Anatomical Landmark Detection
11 papers with code • 0 benchmarks • 0 datasets
Benchmarks
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Libraries
Use these libraries to find Anatomical Landmark Detection models and implementationsMost implemented papers
X-ray-transform Invariant Anatomical Landmark Detection for Pelvic Trauma Surgery
In this work, we present a method to automatically detect anatomical landmarks in X-ray images independent of the viewing direction.
DeepDRR -- A Catalyst for Machine Learning in Fluoroscopy-guided Procedures
Machine learning-based approaches outperform competing methods in most disciplines relevant to diagnostic radiology.
Inverse-Consistent Deep Networks for Unsupervised Deformable Image Registration
Deformable image registration is a fundamental task in medical image analysis, aiming to establish a dense and non-linear correspondence between a pair of images.
You Only Learn Once: Universal Anatomical Landmark Detection
miracle-center/yolo_universal_anatomical_landmark_detection
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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.
Volumetric landmark detection with a multi-scale shift equivariant neural network
Deep neural networks yield promising results in a wide range of computer vision applications, including landmark detection.
Volumetric landmark detection with a multi-scale translation equivariant neural network
tym002/landmark_detection
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IEEE 17th International Symposium on Biomedical Imaging (ISBI) 2020
Deep neural networks yield promising results in a wide range of computer vision applications, including landmark detection.
Feature Aggregation and Refinement Network for 2D AnatomicalLandmark Detection
In this paper, we propose a novel deep network, named feature aggregation and refinement network (FARNet), for the automatic detection of anatomical landmarks.
Point detection through multi-instance deep heatmap regression for sutures in endoscopy
cardio-ai/suture-detection-pytorch
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Method: In this work, we formulate the suture detection task as a multi-instance deep heatmap regression problem, to identify entry and exit points of sutures.
Unsupervised Domain Adaptation for Anatomical Landmark Detection
jhb86253817/uda_med_landmark
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The framework leverages self-training and domain adversarial learning to address the domain gap during adaptation.
FM-OSD: Foundation Model-Enabled One-Shot Detection of Anatomical Landmarks
By using solely a single template image, our method demonstrates significant superiority over strong state-of-the-art one-shot landmark detection methods.
