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Found 8 papers, 2 papers with code
Investigating the Robustness of Vision Transformers against Label Noise in Medical Image Classification
Label noise in medical image classification datasets significantly hampers the training of supervised deep learning methods, undermining their generalizability.
How does self-supervised pretraining improve robustness against noisy labels across various medical image classification datasets?
In this study, we address three key questions: i) How does label noise impact various medical image classification datasets?
Medical Vision Language Pretraining: A survey
Medical Vision Language Pretraining (VLP) has recently emerged as a promising solution to the scarcity of labeled data in the medical domain.
Improving Medical Image Classification in Noisy Labels Using Only Self-supervised Pretraining
In this work, we explore contrastive and pretext task-based self-supervised pretraining to initialize the weights of a deep learning classification model for two medical datasets with self-induced noisy labels -- NCT-CRC-HE-100K tissue histological images and COVID-QU-Ex chest X-ray images.
M-VAAL: Multimodal Variational Adversarial Active Learning for Downstream Medical Image Analysis Tasks
Acquiring properly annotated data is expensive in the medical field as it requires experts, time-consuming protocols, and rigorous validation.
How Does Heterogeneous Label Noise Impact Generalization in Neural Nets?
Incorrectly labeled examples, or label noise, is common in real-world computer vision datasets.
Label Geometry Aware Discriminator for Conditional Generative Networks
Multi-domain image-to-image translation with conditional Generative Adversarial Networks (GANs) can generate highly photo realistic images with desired target classes, yet these synthetic images have not always been helpful to improve downstream supervised tasks such as image classification.
Automatic Cobb Angle Detection using Vertebra Detector and Vertebra Corners Regression
Correct evaluation and treatment of Scoliosis require accurate estimation of spinal curvature.
