Bayesian Random Semantic Data Augmentation for Medical Image Classification

Data augmentation is a critical regularization technique for deep neural networks, particularly in medical image classification. Popular data augmentation approaches include image transformation-based methods, generative data augmentation, and automatic data augmentation. However, these approaches encounter notable limitations: image transformation-based and automated data augmentation techniques cannot implement semantic transformations, leading to a constrained variety of augmented samples, and generative data augmentation methods are computationally expensive. In response to these challenges, we proposed Bayesian Random Semantic Data Augmentation (BRSDA), a novel, efficient, and plug-and-play semantic data augmentation method. BRSDA is motivated by a simple translation in the feature space along specific directions that can effectuate semantic transformations. When given a feature, we define its augmentable semantic magnitude as a random variable and estimate its distribution using variational Bayesian, then sample semantic magnitude and add to the randomly selected semantic direction to achieve semantic data augmentation. We demonstrate the effectiveness of BRSDA on five 2D and six 3D medical image datasets covering nine modalities. We also test BRSDA with mainstream neural network architectures, showcasing its robustness. Furthermore, combining BRSDA with other leading data augmentation methods achieves superior performance. Code is available online at \url{https://github.com/YaoyaoZhu19/BRSDA}.

Towards Safe Propofol Dosing during General Anesthesia Using Deep Offline Reinforcement Learning

Automated anesthesia promises to enable more precise and personalized anesthetic administration and free anesthesiologists from repetitive tasks, allowing them to focus on the most critical aspects of a patient's surgical care. Current research has typically focused on creating simulated environments from which agents can learn. These approaches have demonstrated good experimental results, but are still far from clinical application. In this paper, Policy Constraint Q-Learning (PCQL), a data-driven reinforcement learning algorithm for solving the problem of learning anesthesia strategies on real clinical datasets, is proposed. Conservative Q-Learning was first introduced to alleviate the problem of Q function overestimation in an offline context. A policy constraint term is added to agent training to keep the policy distribution of the agent and the anesthesiologist consistent to ensure safer decisions made by the agent in anesthesia scenarios. The effectiveness of PCQL was validated by extensive experiments on a real clinical anesthesia dataset. Experimental results show that PCQL is predicted to achieve higher gains than the baseline approach while maintaining good agreement with the reference dose given by the anesthesiologist, using less total dose, and being more responsive to the patient's vital signs. In addition, the confidence intervals of the agent were investigated, which were able to cover most of the clinical decisions of the anesthesiologist. Finally, an interpretable method, SHAP, was used to analyze the contributing components of the model predictions to increase the transparency of the model.

Constraining Multi-scale Pairwise Features between Encoder and Decoder Using Contrastive Learning for Unpaired Image-to-Image Translation

Contrastive learning (CL) has shown great potential in image-to-image translation (I2I). Current CL-based I2I methods usually re-exploit the encoder of the generator to maximize the mutual information between the input and generated images, which does not exert an active effect on the decoder part. In addition, though negative samples play a crucial role in CL, most existing methods adopt a random sampling strategy, which may be less effective. In this paper, we rethink the CL paradigm in the unpaired I2I tasks from two perspectives and propose a new one-sided image translation framework called EnCo. First, we present an explicit constraint on the multi-scale pairwise features between the encoder and decoder of the generator to guarantee the semantic consistency of the input and generated images. Second, we propose a discriminative attention-guided negative sampling strategy to replace the random negative sampling, which significantly improves the performance of the generative model with an almost negligible computational overhead. Compared with existing methods, EnCo acts more effective and efficient. Extensive experiments on several popular I2I datasets demonstrate the effectiveness and advantages of our proposed approach, and we achieve several state-of-the-art compared to previous methods.