  Spinal metastasis is the most common disease in bone metastasis and may cause
pain, instability and neurological injuries. Early detection of spinal
metastasis is critical for accurate staging and optimal treatment. The
diagnosis is usually facilitated with Computed Tomography (CT) scans, which
requires considerable efforts from well-trained radiologists. In this paper, we
explore a learning-based automatic bone quality classification method for
spinal metastasis based on CT images. We simultaneously take the posterolateral
spine involvement classification task into account, and employ multi-task
learning (MTL) technique to improve the performance. MTL acts as a form of
inductive bias which helps the model generalize better on each task by sharing
representations between related tasks. Based on the prior knowledge that the
mixed type can be viewed as both blastic and lytic, we model the task of bone
quality classification as two binary classification sub-tasks, i.e., whether
blastic and whether lytic, and leverage a multiple layer perceptron to combine
their predictions. In order to make the model more robust and generalize
better, self-paced learning is adopted to gradually involve from easy to more
complex samples into the training process. The proposed learning-based method
is evaluated on a proprietary spinal metastasis CT dataset. At slice level, our
method significantly outperforms an 121-layer DenseNet classifier in
sensitivities by $+12.54\%$, $+7.23\%$ and $+29.06\%$ for blastic, mixed and
lytic lesions, respectively, meanwhile $+12.33\%$, $+23.21\%$ and $+34.25\%$ at
vertebrae level.
