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We present MultiCheXNet, an end-to-end Multi-task learning model, that isable to take advantage of different X-rays data sets of Pneumonia-like diseasesin one neural architecture, performing three tasks at the same time; diagnosis,segmentation and localization. The common encoder in our architecture cancapture useful common features present in the different tasks. The commonencoder has another advantage of efficient computations, which speeds up theinference time compared to separate models. The specialized decoders heads canthen capture the task-specific features. We employ teacher forcing to addressthe issue of negative samples that hurt the segmentation and localizationperformance. Finally,we employ transfer learning to fine tune the classifier onunseen pneumonia-like diseases. The MTL architecture can be trained on joint ordis-joint labeled data sets. The training of the architecture follows acarefully designed protocol, that pre trains different sub-models onspecialized datasets, before being integrated in the joint MTL model. Ourexperimental setup involves variety of data sets, where the baselineperformance of the 3 tasks is compared to the MTL architecture performance.Moreover, we evaluate the transfer learning mode to COVID-19 data set,both fromindividual classifier model, and from MTL architecture classification head.

MultiCheXNet: A Multi-Task Learning Deep Network For Pneumonia-like Diseases Diagnosis From X-ray Scans