Vision Transformer for femur fracture classification
  In recent years, the scientific community has focused on the development of
CAD tools that could improve bone fractures' classification, mostly based on
Convolutional Neural Network (CNN). However, the discerning accuracy of
fractures' subtypes was far from optimal. This paper proposes a modified
version of a very recent and powerful deep learning technique, the Vision
Transformer (ViT), outperforming CNNs based approaches and consequently
increasing specialists' diagnosis accuracy. 4207 manually annotated images were
used and distributed, by following the AO/OTA classification, in different
fracture types, the largest labeled dataset of proximal femur fractures used in
literature. The ViT architecture was used and compared with a classic CNN and a
multistage architecture composed of successive CNNs in cascade. To demonstrate
the reliability of this approach, 1) the attention maps were used to visualize
the most relevant areas of the images, 2) the performance of a generic CNN and
ViT was compared through unsupervised learning techniques, and 3) 11
specialists were asked to evaluate and classify 150 proximal femur fractures'
images with and without the help of the ViT, then results were compared for
potential improvement. The ViT was able to correctly predict 83% of the test
images. Precision, recall and F1-score were 0.77 (CI 0.64-0.90), 0.76 (CI
0.62-0.91) and 0.77 (CI 0.64-0.89), respectively. The average specialists'
diagnostic improvement was 29% when supported by ViT's predictions,
outperforming the algorithm alone. This paper showed the potential of Vision
Transformers in bone fracture classification. For the first time, good results
were obtained in sub-fractures classification, with the largest and richest
dataset ever. Accordingly, the assisted diagnosis yielded the best results,
proving once again the effectiveness of a coordinated work between neural
networks and specialists.
