BS-Net is an architecture for COVID-19 severity prediction based on clinical data from different modalities. The architecture comprises 1) a shared multi-task feature extraction backbone, 2) a lung segmentation branch, 3) an original registration mechanism that acts as a ”multi-resolution feature alignment” block operating on the encoding backbone , and 4) a multi-regional classification part for the final six-valued score estimation.
All these blocks act together in the final training thanks to a loss specifically crated for this task. This loss guarantees also performance robustness, comprising a differentiable version of the target discrete metric. The learning phase operates in a weakly-supervised fashion. This is due to the fact that difficulties and pitfalls in the visual interpretation of the disease signs on CXRs (spanning from subtle findings to heavy lung impairment), and the lack of detailed localization information, produces unavoidable inter-rater variability among radiologists in assigning scores.
Specifically the architectural details are:
Paper | Code | Results | Date | Stars |
---|