PainNet: Statistical Relation Network with Episode-Based Training for Pain Estimation
This work addresses a domain-specific problem in healthcare for pain assessment from videos, offering an incremental improvement over existing methods.
The paper tackles the problem of estimating sequence-level pain from facial expression videos, which is clinically relevant but understudied, and introduces PainNet, a Statistical Relation Network that uses an embedding module with a statistical layer and episode-based training to achieve state-of-the-art results on self-reported pain estimation.
Despite the span in estimating pain from facial expressions, limited works have focused on estimating the sequence-level pain, which is reported by patients and used commonly in clinics. In this paper, we introduce a novel Statistical Relation Network, referred to as PainNet, designed for the estimation of the sequence-level pain. PainNet employs two key modules, the embedding and the relation modules, for comparing pairs of pain videos, and producing relation scores indicating if each pair belongs to the same pain category or not. At the core of the embedding module is a statistical layer mounted on the top of a RNN for extracting compact video-level features. The statistical layer is implemented as part of the deep architecture. Doing so, allows combining multiple training stages used in previous research, into a single end-to-end training stage. PainNet is trained using the episode-based training scheme, which involves comparing a query video with a set of videos representing the different pain categories. Experimental results show the benefit of using the statistical layer and the episode-based training in the proposed model. Furthermore, PainNet outperforms the state-of-the-art results on self-reported pain estimation.