Vision Transformers for femur fracture classification

@article{Tanzi2021VisionTF,
  title={Vision Transformers for femur fracture classification},
  author={Leonardo Tanzi and Andrea Audisio and Giansalvo Cirrincione and Alessandro Aprato and Enrico Vezzetti},
  journal={Injury},
  year={2021}
}

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References

SHOWING 1-10 OF 45 REFERENCES

Rethinking the Inception Architecture for Computer Vision

This work is exploring ways to scale up networks in ways that aim at utilizing the added computation as efficiently as possible by suitably factorized convolutions and aggressive regularization.

An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Vision Transformer (ViT) attains excellent results compared to state-of-the-art convolutional networks while requiring substantially fewer computational resources to train.

Real-time deep learning semantic segmentation during intra-operative surgery for 3D augmented reality assistance

A two-steps automatic system to align a 3D virtual ad-hoc model of a patient’s organ with its 2D endoscopic image, to assist surgeons during the procedure, to improve the precision of a published work.

Classification of femur fracture in pelvic X-ray images using meta-learned deep neural network

An encoder-decoder structured neural network that utilizes radiology reports as ancillary information at training that achieves a favorable performance in a test dataset containing 227 cases demonstrates the potential for deep learning to improve performance and accelerate application of AI in clinical practice.

Intraoperative surgery room management: A deep learning perspective

The current study aimed to systematically review the literature addressing the use of deep learning (DL) methods in intraoperative surgery applications, focusing on the data collection, the

End-to-End Object Detection with Transformers

This work presents a new method that views object detection as a direct set prediction problem, and demonstrates accuracy and run-time performance on par with the well-established and highly-optimized Faster RCNN baseline on the challenging COCO object detection dataset.

X-Ray Bone Fracture Classification Using Deep Learning: A Baseline for Designing a Reliable Approach

This work analyzes and evaluates a selection of papers, chosen according to their representative approach, where the authors applied different deep learning techniques to classify bone fractures, in order to select the strengths of each of them and try to delineate a generalized strategy.

Deep CNN for 3D Face Recognition

A new approach to the problem consisting of a novel image representation, where specific facial descriptors replace the RGB traditional channels and a convolutional neural network performs the classification, is proposed.

Analysis on Detecting of Leg Bone Fracture from X-ray Images

  • W. W. MyintH. TunK. Tun
  • Materials Science
    International Journal of Scientific and Research Publications (IJSRP)
  • 2018