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Found 21 papers, 6 papers with code
Lumbar Bone Mineral Density Estimation from Chest X-ray Images: Anatomy-aware Attentive Multi-ROI Modeling
Osteoporosis is a common chronic metabolic bone disease often under-diagnosed and under-treated due to the limited access to bone mineral density (BMD) examinations, e. g. via Dual-energy X-ray Absorptiometry (DXA).
Coherence Learning using Keypoint-based Pooling Network for Accurately Assessing Radiographic Knee Osteoarthritis
In this work, we propose a computer-aided diagnosis approach to provide more accurate and consistent assessments of both composite and fine-grained OA grades simultaneously.
Scalable Semi-supervised Landmark Localization for X-ray Images using Few-shot Deep Adaptive Graph
It first trains a DAG model on the labeled data and then fine-tunes the pre-trained model on the unlabeled data with a teacher-student SSL mechanism.
Deep Implicit Statistical Shape Models for 3D Medical Image Delineation
DISSMs use a deep implicit surface representation to produce a compact and descriptive shape latent space that permits statistical models of anatomical variance.
Opportunistic Screening of Osteoporosis Using Plain Film Chest X-ray
In this paper, we propose a method to predict BMD from Chest X-ray (CXR), one of the most common, accessible, and low-cost medical image examinations.
Semi-Supervised Learning for Bone Mineral Density Estimation in Hip X-ray Images
Specifically, we propose a new semi-supervised self-training algorithm to train the BMD regression model using images coupled with DEXA measured BMDs and unlabeled images with pseudo BMDs.
Knowledge Distillation with Adaptive Asymmetric Label Sharpening for Semi-supervised Fracture Detection in Chest X-rays
Exploiting available medical records to train high performance computer-aided diagnosis (CAD) models via the semi-supervised learning (SSL) setting is emerging to tackle the prohibitively high labor costs involved in large-scale medical image annotations.
Automatic Vertebra Localization and Identification in CT by Spine Rectification and Anatomically-constrained Optimization
This paper proposes a robust and accurate method that effectively exploits the anatomical knowledge of the spine to facilitate vertebra localization and identification.
A New Window Loss Function for Bone Fracture Detection and Localization in X-ray Images with Point-based Annotation
Object detection methods are widely adopted for computer-aided diagnosis using medical images.
SAM: Self-supervised Learning of Pixel-wise Anatomical Embeddings in Radiological Images
We introduce such an approach, called Self-supervised Anatomical eMbedding (SAM).
Contour Transformer Network for One-shot Segmentation of Anatomical Structures
In this work, we present Contour Transformer Network (CTN), a one-shot anatomy segmentation method with a naturally built-in human-in-the-loop mechanism.
Deep Hiearchical Multi-Label Classification Applied to Chest X-Ray Abnormality Taxonomies
To this end, we present a deep HMLC approach for CXR CAD.
Learning to Segment Anatomical Structures Accurately from One Exemplar
Accurate segmentation of critical anatomical structures is at the core of medical image analysis.
Anatomy-Aware Siamese Network: Exploiting Semantic Asymmetry for Accurate Pelvic Fracture Detection in X-ray Images
A new contrastive feature learning component in our Siamese network is designed to optimize the deep image features being more salient corresponding to the underlying semantic asymmetries (caused by pelvic fracture occurrences).
Structured Landmark Detection via Topology-Adapting Deep Graph Learning
Image landmark detection aims to automatically identify the locations of predefined fiducial points.
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Unsupervised Learning of Landmarks based on Inter-Intra Subject Consistencies
To recover from the transformed images back to the original subject, the landmark detector is forced to learn spatial locations that contain the consistent semantic meanings both for the paired intra-subject images and between the paired inter-subject images.
Weakly Supervised Universal Fracture Detection in Pelvic X-rays
In this paper, we propose a two-stage hip and pelvic fracture detection method that executes localized fracture classification using weakly supervised ROI mining.
Task Driven Generative Modeling for Unsupervised Domain Adaptation: Application to X-ray Image Segmentation
In this paper, we propose a novel model framework for learning automatic X-ray image parsing from labeled CT scans.
Dilated FCN for Multi-Agent 2D/3D Medical Image Registration
Second, to handle various artifacts in 2D X-ray images, multiple local agents are employed efficiently via FCN-based structures, and an auto attention mechanism is proposed to favor the proposals from regions with more reliable visual cues.
An Artificial Agent for Robust Image Registration
The resulting registration approach inherently encodes both a data-driven matching metric and an optimal registration strategy (policy).
Real-time 2D/3D Registration via CNN Regression
In this paper, we present a Convolutional Neural Network (CNN) regression approach for real-time 2-D/3-D registration.
