Yuanqiong Chen

The Chinese giant salamander (Andrias davidianus), a species of high ecological and conservation value, shows abnormal respiratory behaviors as early signs of health decline. Accurate assessment of its pulmonary respiration is crucial for improving captive breeding and post-breeding parental care—key strategies for its survival and population recovery. However, its nocturnal and cave-dwelling nature makes traditional observation extremely difficult. Manual monitoring suffers from poor visibility at night, while conventional detection methods often miss subtle respiratory movements, limiting behavioral and health research. To address these challenges, this study presents the first automated method for monitoring respiratory behaviors in this species. We propose Mamba-YOLO-SRC, a novel hybrid detection framework that combines Mamba and YOLO architectures to accurately identify four key behaviors: diving (Dive), head-raising (HeadUP), inhalation (Inhale), and exhalation (Exhale). The proposed model achieves a mean average precision (mAP@0.5) of 0.944, with per-class average precision scores of 0.975 for Dive, 0.925 for HeadUP, 0.948 for Exhale, and 0.928 for Inhale. Mamba-YOLO-SRC provides a feasible and referable technical solution for advancing research on the Chinese giant salamander in both captive and natural settings.

The Chinese giant salamander (Andrias davidianus) is a nationally protected species in China, and its respiratory behavior serves as a key indicator of its physiological state, health status, and biological rhythm. However, research on intelligent monitoring of its respiratory behavior remains limited due to several challenges, including the species’ nocturnal habits, resulting in low image contrast and poor quality in dark environments; extremely subtle breathing movements; and high-cost manual annotation, leading to a scarcity of high-quality annotated visual data. These factors severely constrain the application of deep learning techniques in this field. To support research on respiratory behavior monitoring in the Chinese giant salamander, this study constructs and releases the CGS-BR dataset, which is the first vision-based dataset dedicated specifically to respiratory behavior detection in this species. The dataset was collected under controlled simulated breeding conditions and consists of 1732 images extracted from 215 high-definition video clips. Following a standardized procedure, each complete respiratory cycle is manually annotated into four stages: head-up, diving, exhalation, and inhalation. To validate the effectiveness of this dataset, this study selects YOLOv8n as the baseline model, which balances detection accuracy, speed, and parameter count, enabling efficient giant salamander respiratory detection under limited resources. By comparing it with several representative models, we provide a reliable evaluation of the dataset’s applicability. CGS-BR aims to provide fundamental data support for research on respiratory monitoring in the Chinese giant salamander, laying the foundation for subsequent applications in conservation management, captive breeding, health monitoring, and early disease warning.

Clinically, calculating the Cup-to-Disc Ratio (CDR) from fundus images is a straightforward method to screen and diagnose glaucoma. However, the accurate calculation of CDR heavily depends on precise segmentation of the Optic Disc (OD) and Optic Cup (OC). Most U-net-based OD and OC segmentation methods have achieved good results, but these approaches may lead to outliers, poor integrity, inaccurate and unsmooth contours. To alleviate these issues, we propose a Boundary-Enhanced Cascaded Deep Convolutional Neural Network (BEC-DCNN) for robust and accurate OD and OC segmentation in fundus images, which is followed by a two-stage framework. The first stage employs the DeepLabv3+ network with attention mechanism to detect the OD. In the second stage, we integrate a boundary auxiliary branch, which can obtain multi-scale contour feature maps from the backbone network to produce smoother OD and OC segmentation results. This paper designs a simple and efficient joint loss in training to constrain both the segmentation branch and the boundary branch, which is helpful to improve the segmentation precision and the smoothness of the boundary. Our experiments conducted on the three public datasets, Rim-One-r3, Drishti-GS, and REFUGE, demonstrate that our proposed BEC-DCNN outperforms many previous algorithms. Through qualitative and quantitative comparisons with other methods, our proposal produces more accurate and smoother contours. Our method is also applicable to other medical image segmentation tasks that require accurate edges.

Glaucoma represents a significant global contributor to blindness. Accurately segmenting the optic disc (OD) and optic cup (OC) to obtain precise CDR is essential for effective screening. However, existing convolutional neural network (CNN)-based segmentation techniques are often limited by high computational demands and long inference times. This paper proposes an efficient end-to-end method for OD and OC segmentation, utilizing the lightweight MobileNetv3 network as the core feature-extraction module. Our approach combines boundary branches with adversarial learning, to achieve multi-label segmentation of the OD and OC. We validated our proposed approach across three public available datasets: Drishti-GS, RIM-ONE-r3, and REFUGE. The outcomes reveal that the Dice coefficients for the segmentation of OD and OC within these datasets are 0.974/0.900, 0.966/0.875, and 0.962/0.880, respectively. Additionally, our method substantially lowers computational complexity and inference time, thereby enabling efficient and precise segmentation of the optic disc and optic cup.

Glaucoma, a global leading cause of blindness, necessitates accurate segmentation of the optic disc (OD) and optic cup (OC) for effective screening. However, existing segmentation methods based on convolutional neural networks (CNNs) suffer from high computational complexity and long inference times. In this paper, we propose an end-to-end segmentation method for the OD and OC using a lightweight MobileNetv3 as the feature extraction network. Our approach incorporates boundary branches and an adversarial learning network to achieve multi-label segmentation of the OD and OC. We evaluate the proposed method on three publicly available datasets: Drishti-GS, RIM-ONE-r3, and REFUGE. The experimental results demonstrate segmentation accuracies of 0.974/0.900, 0.966/0.875, and 0.962/0.880 for the OD and OC on the respective datasets, while significantly reducing inference time.

Optimizing the breeding techniques and increasing the hatching rate of Andrias davidianus offspring necessitates a thorough understanding of its parental care behaviors. However, A. davidianus’ nocturnal and cave-dwelling tendencies pose significant challenges for direct observation. To address this problem, this study constructed a dataset for the parental care behavior of A. davidianus, applied the target detection method to this behavior for the first time, and proposed a detection model for A. davidianus’ parental care behavior based on the YOLOv8s algorithm. Firstly, a multi-scale feature fusion convolution (MSConv) is proposed and combined with a C2f module, which significantly enhances the feature extraction capability of the model. Secondly, the large separable kernel attention is introduced into the spatial pyramid pooling fast (SPPF) layer to effectively reduce the interference factors in the complex environment. Thirdly, to address the problem of low quality of captured images, Wise-IoU (WIoU) is used to replace CIoU in the original YOLOv8 to optimize the loss function and improve the model’s robustness. The experimental results show that the model achieves 85.7% in the mAP50-95, surpassing the YOLOv8s model by 2.1%. Compared with other mainstream models, the overall performance of our model is much better and can effectively detect the parental care behavior of A. davidianus. Our research method not only offers a reference for the behavior recognition of A. davidianus and other amphibians but also provides a new strategy for the smart breeding of A. davidianus.

Despite the potential to revolutionize disease diagnosis by performing data-driven classification, clinical interpretability of ConvNet remains challenging. In this paper, a novel clinical interpretable ConvNet architecture is proposed not only for accurate glaucoma diagnosis but also for the more transparent interpretation by highlighting the distinguished regions recognized by the network. To the best of our knowledge, this is the first work of providing the interpretable diagnosis of glaucoma with the popular deep learning model. We propose a novel scheme for aggregating features from different scales to promote the performance of glaucoma diagnosis, which we refer to as MLAP. Moreover, by modeling the correspondence from binary diagnosis information to the spatial pixels, the proposed scheme generates glaucoma activations, which bridge the gap between global semantical diagnosis and precise location. In contrast to previous works, it can discover the distinguish local regions in fundus images as evidence for clinical interpretable glaucoma diagnosis. Experimental results, performed on the challenging ORIGA datasets, show that our method on glaucoma diagnosis outperforms state-of-the-art methods with the highest AUC (0.88). Remarkably, the extensive results, optic disc segmentation (Dice of 0.9) and local disease focus localization based on the evidence map, demonstrate the effectiveness of our methods on clinical interpretability.