Brain-Like Replay Naturally Emerges in Reinforcement Learning Agents

Can replay, as a widely observed neural activity pattern in brain regions, particularly in the hippocampus and neocortex, emerge in an artificial agent? If yes, does it contribute to the tasks? In this work, without heavy dependence on complex assumptions, we discover naturally emergent replay under task-optimized paradigm using a recurrent neural network-based reinforcement learning model, which mimics the hippocampus and prefrontal cortex, as well as their intercommunication and the sensory cortex input. The emergent replay in the hippocampus, which results from the episodic memory and cognitive map as well as environment observations, well resembles animal experimental data and serves as an effective indicator of high task performance. The model also successfully reproduces local and nonlocal replay, which matches the human experimental data. Our work provides a new avenue for understanding the mechanisms behind replay.

Continual Learning for Abdominal Multi-Organ and Tumor Segmentation

The ability to dynamically extend a model to new data and classes is critical for multiple organ and tumor segmentation. However, due to privacy regulations, accessing previous data and annotations can be problematic in the medical domain. This poses a significant barrier to preserving the high segmentation accuracy of the old classes when learning from new classes because of the catastrophic forgetting problem. In this paper, we first empirically demonstrate that simply using high-quality pseudo labels can fairly mitigate this problem in the setting of organ segmentation. Furthermore, we put forward an innovative architecture designed specifically for continuous organ and tumor segmentation, which incurs minimal computational overhead. Our proposed design involves replacing the conventional output layer with a suite of lightweight, class-specific heads, thereby offering the flexibility to accommodate newly emerging classes. These heads enable independent predictions for newly introduced and previously learned classes, effectively minimizing the impact of new classes on old ones during the course of continual learning. We further propose incorporating Contrastive Language-Image Pretraining (CLIP) embeddings into the organ-specific heads. These embeddings encapsulate the semantic information of each class, informed by extensive image-text co-training. The proposed method is evaluated on both in-house and public abdominal CT datasets under organ and tumor segmentation tasks. Empirical results suggest that the proposed design improves the segmentation performance of a baseline neural network on newly-introduced and previously-learned classes along the learning trajectory.

ProGroTrack: Deep Learning-Assisted Tracking of Intracellular Protein Growth Dynamics

Accurate tracking of cellular and subcellular structures, along with their dynamics, plays a pivotal role in understanding the underlying mechanisms of biological systems. This paper presents a novel approach, ProGroTrack, that combines the You Only Look Once (YOLO) and ByteTrack algorithms within the detection-based tracking (DBT) framework to track intracellular protein nanostructures. Focusing on iPAK4 protein fibers as a representative case study, we conducted a comprehensive evaluation of YOLOv5 and YOLOv8 models, revealing the superior performance of YOLOv5 on our dataset. Notably, YOLOv5x achieved an impressive mAP50 of 0.839 and F-score of 0.819. To further optimize detection capabilities, we incorporated semi-supervised learning for model improvement, resulting in enhanced performances in all metrics. Subsequently, we successfully applied our approach to track the growth behavior of iPAK4 protein fibers, revealing their two distinct growth phases consistent with a previously reported kinetic model. This research showcases the promising potential of our approach, extending beyond iPAK4 fibers. It also offers a significant advancement in precise tracking of dynamic processes in live cells, and fostering new avenues for biomedical research.