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Found 10 papers, 3 papers with code
Toward Fairness via Maximum Mean Discrepancy Regularization on Logits Space
Fairness has become increasingly pivotal in machine learning for high-risk applications such as machine learning in healthcare and facial recognition.
Achieve Fairness without Demographics for Dermatological Disease Diagnosis
To overcome this, we propose a method enabling fair predictions for sensitive attributes during the testing phase without using such information during training.
AME-CAM: Attentive Multiple-Exit CAM for Weakly Supervised Segmentation on MRI Brain Tumor
Magnetic resonance imaging (MRI) is commonly used for brain tumor segmentation, which is critical for patient evaluation and treatment planning.
Toward Fairness Through Fair Multi-Exit Framework for Dermatological Disease Diagnosis
Fairness has become increasingly pivotal in medical image recognition.
A Novel Confidence Induced Class Activation Mapping for MRI Brain Tumor Segmentation
Magnetic resonance imaging (MRI) is a commonly used technique for brain tumor segmentation, which is critical for evaluating patients and planning treatment.
Conditional Diffusion Models for Weakly Supervised Medical Image Segmentation
Recent advances in denoising diffusion probabilistic models have shown great success in image synthesis tasks.
Fair Multi-Exit Framework for Facial Attribute Classification
Fairness has become increasingly pivotal in facial recognition.
Representative Image Feature Extraction via Contrastive Learning Pretraining for Chest X-ray Report Generation
Medical report generation is a challenging task since it is time-consuming and requires expertise from experienced radiologists.
"One-Shot" Reduction of Additive Artifacts in Medical Images
Medical images may contain various types of artifacts with different patterns and mixtures, which depend on many factors such as scan setting, machine condition, patients' characteristics, surrounding environment, etc.
Do Noises Bother Human and Neural Networks In the Same Way? A Medical Image Analysis Perspective
Deep learning had already demonstrated its power in medical images, including denoising, classification, segmentation, etc.
