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Found 51 papers, 20 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.
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.
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.
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.
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.
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 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.
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.
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.
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 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.
Benchmarking for Public Health Surveillance tasks on Social Media with a Domain-Specific Pretrained Language Model
We present and release PHS-BERT, a transformer-based PLM, to identify tasks related to public health surveillance on social media.
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.
DeepMTS: Deep Multi-task Learning for Survival Prediction in Patients with Advanced Nasopharyngeal Carcinoma using Pretreatment PET/CT
However, the models using the whole target regions will also include non-relevant background information, while the models using segmented tumor regions will disregard potentially prognostic information existing out of primary tumors (e. g., local lymph node metastasis and adjacent tissue invasion).
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.
Benchmarking for Biomedical Natural Language Processing Tasks with a Domain Specific ALBERT
We present BioALBERT, a domain-specific adaptation of A Lite Bidirectional Encoder Representations from Transformers (ALBERT), trained on biomedical (PubMed and PubMed Central) and clinical (MIMIC-III) corpora and fine tuned for 6 different tasks across 20 benchmark datasets.
Classifying vaccine sentiment tweets by modelling domain-specific representation and commonsense knowledge into context-aware attentive GRU
In this study, to classify vaccine sentiment tweets with limited information, we present a novel end-to-end framework consisting of interconnected components that use domain-specific LM trained on vaccine-related tweets and models commonsense knowledge into a bidirectional gated recurrent network (CK-BiGRU) with context-aware attention.
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.
Prediction of 5-year Progression-Free Survival in Advanced Nasopharyngeal Carcinoma with Pretreatment PET/CT using Multi-Modality Deep Learning-based Radiomics
Methods: A total of 257 patients (170/87 in internal/external cohorts) with advanced NPC (TNM stage III or IVa) were enrolled.
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.
BioALBERT: A Simple and Effective Pre-trained Language Model for Biomedical Named Entity Recognition
In recent years, with the growing amount of biomedical documents, coupled with advancement in natural language processing algorithms, the research on biomedical named entity recognition (BioNER) has increased exponentially.
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.
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.
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.
High-parallelism Inception-like Spiking Neural Networks for Unsupervised Feature Learning
Most STDP-based SNNs adopted a slow-learning Fully-Connected (FC) architecture and used a sub-optimal vote-based scheme for spike decoding.
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.
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.
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.
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].
