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Found 34 papers, 4 papers with code
ProtoSAM: One-Shot Medical Image Segmentation With Foundational Models
This work introduces a new framework, ProtoSAM, for one-shot medical image segmentation.
X-ray2CTPA: Generating 3D CTPA scans from 2D X-ray conditioning
Chest X-rays or chest radiography (CXR), commonly used for medical diagnostics, typically enables limited imaging compared to computed tomography (CT) scans, which offer more detailed and accurate three-dimensional data, particularly contrast-enhanced scans like CT Pulmonary Angiography (CTPA).
DINOv2 based Self Supervised Learning For Few Shot Medical Image Segmentation
Deep learning models have emerged as the cornerstone of medical image segmentation, but their efficacy hinges on the availability of extensive manually labeled datasets and their adaptability to unforeseen categories remains a challenge.
A Self Supervised StyleGAN for Image Annotation and Classification with Extremely Limited Labels
In this work, we propose SS-StyleGAN, a self-supervised approach for image annotation and classification suitable for extremely small annotated datasets.
Neural Network Reconstruction of the Left Atrium using Sparse Catheter Paths
Catheter based radiofrequency ablation for pulmonary vein isolation has become the first line of treatment for atrial fibrillation in recent years.
HeartBEiT: Vision Transformer for Electrocardiogram Data Improves Diagnostic Performance at Low Sample Sizes
Thus, we present the first vision-based waveform transformer that can be used to develop specialized models for ECG analysis especially at low sample sizes.
Weakly Supervised Attention Model for RV StrainClassification from volumetric CTPA Scans
We developed a weakly supervised deep learning algorithm, with an emphasis on a novel attention mechanism, to automatically classify RV strain on CTPA.
Learning Rotation Invariant Features for Cryogenic Electron Microscopy Image Reconstruction
A fundamental step in the recovering of the 3D single-particle structure is to align its 2D projections; thus, the construction of a canonical representation with a fixed rotation angle is required.
Automated triage of COVID-19 from various lung abnormalities using chest CT features
The outbreak of COVID-19 has lead to a global effort to decelerate the pandemic spread.
COVID-19 in CXR: from Detection and Severity Scoring to Patient Disease Monitoring
In this work, we estimate the severity of pneumonia in COVID-19 patients and conduct a longitudinal study of disease progression.
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.
Nodule2vec: a 3D Deep Learning System for Pulmonary Nodule Retrieval Using Semantic Representation
Content-based retrieval supports a radiologist decision making process by presenting the doctor the most similar cases from the database containing both historical diagnosis and further disease development history.
Semi-supervised lung nodule retrieval
However, in a previous study, we have shown that binary auxiliary tasks are inferior to the usage of a rough similarity estimate that are derived from data annotations.
Joint Liver Lesion Segmentation and Classification via Transfer Learning
In this work, we study the combination of these two approaches for the problem of liver lesion segmentation and classification.
Coronavirus Detection and Analysis on Chest CT with Deep Learning
The outbreak of the novel coronavirus, officially declared a global pandemic, has a severe impact on our daily lives.
Bone Structures Extraction and Enhancement in Chest Radiographs via CNN Trained on Synthetic Data
Using HU based segmentation of bone structures in the CT domain, a synthetic 2D "Bone x-ray" DRR is produced and used for training the network.
Rapid AI Development Cycle for the Coronavirus (COVID-19) Pandemic: Initial Results for Automated Detection & Patient Monitoring using Deep Learning CT Image Analysis
We conducted multiple retrospective experiments to analyze the performance of the system in the detection of suspected COVID-19 thoracic CT features and to evaluate evolution of the disease in each patient over time using a 3D volume review, generating a Corona score.
A Soft STAPLE Algorithm Combined with Anatomical Knowledge
Supervised machine learning algorithms, especially in the medical domain, are affected by considerable ambiguity in expert markings.
Automatic Segmentation of Muscle Tissue and Inter-muscular Fat in Thigh and Calf MRI Images
Magnetic resonance imaging (MRI) of thigh and calf muscles is one of the most effective techniques for estimating fat infiltration into muscular dystrophies.
Endotracheal Tube Detection and Segmentation in Chest Radiographs using Synthetic Data
Chest radiographs are frequently used to verify the correct intubation of patients in the emergency room.
Deep Feature Learning from a Hospital-Scale Chest X-ray Dataset with Application to TB Detection on a Small-Scale Dataset
The recent emergence of a large Chest X-ray dataset opened the possibility for learning features that are specific to the X-ray analysis task.
Soft labeling by Distilling Anatomical knowledge for Improved MS Lesion Segmentation
Detection and segmentation of MS lesions is a complex task largely due to the extreme unbalanced data, with very small number of lesion pixels that can be used for training.
The Liver Tumor Segmentation Benchmark (LiTS)
In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018.
Improving CNN Training using Disentanglement for Liver Lesion Classification in CT
Training data is the key component in designing algorithms for medical image analysis and in many cases it is the main bottleneck in achieving good results.
An Adversarial Learning Approach to Medical Image Synthesis for Lesion Detection
Being able to provide a "normal" counterpart to a medical image can provide useful side information for medical imaging tasks like lesion segmentation or classification validated by our experiments.
Lung Structures Enhancement in Chest Radiographs via CT based FCNN Training
Two 2D FCNN architectures were trained to accomplish the task: The first performs 2D lung segmentation which is used for normalization of the lung area.
Improving the Segmentation of Anatomical Structures in Chest Radiographs using U-Net with an ImageNet Pre-trained Encoder
Accurate segmentation of anatomical structures in chest radiographs is essential for many computer-aided diagnosis tasks.
A Mixture of Views Network with Applications to the Classification of Breast Microcalcifications
We present a decision concept which explicitly takes into account the input multi-view structure, where for each case there is a different subset of relevant views.
GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classification
Then we present a novel scheme for liver lesion classification using CNN.
Cross-Modality Synthesis from CT to PET using FCN and GAN Networks for Improved Automated Lesion Detection
Quantitative evaluation was conducted using an existing lesion detection software, combining the synthesized PET as a false positive reduction layer for the detection of malignant lesions in the liver.
Synthetic Data Augmentation using GAN for Improved Liver Lesion Classification
In this paper, we present a data augmentation method that generates synthetic medical images using Generative Adversarial Networks (GANs).
Anatomical Data Augmentation For CNN based Pixel-wise Classification
In this work we propose a method for anatomical data augmentation that is based on using slices of computed tomography (CT) examinations that are adjacent to labeled slices as another resource of labeled data for training the network.
Virtual PET Images from CT Data Using Deep Convolutional Networks: Initial Results
In this work we present a novel system for PET estimation using CT scans.
Modeling the Intra-class Variability for Liver Lesion Detection using a Multi-class Patch-based CNN
Automatic detection of liver lesions in CT images poses a great challenge for researchers.
