Tissues and Organs

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Cross submissions (showing 1 of 1 entries )

[1] arXiv:2509.21206 (cross-list from q-bio.TO) [cn-pdf, pdf, html, other]

Title: Data-driven Neural Networks for Windkessel Parameter Calibration Show Chinese title

Title: 基于数据的神经网络用于Windkessel参数校准

Benedikt Hoock, Tobias Köppl

Comments: 32 pages, 15 figures, for associated git see https://github.com/bhoock/WKcalNN, submitted to International Journal for Numerical Methods in Biomedical Engineering

Subjects: Tissues and Organs (q-bio.TO) ; Machine Learning (cs.LG) ; Numerical Analysis (math.NA) ; Optimization and Control (math.OC) ; Quantitative Methods (q-bio.QM)

In this work, we propose a novel method for calibrating Windkessel (WK) parameters in a dimensionally reduced 1D-0D coupled blood flow model. To this end, we design a data-driven neural network (NN)trained on simulated blood pressures in the left brachial artery. Once trained, the NN emulates the pressure pulse waves across the entire simulated domain, i.e., over time, space and varying WK parameters, with negligible error and computational effort. To calibrate the WK parameters on a measured pulse wave, the NN is extended by dummy neurons and retrained only on these. The main objective of this work is to assess the effectiveness of the method in various scenarios -- particularly, when the exact measurement location is unknown or the data are affected by noise. Show Chinese abstract

在本工作中，我们提出了一种新的方法，用于校准降维1D-0D耦合血流模型中的Windkessel (WK)参数。 为此，我们设计了一个基于数据驱动的神经网络（NN），并在左肱动脉的模拟血压上进行训练。 一旦训练完成，该NN能够在整个模拟域内，即在时间、空间和不同的WK参数下，以极小的误差和计算量模拟压力脉冲波。 为了在测量的脉冲波上校准WK参数，通过添加虚拟神经元扩展NN，并仅在这些数据上重新训练。 本文的主要目标是评估该方法在各种场景下的有效性——特别是当精确的测量位置未知或数据受噪声影响时。

Replacement submissions (showing 1 of 1 entries )

[2] arXiv:2509.16328 (replaced) [cn-pdf, pdf, html, other]

Title: The Role of High-Performance GPU Resources in Large Language Model Based Radiology Imaging Diagnosis Show Chinese title

Title: 高性能GPU资源在基于大型语言模型的放射学影像诊断中的作用

Jyun-Ping Kao

Subjects: Tissues and Organs (q-bio.TO)

Large-language models (LLMs) are rapidly being applied to radiology, enabling automated image interpretation and report generation tasks. Their deployment in clinical practice requires both high diagnostic accuracy and low inference latency, which in turn demands powerful hardware. High-performance graphical processing units (GPUs) provide the necessary compute and memory throughput to run large LLMs on imaging data. We review modern GPU architectures (e.g. NVIDIA A100/H100, AMD Instinct MI250X/MI300) and key performance metrics of floating-point throughput, memory bandwidth, VRAM capacity. We show how these hardware capabilities affect radiology tasks: for example, generating reports or detecting findings on CheXpert and MIMIC-CXR images is computationally intensive and benefits from GPU parallelism and tensor-core acceleration. Empirical studies indicate that using appropriate GPU resources can reduce inference time and improve throughput. We discuss practical challenges including privacy, deployment, cost, power and optimization strategies: mixed-precision, quantization, compression, and multi-GPU scaling. Finally, we anticipate that next-generation features (8-bit tensor cores, enhanced interconnect) will further enable on-premise and federated radiology AI. Advancing GPU infrastructure is essential for safe, efficient LLM-based radiology diagnostics. Show Chinese abstract

大型语言模型（LLMs）正在被迅速应用于放射学，使自动图像解释和报告生成任务成为可能。 它们在临床实践中的部署需要高诊断准确性和低推理延迟，这反过来需要强大的硬件。 高性能图形处理单元（GPUs）提供了运行大型LLMs所需的计算和内存吞吐量。 我们回顾了现代GPU架构（例如，NVIDIA A100/H100，AMD Instinct MI250X/MI300）以及浮点吞吐量、内存带宽、VRAM容量等关键性能指标。 我们展示了这些硬件能力如何影响放射学任务：例如，在CheXpert和MIMIC-CXR图像上生成报告或检测发现是计算密集型任务，并且受益于GPU并行性和张量核心加速。 实证研究表明，使用适当的GPU资源可以减少推理时间并提高吞吐量。 我们讨论了实际挑战，包括隐私、部署、成本、功耗和优化策略：混合精度、量化、压缩和多GPU扩展。 最后，我们预计下一代功能（8位张量核心、增强的互连）将进一步推动本地和联邦放射学AI的发展。 推进GPU基础设施对于安全、高效的基于LLM的放射学诊断至关重要。