autonomous Logistic Vehicle for Contactless Goods Transportation
Moreover, many countries have imposed tough lockdown measures to reduce the virus transmission (e. g., retail, catering) during the pandemic, which causes inconveniences for human daily life.
Entangled Transformer for Image Captioning
This phenomenon is known as the semantic gap between vision and language.
Tool Breakage Detection using Deep Learning
In this work, we use the spindle current approach to detect the breakage of machine tools, which has the high performance of real-time monitoring, low cost, and easy to install.
CT Super-resolution GAN Constrained by the Identical, Residual, and Cycle Learning Ensemble(GAN-CIRCLE)
Specifically, with the generative adversarial network (GAN) as the building block, we enforce the cycle-consistency in terms of the Wasserstein distance to establish a nonlinear end-to-end mapping from noisy LR input images to denoised and deblurred HR outputs.
Structure-sensitive Multi-scale Deep Neural Network for Low-Dose CT Denoising
However, the radiation dose reduction compromises the signal-to-noise ratio (SNR), leading to strong noise and artifacts that down-grade CT image quality.
