Author name cluster

Jingmin Xin

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

4 papers

2 author rows

JBHI Journal 2025 Journal Article

Cell-Level Free Cervical Lesion Detection in Cytology Images Via Weakly Supervised Self-Correction

Jiayi Wu
Yan Zhao
Chinmay Chakraborty
Sandeep Kumar Thota
Jingmin Xin
Keping Yu

Cervical cancer remains the fourth most common cancer among women worldwide. Early detection of cervical lesions in cytology images can prevent disease progression, but current deep learning methods for cell- or patch-level analysis in whole slide images (WSI) face significant challenges due to limited, noisy, or incomplete annotations. To address these limitations, weakly supervised learning methods, particularly multiple instance learning (MIL), have been explored. However, traditional MIL methods often suffer from label noise, leading to inaccurate feature extraction, which in turn restricts their robustness and generalization. In this paper, we propose Self-Correcting Instance Learning (SCIL), a novel two-stage instance-based MIL framework designed to enhance instance-level cervical lesion detection under bag-level supervision. SCIL incorporates a weakly supervised self-correction mechanism within a teacher-student architecture to mitigate the effects of noisy pseudo labels. This process involves a contrastive dynamic weighting strategy to adjust instance-level loss and enhance feature representation in stage one, followed by an uncertainty-based self-correction strategy in stage two to retain only high-confidence data with reassigned labels. Extensive evaluations of a slide cervical cytology image dataset demonstrate that SCIL significantly improves the detection of cervical lesions at both the patch and slide levels, highlighting its ability to overcome the limitations of imperfect data in cervical lesion detection.

Details DOI

IROS Conference 2025 Conference Paper

Modeling Human-like Driving Behavior Based on Maximum Entropy Deep Inverse Reinforcement Learning

Jiamin Shi
Tangyike Zhang
Shitao Chen
Nanning Zheng 0001
Jingmin Xin

Modeling expert driving behavior is crucial for the successful implementation of human-like autonomous driving. In this paper, we propose a new sampling-based Maximum Entropy Deep Inverse Reinforcement Learning (MEDIRL) framework. It leverages naturalistic human driving data to train the reward model and thus evaluates driving behaviors from the reward of sampled candidate trajectories. The proposed framework utilizes deep neural networks to learn the feature-reward mapping, which offers superior fitting capabilities compared to traditional linear reward functions. A polynomial trajectory sampler for long-term decision making and a dynamic window trajectory sampler for short-term planning are adopted to simplify the calculation of partition function in the MEDIRL algorithm. In addition, the proposed framework offers a solution to the probability estimation of driving behaviors by calculating the likelihood of sampled candidate trajectories based on their reward values. Comparative experiments are conducted on the NGSIM US-101 Highway dataset, and the experimental results demonstrate the superiority of the proposed model in personalizing reward functions, as well as the applicability of the proposed method in modeling driving behaviors across various time horizons.

Details

AAAI Conference 2024 Conference Paper

GSO-Net: Grid Surface Optimization via Learning Geometric Constraints

Chaoyun Wang
Jingmin Xin
Nanning Zheng
Caigui Jiang

In the context of surface representations, we find a natural structural similarity between grid surface and image data. Motivated by this inspiration, we propose a novel approach: encoding grid surfaces as geometric images and using image processing methods to address surface optimization-related problems. As a result, we have created the first dataset for grid surface optimization and devised a learning-based grid surface optimization network specifically tailored to geometric images, addressing the surface optimization problem through a data-driven learning of geometric constraints paradigm. We conduct extensive experiments on developable surface optimization, surface flattening, and surface denoising tasks using the designed network and datasets. The results demonstrate that our proposed method not only addresses the surface optimization problem better than traditional numerical optimization methods, especially for complex surfaces, but also boosts the optimization speed by multiple orders of magnitude. This pioneering study successfully applies deep learning methods to the field of surface optimization and provides a new solution paradigm for similar tasks, which will provide inspiration and guidance for future developments in the field of discrete surface optimization. The code and dataset are available at https://github.com/chaoyunwang/GSO-Net.

PDF Details DOI

IROS Conference 2023 Conference Paper

InteractionNet: Joint Planning and Prediction for Autonomous Driving with Transformers

Jiawei Fu 0001
Yanqing Shen
Zhiqiang Jian
Shitao Chen
Jingmin Xin
Nanning Zheng 0001

Planning and prediction are two important modules of autonomous driving and have experienced tremendous advancement recently. Nevertheless, most existing methods regard planning and prediction as independent and ignore the correlation between them, leading to the lack of consideration for interaction and dynamic changes of traffic scenarios. To address this challenge, we propose InteractionNet, which leverages transformer to share global contextual reasoning among all traffic participants to capture interaction and interconnect planning and prediction to achieve joint. Besides, InteractionNet deploys another transformer to help the model pay extra attention to the perceived region containing critical or unseen vehicles. InteractionNet outperforms other baselines in several benchmarks, especially in terms of safety, which benefits from the joint consideration of planning and forecasting. The code will be available at https://github.com/fujiawei0724/InteractionNet.

Details