Weakly Supervised Deep Learning in Radiology
Deep learning (DL) is currently the standard artificial intelligence tool for computer-based
image analysis in radiology. Traditionally, DL models have been trained with strongly …
image analysis in radiology. Traditionally, DL models have been trained with strongly …
Dive into the details of self-supervised learning for medical image analysis
Self-supervised learning (SSL) has achieved remarkable performance in various medical
imaging tasks by dint of priors from massive unlabeled data. However, regarding a specific …
imaging tasks by dint of priors from massive unlabeled data. However, regarding a specific …
Delving into masked autoencoders for multi-label thorax disease classification
Abstract Vision Transformer (ViT) has become one of the most popular neural architectures
due to its simplicity, scalability, and compelling performance in multiple vision tasks …
due to its simplicity, scalability, and compelling performance in multiple vision tasks …
Voco: A simple-yet-effective volume contrastive learning framework for 3d medical image analysis
Abstract Self-Supervised Learning (SSL) has demonstrated promising results in 3D medical
image analysis. However the lack of high-level semantics in pre-training still heavily hinders …
image analysis. However the lack of high-level semantics in pre-training still heavily hinders …
Caid: Context-aware instance discrimination for self-supervised learning in medical imaging
MRH Taher, F Haghighi… - … on Medical Imaging …, 2022 - proceedings.mlr.press
Recently, self-supervised instance discrimination methods have achieved significant
success in learning visual representations from unlabeled photographic images. However …
success in learning visual representations from unlabeled photographic images. However …
Learning to distill global representation for sparse-view CT
Sparse-view computed tomography (CT)---using a small number of projections for
tomographic reconstruction---enables much lower radiation dose to patients and …
tomographic reconstruction---enables much lower radiation dose to patients and …
Anatomical invariance modeling and semantic alignment for self-supervised learning in 3d medical image analysis
Self-supervised learning (SSL) has recently achieved promising performance for 3D medical
image analysis tasks. Most current methods follow existing SSL paradigm originally …
image analysis tasks. Most current methods follow existing SSL paradigm originally …
Self-supervised learning framework application for medical image analysis: a review and summary
X Zeng, N Abdullah, P Sumari - BioMedical Engineering OnLine, 2024 - Springer
Manual annotation of medical image datasets is labor-intensive and prone to biases.
Moreover, the rate at which image data accumulates significantly outpaces the speed of …
Moreover, the rate at which image data accumulates significantly outpaces the speed of …
[HTML][HTML] Review of multimodal machine learning approaches in healthcare
Abstract Machine learning methods in healthcare have traditionally focused on using data
from a single modality, limiting their ability to effectively replicate the clinical practice of …
from a single modality, limiting their ability to effectively replicate the clinical practice of …
Self-supervised learning for medical image analysis: Discriminative, restorative, or adversarial?
Discriminative, restorative, and adversarial learning have proven beneficial for self-
supervised learning schemes in computer vision and medical imaging. Existing efforts …
supervised learning schemes in computer vision and medical imaging. Existing efforts …