ISNet: Costless and Implicit Image Segmentation for Deep Classifiers, with Application in COVID-19 Detection

This work proposes a novel deep neural network (DNN) architecture, Implicit Segmentation Neural Network (ISNet), to solve the task of image segmentation followed by classification. It substitutes the common pipeline of two DNNs with a single model. We designed the ISNet for high flexibility and performance: it allows virtually any classification neural network architecture to analyze a common image as if it had been previously segmented. Furthermore, in relation to the unmodified classifier, the ISNet does not cause any increment in computational cost at run-time. We test the architecture with two applications: COVID-19 detection in chest X-rays, and facial attribute estimation. We implement an ISNet based on a DenseNet121 classifier, and compare the model to a U-net (performing lung/face segmentation) followed by a DenseNet121, and to a standalone DenseNet121. The new architecture matched the other DNNs in facial attribute estimation. Moreover, it strongly surpassed them in COVID-19 detection, according to an external test dataset. The ISNet precisely ignored the image regions outside of the lungs or faces. Therefore, in COVID-19 detection it reduced the effects of background bias and shortcut learning, and it improved security in facial attribute estimation. ISNet presents an accurate, fast, and light methodology. The successful implicit segmentation, considering two largely diverse fields, highlights the architecture's general applicability.