Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Grading Loss: A Fracture Grade-based Metric Loss for Vertebral Fracture
  Detection   Osteoporotic vertebral fractures have a severe impact on patients' overall
well-being but are severely under-diagnosed. These fractures present themselves
at various levels of severity measured using the Genant's grading scale.
Insufficient annotated datasets, severe data-imbalance, and minor difference in
appearances between fractured and healthy vertebrae make naive classification
approaches result in poor discriminatory performance. Addressing this, we
propose a representation learning-inspired approach for automated vertebral
fracture detection, aimed at learning latent representations efficient for
fracture detection. Building on state-of-art metric losses, we present a novel
Grading Loss for learning representations that respect Genant's fracture
grading scheme. On a publicly available spine dataset, the proposed loss
function achieves a fracture detection F1 score of 81.5%, a 10% increase over a
naive classification baseline.

5
The categories of this paper are: gr-qc
23
The categories of this paper are: solv-int
22
The categories of this paper are: chao-dyn
3
The categories of this paper are: physics
35
The categories of this paper are: mtrl-th
28
The categories of this paper are: alg-geom
8
The categories of this paper are: hep-ex
29
The categories of this paper are: funct-an
26
The categories of this paper are: bayes-an
7
The categories of this paper are: hep-th
14
The categories of this paper are: math-ph
12
The categories of this paper are: nucl-th
33
The categories of this paper are: plasm-ph
24
The categories of this paper are: dg-ga
0
The categories of this paper are: hep-ph
32
The categories of this paper are: atom-ph