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Found 8 papers, 4 papers with code
Think Twice Before Selection: Federated Evidential Active Learning for Medical Image Analysis with Domain Shifts
Extensive experiments and analysis on five real multi-center medical image datasets demonstrate the superiority of FEAL over the state-of-the-art active learning methods in federated scenarios with domain shifts.
Tackling the Incomplete Annotation Issue in Universal Lesion Detection Task By Exploratory Training
Pseudo-label-based methods examine the training data and mine unlabelled objects for retraining, which have shown to be effective to tackle this issue.
Analyzing Deep Learning Based Brain Tumor Segmentation with Missing MRI Modalities
Approaches evaluated include the Adversarial Co-training Network (ACN) and a combination of mmGAN and DeepMedic.
FIBA: Frequency-Injection based Backdoor Attack in Medical Image Analysis
However, designing a unified BA method that can be applied to various MIA systems is challenging due to the diversity of imaging modalities (e. g., X-Ray, CT, and MRI) and analysis tasks (e. g., classification, detection, and segmentation).
Auto Learning Attention
Attention modules have been demonstrated effective in strengthening the representation ability of a neural network via reweighting spatial or channel features or stacking both operations sequentially.
Inter-layer Transition in Neural Architecture Search
Differential Neural Architecture Search (NAS) methods represent the network architecture as a repetitive proxy directed acyclic graph (DAG) and optimize the network weights and architecture weights alternatively in a differential manner.
Autonomous Deep Learning: A Genetic DCNN Designer for Image Classification
Recent years have witnessed the breakthrough success of deep convolutional neural networks (DCNNs) in image classification and other vision applications.
A Tribe Competition-Based Genetic Algorithm for Feature Selection in Pattern Classification
In this paper, a tribe competition-based genetic algorithm (TCbGA) is proposed for feature selection in pattern classification.
