Generative Adversarial Network Based Synthetic Learning and a Novel Domain Relevant Loss Term for Spine Radiographs

Problem: There is a lack of big data for the training of deep learning models in medicine, characterized by the time cost of data collection and privacy concerns. Generative adversarial networks (GANs) offer both the potential to generate new data, as well as to use this newly generated data, without inclusion of patients' real data, for downstream applications. Approach: A series of GANs were trained and applied for a downstream computer vision spine radiograph abnormality classification task. Separate classifiers were trained with either access or no access to the original imaging. Trained GANs included a conditional StyleGAN2 with adaptive discriminator augmentation, a conditional StyleGAN2 with adaptive discriminator augmentation to generate spine radiographs conditional on lesion type, and using a novel clinical loss term for the generator a StyleGAN2 with adaptive discriminator augmentation conditional on abnormality (SpineGAN). Finally, a differential privacy imposed StyleGAN2 with adaptive discriminator augmentation conditional on abnormality was trained and an ablation study was performed on its differential privacy impositions. Key Results: We accomplish GAN generation of synthetic spine radiographs without meaningful input for the first time from a literature review. We further demonstrate the success of synthetic learning for the spine domain with a downstream clinical classification task (AUC of 0.830 using synthetic data compared to AUC of 0.886 using the real data). Importantly, the introduction of a new clinical loss term for the generator was found to increase generation recall as well as accelerate model training. Lastly, we demonstrate that, in a limited size medical dataset, differential privacy impositions severely impede GAN training, finding that this is specifically due to the requirement for gradient perturbation with noise.

Understanding Transfer Learning for Chest Radiograph Clinical Report Generation with Modified Transformer Architectures

The image captioning task is increasingly prevalent in artificial intelligence applications for medicine. One important application is clinical report generation from chest radiographs. The clinical writing of unstructured reports is time consuming and error-prone. An automated system would improve standardization, error reduction, time consumption, and medical accessibility. In this paper we demonstrate the importance of domain specific pre-training and propose a modified transformer architecture for the medical image captioning task. To accomplish this, we train a series of modified transformers to generate clinical reports from chest radiograph image input. These modified transformers include: a meshed-memory augmented transformer architecture with visual extractor using ImageNet pre-trained weights, a meshed-memory augmented transformer architecture with visual extractor using CheXpert pre-trained weights, and a meshed-memory augmented transformer whose encoder is passed the concatenated embeddings using both ImageNet pre-trained weights and CheXpert pre-trained weights. We use BLEU(1-4), ROUGE-L, CIDEr, and the clinical CheXbert F1 scores to validate our models and demonstrate competitive scores with state of the art models. We provide evidence that ImageNet pre-training is ill-suited for the medical image captioning task, especially for less frequent conditions (eg: enlarged cardiomediastinum, lung lesion, pneumothorax). Furthermore, we demonstrate that the double feature model improves performance for specific medical conditions (edema, consolidation, pneumothorax, support devices) and overall CheXbert F1 score, and should be further developed in future work. Such a double feature model, including both ImageNet pre-training as well as domain specific pre-training, could be used in a wide range of image captioning models in medicine.