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Found 91 papers, 22 papers with code
DeltaNet: Conditional Medical Report Generation for COVID-19 Diagnosis
To reduce the workload of radiologists, we propose DeltaNet to generate medical reports automatically.
Meta-hallucinator: Towards Few-Shot Cross-Modality Cardiac Image Segmentation
To achieve efficient few-shot cross-modality segmentation, we propose a novel transformation-consistent meta-hallucination framework, meta-hallucinator, with the goal of learning to diversify data distributions and generate useful examples for enhancing cross-modality performance.
PELE scores: Pelvic X-ray Landmark Detection by Pelvis Extraction and Enhancement
The pelvis, the lower part of the trunk, supports and balances the trunk.
Unified Multi-Modal Image Synthesis for Missing Modality Imputation
Besides, we propose a Dynamic Feature Unification Module to integrate information from a varying number of available modalities, which enables the network to be robust to random missing modalities.
FedFTN: Personalized Federated Learning with Deep Feature Transformation Network for Multi-institutional Low-count PET Denoising
While previous federated learning (FL) algorithms enable multi-institution collaborative training without the need of aggregating local data, addressing the large domain shift in the application of multi-institutional low-count PET denoising remains a challenge and is still highly under-explored.
DiffULD: Diffusive Universal Lesion Detection
But they are still ineffective for ULD due to the insufficient training targets.
Causal Image Synthesis of Brain MR in 3D
Clinical decision making requires counterfactual reasoning based on a factual medical image and thus necessitates causal image synthesis.
Evidence-aware multi-modal data fusion and its application to total knee replacement prediction
For each branch, there is an evidence network that takes the extracted features as input and outputs an evidence score, which is designed to represent the reliability of the output from the current branch.
DuDoRNeXt: A hybrid model for dual-domain undersampled MRI reconstruction
Recent deep learning methods for MRI reconstruction adopt CNN or ViT as backbone, which lack in utilizing the complementary properties of CNN and ViT.
GDDS: Pulmonary Bronchioles Segmentation with Group Deep Dense Supervision
To address these problems, we contribute a new bronchial segmentation method based on Group Deep Dense Supervision (GDDS) that emphasizes fine-scale bronchioles segmentation in a simple-but-effective manner.
Lung Nodule Segmentation and Low-Confidence Region Prediction with Uncertainty-Aware Attention Mechanism
In this paper, we propose an Uncertainty-Aware Attention Mechanism (UAAM) that utilizes consensus and disagreements among multiple annotations to facilitate better segmentation.
FairAdaBN: Mitigating unfairness with adaptive batch normalization and its application to dermatological disease classification
Deep learning is becoming increasingly ubiquitous in medical research and applications while involving sensitive information and even critical diagnosis decisions.
O2CTA: Introducing Annotations from OCT to CCTA in Coronary Plaque Analysis
Therefore, it is clinically critical to introduce annotations of plaque tissue and lumen characteristics from OCT to paired CCTA scans, denoted as \textbf{the O2CTA problem} in this paper.
Distortion-Disentangled Contrastive Learning
The POCL method typically uses a single loss function to extract the distortion invariant representation (DIR) which describes the proximity of positive-pair representations affected by different distortions.
Mixed-order self-paced curriculum learning for universal lesion detection
Most SCL methods commonly adopt a loss-based strategy of estimating data difficulty and deweighting the `hard' samples in the early training stage.
Multi-site Organ Segmentation with Federated Partial Supervision and Site Adaptation
Objective and Impact Statement: Accurate organ segmentation is critical for many clinical applications at different clinical sites, which may have their specific application requirements that concern different organs.
MURPHY: Relations Matter in Surgical Workflow Analysis
Autonomous robotic surgery has advanced significantly based on analysis of visual and temporal cues in surgical workflow, but relational cues from domain knowledge remain under investigation.
LE-UDA: Label-efficient unsupervised domain adaptation for medical image segmentation
To assess the effectiveness of our method, we conduct extensive experiments on two different tasks for cross-modality segmentation between MRI and CT images.
Information-guided pixel augmentation for pixel-wise contrastive learning
To the best of our knowledge, we are the first to propose a pixel augmentation method with a pixel granularity for enhancing unsupervised pixel-wise contrastive learning.
DeltaNet:Conditional Medical Report Generation for COVID-19 Diagnosis
To reduce the workload of radiologists, we propose DeltaNet to generate medical reports automatically.
MONAI: An open-source framework for deep learning in healthcare
For AI models to be used clinically, they need to be made safe, reproducible and robust, and the underlying software framework must be aware of the particularities (e. g. geometry, physiology, physics) of medical data being processed.
Active CT Reconstruction with a Learned Sampling Policy
Experiments on the VerSe dataset demonstrate this ability of our sampling policy, which is difficult to achieve based on uniform sampling.
DA-VSR: Domain Adaptable Volumetric Super-Resolution For Medical Images
Medical image super-resolution (SR) is an active research area that has many potential applications, including reducing scan time, bettering visual understanding, increasing robustness in downstream tasks, etc.
Progress and Prospects for Fairness in Healthcare and Medical Image Analysis
Machine learning-enabled medical imaging analysis has become a vital part of the current automatic diagnosis system.
Stabilize, Decompose, and Denoise: Self-Supervised Fluoroscopy Denoising
Fluoroscopy is an imaging technique that uses X-ray to obtain a real-time 2D video of the interior of a 3D object, helping surgeons to observe pathological structures and tissue functions especially during intervention.
REGAS: REspiratory-GAted Synthesis of Views for Multi-Phase CBCT Reconstruction from a single 3D CBCT Acquisition
It is a long-standing challenge to reconstruct Cone Beam Computed Tomography (CBCT) of the lung under respiratory motion.
Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectives
Transformer, the latest technological advance of deep learning, has gained prevalence in natural language processing or computer vision.
SATr: Slice Attention with Transformer for Universal Lesion Detection
Universal Lesion Detection (ULD) in computed tomography plays an essential role in computer-aided diagnosis.
DATR: Domain-adaptive transformer for multi-domain landmark detection
In this work, we propose a universal model for multi-domain landmark detection by taking advantage of transformer for modeling long dependencies and develop a domain-adaptive transformer model, named as DATR, which is trained on multiple mixed datasets from different anatomies and capable of detecting landmarks of any image from those anatomies.
DFTR: Depth-supervised Fusion Transformer for Salient Object Detection
This paper, for the first time, seeks to expand the applicability of depth supervision to the Transformer architecture.
Recovering medical images from CT film photos
While medical images such as computed tomography (CT) are stored in DICOM format in hospital PACS, it is still quite routine in many countries to print a film as a transferable medium for the purposes of self-storage and secondary consultation.
Towards performant and reliable undersampled MR reconstruction via diffusion model sampling
Magnetic Resonance (MR) image reconstruction from under-sampled acquisition promises faster scanning time.
Undersampled MRI Reconstruction with Side Information-Guided Normalisation
In this paper, we investigate the use of such side information as normalisation parameters in a convolutional neural network (CNN) to improve undersampled MRI reconstruction.
Rib Suppression in Digital Chest Tomosynthesis
Digital chest tomosynthesis (DCT) is a technique to produce sectional 3D images of a human chest for pulmonary disease screening, with 2D X-ray projections taken within an extremely limited range of angles.
Universal Segmentation of 33 Anatomies
Firstly, while it is ideal to learn such a model from a large-scale, fully-annotated dataset, it is practically hard to curate such a dataset.
MixCL: Pixel label matters to contrastive learning
It is essential for medical image analysis, which is often notorious for its lack of annotations.
Learning Incrementally to Segment Multiple Organs in a CT Image
In each incremental learning (IL) stage, we lose the access to previous data and annotations, whose knowledge is assumingly captured by the current model, and gain the access to a new dataset with annotations of new organ categories, from which we learn to update the organ segmentation model to include the new organs.
Relative distance matters for one-shot landmark detection
Contrastive learning based methods such as cascade comparing to detect (CC2D) have shown great potential for one-shot medical landmark detection.
Radiology Report Generation with a Learned Knowledge Base and Multi-modal Alignment
In clinics, a radiology report is crucial for guiding a patient's treatment.
Which images to label for few-shot medical landmark detection?
We herein propose a novel Sample Choosing Policy (SCP) to select "the most worthy" images for annotation, in the context of few-shot medical landmark detection.
Medical Aegis: Robust adversarial protectors for medical images
To the best of our knowledge, Medical Aegis is the first defense in the literature that successfully addresses the strong adaptive adversarial example attacks to medical images.
DuDoTrans: Dual-Domain Transformer Provides More Attention for Sinogram Restoration in Sparse-View CT Reconstruction
While Computed Tomography (CT) reconstruction from X-ray sinograms is necessary for clinical diagnosis, iodine radiation in the imaging process induces irreversible injury, thereby driving researchers to study sparse-view CT reconstruction, that is, recovering a high-quality CT image from a sparse set of sinogram views.
GAN-based disentanglement learning for chest X-ray rib suppression
Clinical evidence has shown that rib-suppressed chest X-rays (CXRs) can improve the reliability of pulmonary disease diagnosis.
Shallow Attention Network for Polyp Segmentation
To address the above issues, we propose the Shallow Attention Network (SANet) for polyp segmentation.
Ranked #8 on
Video Polyp Segmentation
on SUN-SEG-Easy (Unseen)
Shallow Feature Matters for Weakly Supervised Object Localization
In practice, our SPOL model first generates the CAMs through a novel element-wise multiplication of shallow and deep feature maps, which filters the background noise and generates sharper boundaries robustly.
Where is the disease? Semi-supervised pseudo-normality synthesis from an abnormal image
Pseudo-normality synthesis, which computationally generates a pseudo-normal image from an abnormal one (e. g., with lesions), is critical in many perspectives, from lesion detection, data augmentation to clinical surgery suggestion.
Dual-GAN: Joint BVP and Noise Modeling for Remote Physiological Measurement
Remote photoplethysmography (rPPG) based physiological measurement has great application values in health monitoring, emotion analysis, etc.
CTSpine1K: A Large-Scale Dataset for Spinal Vertebrae Segmentation in Computed Tomography
Spine-related diseases have high morbidity and cause a huge burden of social cost.
Anatomy-guided Multimodal Registration by Learning Segmentation without Ground Truth: Application to Intraprocedural CBCT/MR Liver Segmentation and Registration
Our experimental results on in-house TACE patient data demonstrated that our APA2Seg-Net can generate robust CBCT and MR liver segmentation, and the anatomy-guided registration framework with these segmenters can provide high-quality multimodal registrations.
Conditional Training with Bounding Map for Universal Lesion Detection
Universal Lesion Detection (ULD) in computed tomography plays an essential role in computer-aided diagnosis.
Ranked #3 on
Medical Object Detection
on DeepLesion
Pathological Image Segmentation with Noisy Labels
Segmentation of pathological images is essential for accurate disease diagnosis.
Improving Generalizability in Limited-Angle CT Reconstruction with Sinogram Extrapolation
More importantly, we show that using such a sinogram extrapolation module significantly improves the generalization capability of the model on unseen datasets (e. g., COVID-19 and LIDC datasets) when compared to existing approaches.
Universal Undersampled MRI Reconstruction
Simply mixing images from multiple anatomies for training a single network does not lead to an ideal universal model due to the statistical shift among datasets of various anatomies, the need to retrain from scratch on all datasets with the addition of a new dataset, and the difficulty in dealing with imbalanced sampling when the new dataset is further of a smaller size.
You Only Learn Once: Universal Anatomical Landmark Detection
However, all of those methods are unary in the sense that a highly specialized network is trained for a single task say associated with a particular anatomical region.
Incremental Learning for Multi-organ Segmentation with Partially Labeled Datasets
In each IL stage, we lose access to the previous annotations, whose knowledge is assumingly captured by the current model, and gain the access to a new dataset with annotations of new organ categories, from which we learn to update the organ segmentation model to include the new organs.
One-Shot Medical Landmark Detection
The success of deep learning methods relies on the availability of a large number of datasets with annotations; however, curating such datasets is burdensome, especially for medical images.
U-DuDoNet: Unpaired dual-domain network for CT metal artifact reduction
Recently, both supervised and unsupervised deep learning methods have been widely applied on the CT metal artifact reduction (MAR) task.
Deep reinforcement learning in medical imaging: A literature review
Deep reinforcement learning (DRL) augments the reinforcement learning framework, which learns a sequence of actions that maximizes the expected reward, with the representative power of deep neural networks.
CT Film Recovery via Disentangling Geometric Deformation and Illumination Variation: Simulated Datasets and Deep Models
We also record all accompanying information related to the geometric deformation (such as 3D coordinate, depth, normal, and UV maps) and illumination variation (such as albedo map).
A Hierarchical Feature Constraint to Camouflage Medical Adversarial Attacks
Deep neural networks (DNNs) for medical images are extremely vulnerable to adversarial examples (AEs), which poses security concerns on clinical decision making.
Deep Learning to Segment Pelvic Bones: Large-scale CT Datasets and Baseline Models
Due to the lack of a large-scale pelvic CT dataset with annotations, deep learning methods are not fully explored.
Aggregative Self-Supervised Feature Learning from a Limited Sample
Then, in self-aggregative SSL, we propose to self-complement an existing proxy task with an auxiliary loss function based on a linear centered kernel alignment metric, which explicitly promotes the exploring of where are uncovered by the features learned from a proxy task at hand to further boost the modeling capability.
Label-Free Segmentation of COVID-19 Lesions in Lung CT
Scarcity of annotated images hampers the building of automated solution for reliable COVID-19 diagnosis and evaluation from CT. To alleviate the burden of data annotation, we herein present a label-free approach for segmenting COVID-19 lesions in CT via pixel-level anomaly modeling that mines out the relevant knowledge from normal CT lung scans.
Limited View Tomographic Reconstruction Using a Deep Recurrent Framework with Residual Dense Spatial-Channel Attention Network and Sinogram Consistency
To derive quality reconstruction, previous state-of-the-art methods use UNet-like neural architectures to directly predict the full view reconstruction from limited view data; but these methods leave the deep network architecture issue largely intact and cannot guarantee the consistency between the sinogram of the reconstructed image and the acquired sinogram, leading to a non-ideal reconstruction.
A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises
In this survey paper, we first present traits of medical imaging, highlight both clinical needs and technical challenges in medical imaging, and describe how emerging trends in deep learning are addressing these issues.
Bounding Maps for Universal Lesion Detection
The bounding maps (BMs) are used in two-stage anchor-based ULD frameworks to reduce the FP rate.
Miss the Point: Targeted Adversarial Attack on Multiple Landmark Detection
A comprehensive evaluation on a public dataset for cephalometric landmark detection demonstrates that the adversarial examples generated by ATI-FGSM break the CNN-based network more effectively and efficiently, compared with the original Iterative FGSM attack.
Marginal loss and exclusion loss for partially supervised multi-organ segmentation
Annotating multiple organs in medical images is both costly and time-consuming; therefore, existing multi-organ datasets with labels are often low in sample size and mostly partially labeled, that is, a dataset has a few organs labeled but not all organs.
Embedding Task Knowledge into 3D Neural Networks via Self-supervised Learning
To this end, self-supervised learning (SSL), as a potential solution for deficient annotated data, attracts increasing attentions from the community.
Human Recognition Using Face in Computed Tomography
With the mushrooming use of computed tomography (CT) images in clinical decision making, management of CT data becomes increasingly difficult.
DuDoRNet: Learning a Dual-Domain Recurrent Network for Fast MRI Reconstruction with Deep T1 Prior
In this work, we address the above two limitations by proposing a Dual Domain Recurrent Network (DuDoRNet) with deep T1 prior embedded to simultaneously recover k-space and images for accelerating the acquisition of MRI with a long imaging protocol.
A$^3$DSegNet: Anatomy-aware artifact disentanglement and segmentation network for unpaired segmentation, artifact reduction, and modality translation
In contrast, there exists a wealth of artifact-free, high quality CT images with vertebra annotations.
First image then video: A two-stage network for spatiotemporal video denoising
This two-stage network, when trained in an end-to-end fashion, yields the state-of-the-art performances on the video denoising benchmark Vimeo90K dataset in terms of both denoising quality and computation.
Encoding Metal Mask Projection for Metal Artifact Reduction in Computed Tomography
Metal artifact reduction (MAR) in computed tomography (CT) is a notoriously challenging task because the artifacts are structured and non-local in the image domain.
Encoding CT Anatomy Knowledge for Unpaired Chest X-ray Image Decomposition
We hereby propose a decomposition generative adversarial network (DecGAN) to anatomically decompose a CXR image but with unpaired data.
One-to-one Mapping for Unpaired Image-to-image Translation
Here we propose a self-inverse network learning approach for unpaired image-to-image translation.
Towards Learning a Self-inverse Network for Bidirectional Image-to-image Translation
A self-inverse network shares several distinct advantages: only one network instead of two, better generalization and more restricted parameter space.
3D U$^2$-Net: A 3D Universal U-Net for Multi-Domain Medical Image Segmentation
Instead of a collection of multiple models, it is highly desirable to learn a universal data representation for different tasks, ideally a single model with the addition of a minimal number of parameters steered to each task.
Deep Slice Interpolation via Marginal Super-Resolution, Fusion and Refinement
We propose a marginal super-resolution (MSR) approach based on 2D convolutional neural networks (CNNs) for interpolating an anisotropic brain magnetic resonance scan along the highly under-sampled direction, which is assumed to axial without loss of generality.
Towards multi-sequence MR image recovery from undersampled k-space data
Undersampled MR image recovery has been widely studied for accelerated MR acquisition.
ADN: Artifact Disentanglement Network for Unsupervised Metal Artifact Reduction
Current deep neural network based approaches to computed tomography (CT) metal artifact reduction (MAR) are supervised methods that rely on synthesized metal artifacts for training.
Generative Mask Pyramid Network for CT/CBCT Metal Artifact Reduction with Joint Projection-Sinogram Correction
A conventional approach to computed tomography (CT) or cone beam CT (CBCT) metal artifact reduction is to replace the X-ray projection data within the metal trace with synthesized data.
Artifact Disentanglement Network for Unsupervised Metal Artifact Reduction
Extensive experiments show that our method significantly outperforms the existing unsupervised models for image-to-image translation problems, and achieves comparable performance to existing supervised models on a synthesized dataset.
Multiview 2D/3D Rigid Registration via a Point-Of-Interest Network for Tracking and Triangulation ($\text{POINT}^2$)
As the POI tracker is shift-invariant, $\text{POINT}^2$ is more robust to the initial pose of the 3D pre-intervention image.
Face Completion with Semantic Knowledge and Collaborative Adversarial Learning
Unlike a conventional background inpainting approach that infers a missing area from image patches similar to the background, face completion requires semantic knowledge about the target object for realistic outputs.
Combo Loss: Handling Input and Output Imbalance in Multi-Organ Segmentation
The output imbalance refers to the imbalance between the false positives and false negatives of the inference model.
Select, Attend, and Transfer: Light, Learnable Skip Connections
Skip connections in deep networks have improved both segmentation and classification performance by facilitating the training of deeper network architectures, and reducing the risks for vanishing gradients.
3D Anisotropic Hybrid Network: Transferring Convolutional Features from 2D Images to 3D Anisotropic Volumes
The focal loss is further utilized for more effective end-to-end learning.
Automatic Liver Segmentation Using an Adversarial Image-to-Image Network
Automatic liver segmentation in 3D medical images is essential in many clinical applications, such as pathological diagnosis of hepatic diseases, surgical planning, and postoperative assessment.
Automatic Vertebra Labeling in Large-Scale 3D CT using Deep Image-to-Image Network with Message Passing and Sparsity Regularization
In this paper, we propose an automatic and fast algorithm to localize and label the vertebra centroids in 3D CT volumes.
Iterative Multi-domain Regularized Deep Learning for Anatomical Structure Detection and Segmentation from Ultrasound Images
Accurate detection and segmentation of anatomical structures from ultrasound images are crucial for clinical diagnosis and biometric measurements.
