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Found 13 papers, 4 papers with code
Self-Supervised Multi-Modality Learning for Multi-Label Skin Lesion Classification
The clinical diagnosis of skin lesion involves the analysis of dermoscopic and clinical modalities.
A Dual-branch Self-supervised Representation Learning Framework for Tumour Segmentation in Whole Slide Images
These SSL approaches, however, are not designed for handling multi-resolution WSIs, which limits their performance in learning discriminative image features.
A Review of Predictive and Contrastive Self-supervised Learning for Medical Images
Over the last decade, supervised deep learning on manually annotated big data has been progressing significantly on computer vision tasks.
UNAEN: Unsupervised Abnormality Extraction Network for MRI Motion Artifact Reduction
Our results substantiate the potential of UNAEN as a promising solution applicable in real-world clinical environments, with the capability to enhance diagnostic accuracy and facilitate image-guided therapies.
Z-SSMNet: A Zonal-aware Self-Supervised Mesh Network for Prostate Cancer Detection and Diagnosis in bpMRI
However, existing state of the art AI algorithms which are based on deep learning technology are often limited to 2D images that fails to capture inter-slice correlations in 3D volumetric images.
Unsupervised Representation Learning for 3D MRI Super Resolution with Degradation Adaptation
However, the difference in degradation representations between synthetic and authentic LR images suppresses the quality of SR images reconstructed from authentic LR images.
Self-supervised Representation Learning Framework for Remote Physiological Measurement Using Spatiotemporal Augmentation Loss
To address these problems, we present a novel self-supervised spatiotemporal learning framework for remote physiological signal representation learning, where there is a lack of labelled training data.
A Spatial Guided Self-supervised Clustering Network for Medical Image Segmentation
Hence, we propose a new spatial guided self-supervised clustering network (SGSCN) for medical image segmentation, where we introduce multiple loss functions designed to aid in grouping image pixels that are spatially connected and have similar feature representations.
A Spatiotemporal Volumetric Interpolation Network for 4D Dynamic Medical Image
SVIN introduces dual networks: first is the spatiotemporal motion network that leverages the 3D convolutional neural network (CNN) for unsupervised parametric volumetric registration to derive spatiotemporal motion field from two-image volumes; the second is the sequential volumetric interpolation network, which uses the derived motion field to interpolate image volumes, together with a new regression-based module to characterize the periodic motion cycles in functional organ structures.
Unsupervised Feature Learning with K-means and An Ensemble of Deep Convolutional Neural Networks for Medical Image Classification
Hence, we propose a new unsupervised feature learning method that learns feature representations to then differentiate dissimilar medical images using an ensemble of different convolutional neural networks (CNNs) and K-means clustering.
Unsupervised Deep Transfer Feature Learning for Medical Image Classification
The accuracy and robustness of image classification with supervised deep learning are dependent on the availability of large-scale, annotated training data.
Convolutional Sparse Kernel Network for Unsupervised Medical Image Analysis
The availability of large-scale annotated image datasets and recent advances in supervised deep learning methods enable the end-to-end derivation of representative image features that can impact a variety of image analysis problems.
Automatic Skin Lesion Analysis using Large-scale Dermoscopy Images and Deep Residual Networks
Dermoscopy images play an important role in the non-invasive early detection of melanoma [1].
