Graph-Ensemble Learning Model for Multi-label Skin Lesion Classification
  using Dermoscopy and Clinical Images
Peng Tang Yang Nan Tobias Lasser
Many skin lesion analysis (SLA) methods recently focused on developing a
multi-modal-based multi-label classification method due to two factors. The
first is multi-modal data, i.e., clinical and dermoscopy images, which can
provide complementary information to obtain more accurate results than
single-modal data. The second one is that multi-label classification, i.e.,
seven-point checklist (SPC) criteria as an auxiliary classification task can
not only boost the diagnostic accuracy of melanoma in the deep learning (DL)
pipeline but also provide more useful functions to the clinical doctor as it is
commonly used in clinical dermatologist's diagnosis. However, most methods only
focus on designing a better module for multi-modal data fusion; few methods
explore utilizing the label correlation between SPC and skin disease for
performance improvement. This study fills the gap that introduces a Graph
Convolution Network (GCN) to exploit prior co-occurrence between each category
as a correlation matrix into the DL model for the multi-label classification.
However, directly applying GCN degraded the performances in our experiments; we
attribute this to the weak generalization ability of GCN in the scenario of
insufficient statistical samples of medical data. We tackle this issue by
proposing a Graph-Ensemble Learning Model (GELN) that views the prediction from
GCN as complementary information of the predictions from the fusion model and
adaptively fuses them by a weighted averaging scheme, which can utilize the
valuable information from GCN while avoiding its negative influences as much as
possible. To evaluate our method, we conduct experiments on public datasets.
The results illustrate that our GELN can consistently improve the
classification performance on different datasets and that the proposed method
can achieve state-of-the-art performance in SPC and diagnosis classification.