arXiv subjects

Boyi Zheng

Publications and source records attributed to Boyi Zheng.

3 recordsLinked to original sources

arXiv2026

Long-Delayed Afterpulse Measurement of JUNO 20-inch Photomultiplier Tubes

In large-scale liquid scintillator detectors such as the Jiangmen Underground Neutrino Observatory (JUNO), high-intensity events like cosmic muons induce photomultiplier tube (PMT) afterpulses that can interfere with the analysis of delayed physics signals. To systematically evaluate this instrumental background, we present a dedicated measurement of long-delayed afterpulses in two types of JUNO 20-inch PMTs: a dynode-based PMT and a microchannel-plate (MCP) PMT. The afterpulse time profiles were first characterized within a direct 1.8~ms waveform window and were further extended to 20~ms using a sliding-window readout strategy. Distinct long-delayed components are observed, revealing a strong dependence on the PMT multiplication structure. The dynode PMT exhibits a broad afterpulse component peaking at approximately 260~$\mu$s, whereas the MCP-PMT shows a pronounced peak around 90~$\mu$s, an additional component around 550~$\mu$s, and a much smaller, broadly distributed millisecond-scale component. For the microsecond-scale components, the afterpulse yield per primary photoelectron is at the $10^{-3}$ level in the selected delayed windows and increases approximately linearly with the primary light intensity. The accumulated delayed activity can therefore become non-negligible following high-intensity events. These quantitative findings provide critical inputs for PMT response characterization and for the accurate modeling of delayed correlated backgrounds in high-precision neutrino experiments.

hep-ex↗

arXiv2025

PSScreen V2: Partially Supervised Multiple Retinal Disease Screening

In this work, we propose PSScreen V2, a partially supervised self-training framework for multiple retinal disease screening. Unlike previous methods that rely on fully labelled or single-domain datasets, PSScreen V2 is designed to learn from multiple partially labelled datasets with different distributions, addressing both label absence and domain shift challenges. To this end, PSScreen V2 adopts a three-branch architecture with one teacher and two student networks. The teacher branch generates pseudo labels from weakly augmented images to address missing labels, while the two student branches introduce novel feature augmentation strategies: Low-Frequency Dropout (LF-Dropout), which enhances domain robustness by randomly discarding domain-related low-frequency components, and Low-Frequency Uncertainty (LF-Uncert), which estimates uncertain domain variability via adversarially learned Gaussian perturbations of low-frequency statistics. Extensive experiments on multiple in-domain and out-of-domain fundus datasets demonstrate that PSScreen V2 achieves state-of-the-art performance and superior domain generalization ability. Furthermore, compatibility tests with diverse backbones, including the vision foundation model DINOv2, as well as evaluations on chest X-ray datasets, highlight the universality and adaptability of the proposed framework. The codes are available at https://github.com/boyiZheng99/PSScreen_V2.

cs.CV↗

arXiv2025

PSScreen: Partially Supervised Multiple Retinal Disease Screening

Leveraging multiple partially labeled datasets to train a model for multiple retinal disease screening reduces the reliance on fully annotated datasets, but remains challenging due to significant domain shifts across training datasets from various medical sites, and the label absent issue for partial classes. To solve these challenges, we propose PSScreen, a novel Partially Supervised multiple retinal disease Screening model. Our PSScreen consists of two streams and one learns deterministic features and the other learns probabilistic features via uncertainty injection. Then, we leverage the textual guidance to decouple two types of features into disease-wise features and align them via feature distillation to boost the domain generalization ability. Meanwhile, we employ pseudo label consistency between two streams to address the label absent issue and introduce a self-distillation to transfer task-relevant semantics about known classes from the deterministic to the probabilistic stream to further enhance the detection performances. Experiments show that our PSScreen significantly enhances the detection performances on six retinal diseases and the normal state averagely and achieves state-of-the-art results on both in-domain and out-of-domain datasets. Codes are available at https://github.com/boyiZheng99/PSScreen.

cs.CV↗