arXiv subjects

Wenjin Chen

Publications and source records attributed to Wenjin Chen.

4 recordsLinked to original sources

arXiv2025

Echo-CoPilot: A Multiple-Perspective Agentic Framework for Reliable Echocardiography Interpretation

Echocardiography interpretation requires integrating multi-view temporal evidence with quantitative measurements and guideline-grounded reasoning, yet existing foundation-model pipelines largely solve isolated subtasks and fail when tool outputs are noisy or values fall near clinical cutoffs. We propose Echo-CoPilot, an end-to-end agentic framework that combines a multi-perspective workflow with knowledge-graph guided measurement selection. Echo-CoPilot runs three independent ReAct-style agents, structural, pathological, and quantitative, that invoke specialized echocardiography tools to extract parameters while querying EchoKG to determine which measurements are required for the clinical question and which should be avoided. A self-contrast language model then compares the evidence-grounded perspectives, generates a discrepancy checklist, and re-queries EchoKG to apply the appropriate guideline thresholds and resolve conflicts, reducing hallucinated measurement selection and borderline flip-flops. On MIMICEchoQA, Echo-CoPilot provides higher accuracy compared to SOTA baselines and, under a stochasticity stress test, achieves higher reliability through more consistent conclusions and fewer answer changes across repeated runs. Our code is publicly available at~\href{https://github.com/moeinheidari7829/Echo-CoPilot}{\textcolor{magenta}{GitHub}}.

cs.AI↗

arXiv2025

Echo-E$^3$Net: Efficient Endocardial Spatio-Temporal Network for Ejection Fraction Estimation

Objective To develop a robust and computationally efficient deep learning model for automated left ventricular ejection fraction (LVEF) estimation from echocardiography videos that is suitable for real-time point-of-care ultrasound (POCUS) deployment. Methods We propose Echo-E$^3$Net, an endocardial spatio-temporal network that explicitly incorporates cardiac anatomy into LVEF prediction. The model comprises a dual-phase Endocardial Border Detector (E$^2$CBD) that uses phase-specific cross attention to localize end-diastolic and end-systolic endocardial landmarks and to learn phase-aware landmark embeddings, and an Endocardial Feature Aggregator (E$^2$FA) that fuses these embeddings with global statistical descriptors of deep feature maps to refine EF regression. Training is guided by a multi-component loss inspired by Simpson's biplane method that jointly supervises EF and landmark geometry. We evaluate Echo-E$^3$Net on the EchoNet-Dynamic dataset using RMSE and R$^2$ while reporting parameter count and GFLOPs to characterize efficiency. Results On EchoNet-Dynamic, Echo-E$^3$Net achieves an RMSE of 5.20 and an R$^2$ score of 0.82 while using only 1.55M parameters and 8.05 GFLOPs. The model operates without external pre-training, heavy data augmentation, or test-time ensembling, supporting practical real-time deployment. Conclusion By combining phase-aware endocardial landmark modeling with lightweight spatio-temporal feature aggregation, Echo-E$^3$Net improves the efficiency and robustness of automated LVEF estimation and is well-suited for scalable clinical use in POCUS settings. Code is available at https://github.com/moeinheidari7829/Echo-E3Net

eess.IV↗

arXiv2025

A Study on the Performance of U-Net Modifications in Retroperitoneal Tumor Segmentation

The retroperitoneum hosts a variety of tumors, including rare benign and malignant types, which pose diagnostic and treatment challenges due to their infrequency and proximity to vital structures. Estimating tumor volume is difficult due to their irregular shapes, and manual segmentation is time-consuming. Automatic segmentation using U-Net and its variants, incorporating Vision Transformer (ViT) elements, has shown promising results but struggles with high computational demands. To address this, architectures like the Mamba State Space Model (SSM) and Extended Long-Short Term Memory (xLSTM) offer efficient solutions by handling long-range dependencies with lower resource consumption. This study evaluates U-Net enhancements, including CNN, ViT, Mamba, and xLSTM, on a new in-house CT dataset and a public organ segmentation dataset. The proposed ViLU-Net model integrates Vi-blocks for improved segmentation. Results highlight xLSTM's efficiency in the U-Net framework. The code is publicly accessible on GitHub.

eess.IV↗

arXiv2011

Phase diagram of Symmetric Iterated Prisoner's Dilemma of Two-Companies with Partial Imitation Rule

The problem of two companies of agents with one-step memory playing game is investigated in the context of the Iterated Prisoner's Dilemma under the partial imitation rule, where a player can imitate only those moves that he has observed in his games with his opponent. We limit our study to the special case where the players in the two groups enjoy the same conditions on a fully connected network, so that there are only two payoff matrices required: one for players playing games with members of the same company, and the other one for players playing games with members from a different company. We show that this symmetric case of two companies of players can be reduced to the one-company case with an effective payoff matrix, from which a phase diagram for the players using the two dominant strategies, Pavlov and Grim Trigger can be constructed. The phase diagram is computed by numerical integration of the approximate mean value equations. The results are in good agreement with simulations of the two-company model. The phase diagram leads to an interesting conclusion that a player will more likely become a Grim Trigger, regardless of their affiliated company, when the noise level increases so that he is more irrational, or when the intra-group temptation to defect increases.

physics.soc-ph↗