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 original classifier, the ISNet does not cause any increment in computational cost at run-time. We implement an ISNet based on a DenseNet121 classifier to solve the task of COVID-19 detection in chest X-rays. The ISNet precisely ignored the image regions outside of the lungs;it achieved 94.5 +/-4.1% mean accuracy with an external test database, surpassing the performances of state-of-the-art DNNs (U-Net segmenter followed by DenseNet121 and standalone DenseNet121) by 6 to 7.9%. ISNet presents an accurate, fast, and light methodology to perform classification preceded by segmentation.
ISNet: Costless and Implicit Image Segmentation for Deep Classifiers, with Application in COVID-19 Detection (preprint)