  In recent years, explainable methods for artificial intelligence (XAI) have
tried to reveal and describe models' decision mechanisms in the case of
classification tasks. However, XAI for semantic segmentation and in particular
for single instances has been little studied to date. Understanding the process
underlying automatic segmentation of single instances is crucial to reveal what
information was used to detect and segment a given object of interest. In this
study, we proposed two instance-level explanation maps for semantic
segmentation based on SmoothGrad and Grad-CAM++ methods. Then, we investigated
their relevance for the detection and segmentation of white matter lesions
(WML), a magnetic resonance imaging (MRI) biomarker in multiple sclerosis (MS).
687 patients diagnosed with MS for a total of 4043 FLAIR and MPRAGE MRI scans
were collected at the University Hospital of Basel, Switzerland. Data were
randomly split into training, validation and test sets to train a 3D U-Net for
MS lesion segmentation. We observed 3050 true positive (TP), 1818 false
positive (FP), and 789 false negative (FN) cases. We generated instance-level
explanation maps for semantic segmentation, by developing two XAI methods based
on SmoothGrad and Grad-CAM++. We investigated: 1) the distribution of gradients
in saliency maps with respect to both input MRI sequences; 2) the model's
response in the case of synthetic lesions; 3) the amount of perilesional tissue
needed by the model to segment a lesion. Saliency maps (based on SmoothGrad) in
FLAIR showed positive values inside a lesion and negative in its neighborhood.
Peak values of saliency maps generated for these four groups of volumes
presented distributions that differ significantly from one another, suggesting
a quantitative nature of the proposed saliency. Contextual information of 7mm
around the lesion border was required for their segmentation.
