arXiv:2102.11385
공식 코드 · Papers with Code
Lightweight Combinational Machine Learning Algorithm for Sorting Canine Torso Radiographs
원문 초록
The veterinary field lacks automation in contrast to the tremendous technological advances made in the human medical field. Implementation of machine learning technology can shorten any step of the automation process. This paper explores these core concepts and starts with automation in sorting radiographs for canines by view and anatomy. This is achieved by developing a new lightweight algorithm inspired by AlexNet, Inception, and SqueezeNet. The proposed module proves to be lighter than SqueezeNet while maintaining accuracy higher than that of AlexNet, ResNet, DenseNet, and SqueezeNet.
함께 살펴볼 논문
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Are Vision Foundation Models Ready for Out-of-the-Box Medical Image Registration?
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SV-DRR: High-Fidelity Novel View X-Ray Synthesis Using Diffusion Model
Toward Reliable AR-Guided Surgical Navigation: Interactive Deformation Modeling with Data-Driven Biomechanics and Prompts
Are Vision Language Models Ready for Clinical Diagnosis? A 3D Medical Benchmark for Tumor-centric Visual Question Answering
PolyPose: Localizing Deformable Anatomy in 3D from Sparse 2D X-ray Images using Polyrigid Transforms
토론
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