Several paradigms have been proposed to resolve the domain shift. An intuitive solution is directly mixing the heterogeneous images from multiple cohorts to make the training data adequate. However, the model’s prediction capability could not be explicitly improved, in contrast, would be limited by overfitting when distribution heterogeneity is significant [19-20]. Another common practice is to pre-train the model in the source domain, then fine-tune it in the target domain. It generally requires sufficient labeled data from the target domain to tune massive network parameters manually, which is still labor-intensive. Domain adaptation (DA) has emerged as a more promising method, allowing effective knowledge transfer [18, 21] from the label-rich source domain to the target domain. Recently, unsupervised DA (UDA) methods have drawn increasing attention, accredited to their immunity of using target labels for training [22]. They can be generally categorized as image translation and feature alignment approaches. In the former one, the models can align image appearance [18, 23] by translating images from one domain to another using generative models, such as generative adversarial networks (GAN) [24]. Difficulties mainly come from whole-slide image translation, and image synthesis due to insufficient similarity of images. Besides, these models usually focus on low-level feature extraction, suffering from inconspicuous lesion texture and characteristics [25]. In contrast, the latter one, feature alignment-based models, could be more effective in resolving domain shift by extracting domain-invariant features, either minimizing correlation distance between domains [26], or assimilating feature distributions through adversarial learning [27]. Yet, very few of them are dedicated to prostate lesion detection and/or classification, particularly using mpMRI. Therefore, an effective UDA model for fully automated mpMRI-based PLDC is highly desirable in prior to its invasive biopsy, if necessary.
In this work, we develop Coarse Mask-guided Deep Domain Adaptation
Network (CMD²A-Net) for both coarse prostate lesions detection and
lesion malignancy classification. Besides,
we also extend the proposed
network to an open-sourced system. This executable end-to-end system
takes mpMRI sequences as input, and outputs coarse lesion contours as
well as lesion malignancy. The system can also be downloaded online. Our
work contributions can be summarized below:
(1) Development of a deep-learning-based system for fully automated prostate lesion assessment. Our end-to-end system is dedicated to PLDC on multi-cohort mpMRI without the need of prior manual processing on mpMRI sequences.
(2) Design of a UDA model (i.e., CMD²A-Net) capable of leveraging cross-site representation transfer to realize accurate PLDC without requiring target labels, where weakly-supervised coarse lesion segmentation modules are incorporated, in order to extract informative lesion features, thus facilitating feature alignment between domains.
(3) Experimental evaluation of CMD²A-Net on one public dataset (i.e., PROSTATEx [13]) and three local cohort datasets, including lesion assessments with various mpMRI sequence inputs, comparisons with state-of-the-art models, as well as ablation study. The capability of transferring knowledge from PROSTATEx to our small-scale local cohort datasets is demonstrated over the state-of-the-art models.
Related Work
CNNs have been proved effective and widely applied for mpMRI-based PCa classification with promising performance. Wang and Wang [14]a attempted to explore optimal mpMRI sequence combinations as the CNN’s input, and their model achieved an AUC of 0.95, which was reported to outperform all models in the PROSTATEx Challenge. Rather than PCa classification only, Kiraly, et al. [28] developed a model with an encoder-decoder architecture to detect prostate lesions and simultaneously classify the lesion malignancy. However, these studies required manually-cropped regions of prostate, which would be time-consuming and expensive [23a, 29].