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Found 14 papers, 5 papers with code
Deep learning automates Cobb angle measurement compared with multi-expert observers
To address these challenges and the lack of interpretability found in certain existing automated methods, we have created fully automated software that not only precisely measures the Cobb angle but also provides clear visualizations of these measurements.
Causal Mediation Analysis with Multi-dimensional and Indirectly Observed Mediators
This is important in many scientific applications to identify the underlying mechanisms of a treatment effect.
Domain Adaptation via Rebalanced Sub-domain Alignment
To address this limitation, we propose a novel generalization bound that reweights source classification error by aligning source and target sub-domains.
Estimating Causal Effects using a Multi-task Deep Ensemble
A number of methods have been proposed for causal effect estimation, yet few have demonstrated efficacy in handling data with complex structures, such as images.
Enabling Augmented Segmentation and Registration in Ultrasound-Guided Spinal Surgery via Realistic Ultrasound Synthesis from Diagnostic CT Volume
While the US is not a standard paradigm for spinal surgery, the scarcity of intra-operative clinical US data is an insurmountable bottleneck in training a neural network.
Planning-oriented Autonomous Driving
Oriented at this, we revisit the key components within perception and prediction, and prioritize the tasks such that all these tasks contribute to planning.
Knee arthritis severity measurement using deep learning: a publicly available algorithm with a multi-institutional validation showing radiologist-level performance
We propose a novel deep learning-based five-step algorithm to automatically grade KOA from posterior-anterior (PA) views of radiographs: (1) image preprocessing (2) localization of knees joints in the image using the YOLO v3-Tiny model, (3) initial assessment of the severity of osteoarthritis using a convolutional neural network-based classifier, (4) segmentation of the joints and calculation of the joint space narrowing (JSN), and (5), a combination of the JSN and the initial assessment to determine a final Kellgren-Lawrence (KL) score.
Image-Guided Navigation of a Robotic Ultrasound Probe for Autonomous Spinal Sonography Using a Shadow-aware Dual-Agent Framework
Ultrasound (US) imaging is commonly used to assist in the diagnosis and interventions of spine diseases, while the standardized US acquisitions performed by manually operating the probe require substantial experience and training of sonographers.
Human-Aware Robot Navigation via Reinforcement Learning with Hindsight Experience Replay and Curriculum Learning
In recent years, the growing demand for more intelligent service robots is pushing the development of mobile robot navigation algorithms to allow safe and efficient operation in a dense crowd.
Automatic Recognition of Abdominal Organs in Ultrasound Images based on Deep Neural Networks and K-Nearest-Neighbor Classification
In order to shorten the examination time and reduce the cognitive burden on the sonographers, we present a classification method that combines the deep learning techniques and k-Nearest-Neighbor (k-NN) classification to automatically recognize various abdominal organs in the ultrasound images in real time.
Autonomous Navigation of an Ultrasound Probe Towards Standard Scan Planes with Deep Reinforcement Learning
Furthermore, we propose a confidence-based approach to encode the optimization of image quality in the learning process.
Learning Mobile Robot Navigation in the Dense Crowd with Deep Reinforcement Learning
In recent years, the growing demand for more intelligent service robots is pushing the development of mobile robot navigation algorithms.
SARL*: Deep Reinforcement Learning based Human-Aware Navigation for Mobile Robot in Indoor Environments
In a human-robot coexisting environment, reaching the goal position safely and efficiently is essential for a mobile service robot.
Optimize transfer learning for lung diseases in bronchoscopy using a new concept: sequential fine-tuning
To help doctors to be more selective on biopsies and provide a second opinion on diagnosis, in this work, we propose a computer-aided diagnosis (CAD) system for lung diseases including cancers and tuberculosis (TB).
