Adjustable Robust Transformer for High Myopia Screening in Optical
  Coherence Tomography
Xiao Ma Zetian Zhang Zexuan Ji Kun Huang Na Su Songtao Yuan Qiang Chen
Myopia is a manifestation of visual impairment caused by an excessively
elongated eyeball. Image data is critical material for studying high myopia and
pathological myopia. Measurements of spherical equivalent and axial length are
the gold standards for identifying high myopia, but the available image data
for matching them is scarce. In addition, the criteria for defining high myopia
vary from study to study, and therefore the inclusion of samples in automated
screening efforts requires an appropriate assessment of interpretability. In
this work, we propose a model called adjustable robust transformer (ARTran) for
high myopia screening of optical coherence tomography (OCT) data. Based on
vision transformer, we propose anisotropic patch embedding (APE) to capture
more discriminative features of high myopia. To make the model effective under
variable screening conditions, we propose an adjustable class embedding (ACE)
to replace the fixed class token, which changes the output to adapt to
different conditions. Considering the confusion of the data at high myopia and
low myopia threshold, we introduce the label noise learning strategy and
propose a shifted subspace transition matrix (SST) to enhance the robustness of
the model. Besides, combining the two structures proposed above, the model can
provide evidence for uncertainty evaluation. The experimental results
demonstrate the effectiveness and reliability of the proposed method. Code is
available at: https://github.com/maxiao0234/ARTran.