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Found 32 papers, 14 papers with code
Dual-modal Dynamic Traceback Learning for Medical Report Generation
Recent generative representation learning methods have demonstrated the benefits of dual-modal learning from both image and text modalities.
Full-resolution MLPs Empower Medical Dense Prediction
This textural information is crucial for medical dense prediction as it can differentiate the subtle human anatomy in medical images.
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.
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.
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.
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.
CG-3DSRGAN: A classification guided 3D generative adversarial network for image quality recovery from low-dose PET images
We compared our method to the state-of-the-art methods on whole-body PET with different dose reduction factors (DRFs).
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.
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.
Brain Tumor Sequence Registration with Non-iterative Coarse-to-fine Networks and Dual Deep Supervision
In this study, we focus on brain tumor sequence registration between pre-operative and follow-up Magnetic Resonance Imaging (MRI) scans of brain glioma patients, in the context of Brain Tumor Sequence Registration challenge (BraTS-Reg 2022).
Radiomics-enhanced Deep Multi-task Learning for Outcome Prediction in Head and Neck Cancer
Recently, deep learning methods have been proposed to perform end-to-end outcome prediction so as to remove the reliance on manual segmentation.
Automatic Tumor Segmentation via False Positive Reduction Network for Whole-Body Multi-Modal PET/CT Images
In this study, we introduce a false positive reduction network to overcome this limitation.
Non-iterative Coarse-to-fine Registration based on Single-pass Deep Cumulative Learning
Recently, iterative deep registration methods have been used to alleviate this limitation, where the transformations are iteratively learned in a coarse-to-fine manner.
Unsupervised Difference Learning for Noisy Rigid Image Alignment
Rigid image alignment is a fundamental task in computer vision, while the traditional algorithms are either too sensitive to noise or time-consuming.
The Brain Tumor Sequence Registration (BraTS-Reg) Challenge: Establishing Correspondence Between Pre-Operative and Follow-up MRI Scans of Diffuse Glioma Patients
Registration of longitudinal brain MRI scans containing pathologies is challenging due to dramatic changes in tissue appearance.
Unsupervised Landmark Detection Based Spatiotemporal Motion Estimation for 4D Dynamic Medical Images
In the first stage, we process the raw dense image to extract sparse landmarks to represent the target organ anatomical topology and discard the redundant information that is unnecessary for motion estimation.
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.
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.
Multi-Modality Information Fusion for Radiomics-based Neural Architecture Search
'Radiomics' is a method that extracts mineable quantitative features from radiographic images.
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.
IntersectGAN: Learning Domain Intersection for Generating Images with Multiple Attributes
For example, given two image domains $X_1$ and $X_2$ with certain attributes, the intersection $X_1 \cap X_2$ denotes a new domain where images possess the attributes from both $X_1$ and $X_2$ domains.
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.
Improving Automatic Skin Lesion Segmentation using Adversarial Learning based Data Augmentation
Segmentation of skin lesions is considered as an important step in computer aided diagnosis (CAD) for automated melanoma diagnosis.
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.
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.
Automatic Liver Lesion Detection using Cascaded Deep Residual Networks
Recently, deep learning methods based on fully convolutional networks (FCNs) have been successful in many segmentation problems primarily because they leverage a large labelled dataset to hierarchically learn the features that best correspond to the shallow visual appearance as well as the deep semantics of the areas to be segmented.
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].
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.
