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Found 25 papers, 7 papers with code
Enhancing medical vision-language contrastive learning via inter-matching relation modelling
These learned image representations can be transferred to and benefit various downstream medical vision tasks such as disease classification and segmentation.
PET Synthesis via Self-supervised Adaptive Residual Estimation Generative Adversarial Network
We introduce (1) An adaptive residual estimation mapping mechanism, AE-Net, designed to dynamically rectify the preliminary synthesized PET images by taking the residual map between the low-dose PET and synthesized output as the input, and (2) A self-supervised pre-training strategy to enhance the feature representation of the coarse generator.
AutoFuse: Automatic Fusion Networks for Deformable Medical Image Registration
However, DNN-based registration needs to explore the spatial information of each image and fuse this information to characterize spatial correspondence.
Non-iterative Coarse-to-fine Transformer Networks for Joint Affine and Deformable Image Registration
Recently, Non-Iterative Coarse-to-finE (NICE) registration methods have been proposed to perform coarse-to-fine registration in a single network and showed advantages in both registration accuracy and runtime.
Merging-Diverging Hybrid Transformer Networks for Survival Prediction in Head and Neck Cancer
In view of this, we propose a merging-diverging learning framework for survival prediction from multi-modality images.
AdaMSS: Adaptive Multi-Modality Segmentation-to-Survival Learning for Survival Outcome Prediction from PET/CT Images
Instead of adopting MTL, we propose a novel Segmentation-to-Survival Learning (SSL) strategy, where our AdaMSS is trained for tumor segmentation and survival prediction sequentially in two stages.
Hyper-Connected Transformer Network for Multi-Modality PET-CT Segmentation
We also introduced a hyper connected fusion to fuse the contextual and complementary image features across multiple transformers in an iterative manner.
Spatio-Temporal Dual-Stream Neural Network for Sequential Whole-Body PET Segmentation
Our ST-DSNN learns and accumulates image features from the PET images done over time.
Predicting Distant Metastases in Soft-Tissue Sarcomas from PET-CT scans using Constrained Hierarchical Multi-Modality Feature Learning
Most CNNs are designed for single-modality imaging data (CT or PET alone) and do not exploit the information embedded in PET-CT where there is a combination of an anatomical and functional imaging modality.
Graph-Based Intercategory and Intermodality Network for Multilabel Classification and Melanoma Diagnosis of Skin Lesions in Dermoscopy and Clinical Images
Further, there is not a method to exploit the intercategory relationships in the 7PC.
Enhancing Medical Image Registration via Appearance Adjustment Networks
In this study, we propose an Appearance Adjustment Network (AAN) to enhance the adaptability of DLRs to appearance variations.
Attention-Enhanced Cross-Task Network for Analysing Multiple Attributes of Lung Nodules in CT
Furthermore, lung nodules are often heterogeneous in the cross-sectional image slices of a 3D volume.
Multimodal Spatial Attention Module for Targeting Multimodal PET-CT Lung Tumor Segmentation
Our MSAM can be applied to common backbone architectures and trained end-to-end.
Deep Multi-Scale Resemblance Network for the Sub-class Differentiation of Adrenal Masses on Computed Tomography Images
The application of CNNs, to adrenal masses is challenging due to large intra-class variations, large inter-class similarities and imbalanced training data due to the size of the mass lesions.
Multi-Modality Information Fusion for Radiomics-based Neural Architecture Search
'Radiomics' is a method that extracts mineable quantitative features from radiographic images.
Semi-supervised estimation of event temporal length for cell event detection
Cell event detection in cell videos is essential for monitoring of cellular behavior over extended time periods.
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.
Automated Segmentation of the Optic Disk and Cup using Dual-Stage Fully Convolutional Networks
Our approach leverages deep residual architectures and FCNs and learns and infers the location of the optic cup and disk in a step-wise manner with fine-grained details.
Co-Learning Feature Fusion Maps from PET-CT Images of Lung Cancer
Our aim is to improve fusion of the complementary information in multi-modality PET-CT with a new supervised convolutional neural network (CNN) that learns to fuse complementary information for multi-modality medical image analysis.
3D Global Convolutional Adversarial Network\\ for Prostate MR Volume Segmentation
In this paper, we propose a 3D Global Convolutional Adversarial Network (3D GCA-Net) to address efficient prostate MR volume segmentation.
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.
An unsupervised long short-term memory neural network for event detection in cell videos
We compared our method to several published supervised methods evaluated on the same dataset and to a supervised LSTM method with a similar design and configuration to our unsupervised method.
Synthesis of Positron Emission Tomography (PET) Images via Multi-channel Generative Adversarial Networks (GANs)
Positron emission tomography (PET) image synthesis plays an important role, which can be used to boost the training data for computer aided diagnosis systems.
Morphometry-Based Longitudinal Neurodegeneration Simulation with MR Imaging
We present a longitudinal MR simulation framework which simulates the future neurodegenerative progression by outputting the predicted follow-up MR image and the voxel-based morphometry (VBM) map.
