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Found 6 papers, 5 papers with code
Self-supervised Learning for Segmentation and Quantification of Dopamine Neurons in Parkinson's Disease
To this end, we propose an end-to-end deep learning framework based on self-supervised learning for the segmentation and quantification of dopaminergic neurons in PD animal models.
DiRA: Discriminative, Restorative, and Adversarial Learning for Self-supervised Medical Image Analysis
Discriminative learning, restorative learning, and adversarial learning have proven beneficial for self-supervised learning schemes in computer vision and medical imaging.
CAiD: Context-Aware Instance Discrimination for Self-supervised Learning in Medical Imaging
Our extensive experiments demonstrate that CAiD (1) enriches representations learned from existing instance discrimination methods; (2) delivers more discriminative features by adequately capturing finer contextual information from individual medial images; and (3) improves reusability of low/mid-level features compared to standard instance discriminative methods.
A Systematic Benchmarking Analysis of Transfer Learning for Medical Image Analysis
Transfer learning from supervised ImageNet models has been frequently used in medical image analysis.
Transferable Visual Words: Exploiting the Semantics of Anatomical Patterns for Self-supervised Learning
This paper introduces a new concept called "transferable visual words" (TransVW), aiming to achieve annotation efficiency for deep learning in medical image analysis.
Learning Semantics-enriched Representation via Self-discovery, Self-classification, and Self-restoration
To this end, we train deep models to learn semantically enriched visual representation by self-discovery, self-classification, and self-restoration of the anatomy underneath medical images, resulting in a semantics-enriched, general-purpose, pre-trained 3D model, named Semantic Genesis.
Ranked #1 on
Lung Nodule Detection
on LUNA2016 FPRED
