arXiv subjects

Zeliang Ma

Publications and source records attributed to Zeliang Ma.

3 recordsLinked to original sources

arXiv2026

Dose-LET Interactions Predict Capsular Contracture After Proton Postmastectomy Radiation Therapy

Pencil beam scanning (PBS) proton therapy provides highly conformal dose distributions that are increasingly leveraged for postmastectomy radiation therapy (PMRT) to reduce cardiopulmonary exposure. However, implant-based reconstruction in the setting of PMRT remains vulnerable to capsular contracture, and biological mechanisms of possible high linear energy transfer (LET) in PBS have not been well characterized. A retrospective case-control study was conducted on consecutive breast cancer patients who underwent mastectomy followed by implant-based reconstruction and proton PMRT (50 Gy in 25 fractions) between 2015 and 2021. Dose-LET volume histograms (DLVHs) were calculated for peri-implant tissue (5-mm shell around the implant). Generalized linear mixed-effects regression (GLMER) was employed to identify DLVH indices significantly associated with capsular contracture. Spearman correlation analysis was used to eliminate redundance. DLVCs were derived from receiver operating characteristic (ROC) analysis and validated using support vector machine (SVM)-based normal tissue complication probability (NTCP) model. Eight capsular contracture and 16 matched controls patients were analyzed. Three independent and significant DLVH indices were identified(p<0.01). The corresponding DLVCs were: V(55.8 Gy[RBE=1.1], 2.2 keV/{\mu}m) < 0.0033%, V(50.3 Gy[RBE=1.1], 5.4 keV/{\mu}m) < 0.0017%, and V(32.8 Gy[RBE=1.1], 0.9 keV/{\mu}m) > 96.98%. The SVM-based NTCP model achieved an area under the ROC curve (AUROC) of 0.867, with 91.7% accuracy, 87.5% sensitivity, and 93.8% specificity. Capsular contracture following proton PMRT is significantly associated with the synergistic interplay between dose and LETd in peri-implant tissue. The derived DLVCs provide actionable dosimetric constraints that can be integrated into treatment planning to minimize capsular contracture risk in proton PMRT.

physics.med-ph↗

arXiv2024

The RoboDrive Challenge: Drive Anytime Anywhere in Any Condition

In the realm of autonomous driving, robust perception under out-of-distribution conditions is paramount for the safe deployment of vehicles. Challenges such as adverse weather, sensor malfunctions, and environmental unpredictability can severely impact the performance of autonomous systems. The 2024 RoboDrive Challenge was crafted to propel the development of driving perception technologies that can withstand and adapt to these real-world variabilities. Focusing on four pivotal tasks -- BEV detection, map segmentation, semantic occupancy prediction, and multi-view depth estimation -- the competition laid down a gauntlet to innovate and enhance system resilience against typical and atypical disturbances. This year's challenge consisted of five distinct tracks and attracted 140 registered teams from 93 institutes across 11 countries, resulting in nearly one thousand submissions evaluated through our servers. The competition culminated in 15 top-performing solutions, which introduced a range of innovative approaches including advanced data augmentation, multi-sensor fusion, self-supervised learning for error correction, and new algorithmic strategies to enhance sensor robustness. These contributions significantly advanced the state of the art, particularly in handling sensor inconsistencies and environmental variability. Participants, through collaborative efforts, pushed the boundaries of current technologies, showcasing their potential in real-world scenarios. Extensive evaluations and analyses provided insights into the effectiveness of these solutions, highlighting key trends and successful strategies for improving the resilience of driving perception systems. This challenge has set a new benchmark in the field, providing a rich repository of techniques expected to guide future research in this field.

cs.CV↗

arXiv2024

MLS-Track: Multilevel Semantic Interaction in RMOT

The new trend in multi-object tracking task is to track objects of interest using natural language. However, the scarcity of paired prompt-instance data hinders its progress. To address this challenge, we propose a high-quality yet low-cost data generation method base on Unreal Engine 5 and construct a brand-new benchmark dataset, named Refer-UE-City, which primarily includes scenes from intersection surveillance videos, detailing the appearance and actions of people and vehicles. Specifically, it provides 14 videos with a total of 714 expressions, and is comparable in scale to the Refer-KITTI dataset. Additionally, we propose a multi-level semantic-guided multi-object framework called MLS-Track, where the interaction between the model and text is enhanced layer by layer through the introduction of Semantic Guidance Module (SGM) and Semantic Correlation Branch (SCB). Extensive experiments on Refer-UE-City and Refer-KITTI datasets demonstrate the effectiveness of our proposed framework and it achieves state-of-the-art performance. Code and datatsets will be available.

cs.CV↗