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Computer-aided diagnosis systems (CADs) present valuable second opinions to radiologists in diagnosis. Many studies on thyroid nodules have proposed various CADs to provide a binary result, benignity or malignancy, for doctors, ignoring interpretability of more ultrasonic features that could be more useful. We develop an interpretable CADs (iCADS) that utilizes deep-learning networks' classification power and interpretability potential of clinical guidelines, like TIRADS, a well-established scale for thyroid nodules. iCADS incorporates a main neural-networks model and six neural-network based interpreters. The outputs of the six interpreters are compared with TIRADS guidelines and the matched result will form a report, more than a benignity or malignancy result, for radiologists. Clinical images of 16,946 thyroid nodules from 5,885 patients were used to train the proposed iCADS. An extra experimental data set containing 501 images were used to test the performance of the model. For better illustrating the assistant ability of iCADS, we also recruited ten junior radiologists to make diagnosis decisions with or without the help of different versions of iCADS. The experiments demonstrated that iCADS can largely improve junior radiologists diagnosis with the help of interpreter strategy. These experiments are also the very first attempt to evaluate the effect of interpretability of deep-learning based CADs in clinical practice. Comparison experiments with other deep-learning based CADs and traditional CADs indicated that the interpreter strategy can easily be combined to other intelligent CADs without the loss of performance. The framework of iCADS can also inspire more research on the development of CADs.

A Novel Interpretable Computer-Aided Diagnosis System of Thyroid Nodules on Ultrasound Based on Clinical Experience