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Two-dimensional shear wave elastography (2D-SWE) is an effective and feasible method for plantar fasciitis (PF) evaluation. Until now, only experienced doctors have been able to give relatively accurate evaluation via ultrasound images, resulting in low efficiency and high cost. Therefore, designing automatic algorithms to recognize the pattern of these ultrasound images is urgently required. In recent years, deep learning (DL) has made considerable progress in computer-aided diagnosis (CAD). However, there have been no studies that apply DL to the diagnosis of PF. To achieve robust PF classification, this paper builds a deep Siamese framework with multitask learning and transfer learning (DS-MLTL), which learns discriminative visual features and effective recognition functions using 2D-SWE. The DS-MLTL model comprises two VGG-style branches and a multitask loss including a classification loss and a Siamese loss. The Siamese loss leverages the intrinsic structure (similarities) of different images and contains a contrastive constraint and a similar constraint. In our framework, visual features and the multitask loss are learned jointly, and they can benefit from each other. To train the DS-MLTL model effectively, the model transfers knowledge from the large-scale ImageNet dataset to the PF classification task. For model evaluation, an SWE dataset of plantar fascia, which contains 282 images of a PF pattern and 60 images of a healthy pattern, is collected. Experimental results show that the DS-MLTL method achieves favorable accuracy of 85.09 ± 6.67% and performs better than human-crafted features extracted from B-mode ultrasound and SWE. In addition, DS-MLTL also obtains the best performance compared with different DL models.

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