arXiv Machine Learning August 5, 2026 By Seonaeng Cho, Minjee Seo, Minju Seol, Juil Park, Joon Ho Kwon, Kyungho Yoon

Automatic Patient-Specific Microwave Ablation Planning Accelerated by a Physics-Guided Deep Learning Model

Read the original on arXiv Machine Learning →

arXiv:2608. 03086v1 Announce Type: cross Abstract: Microwave ablation (MWA) is a promising minimally invasive treatment for liver tumors, but its therapeutic outcome strongly depends on patient-specific planning of antenna insertion trajectory, power, and treatment duration.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

benchmarks

arXiv Machine Learning

Jul 16

AI-Augmented Adaptive Digital Twin Modeling for Brain Tumor Evolution Prediction and Treatment Scheduling

arXiv:2607. 13877v1 Announce Type: new Abstract: Brain tumor progression exhibits spatially heterogeneous growth, patient-specific treatment response, and complex interactions with surrounding anatomy, making accurate long-term prediction challenging.

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diffusion

arXiv Machine Learning

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tFUSOperator: Operator Learning for Transcranial Focused Ultrasound Digital Twins

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By Minjee Seo, Haris Ghafoor, Minju Seol, Seonaeng Cho, Kyungho Yoon

arXiv AI

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The Large Cancer Assistant (LCA): A Model-Agnostic Orchestration Framework for Scalable Clinical Decision Support in Oncology

arXiv:2607. 06531v1 Announce Type: new Abstract: - Objective: Multimodal deep learning models in oncology are currently limited by monolithic designs that rigidly couple data ingestion, clinical routing, and artificial intelligence (AI) inference.

By Ghassen Marrakchi, Basarab Matei

agents multimodal safety

arXiv Machine Learning

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TRAPS: Therapeutic Response Analysis via Pathway-informed Stratification

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benchmarks

arXiv Machine Learning

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Benchmark Evaluation of Federated Learning on Multi-organ Images

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By Junbin Mao, Xu Tian, Jianchun Zhu, Ludi Li, Jin Liu

benchmarks safety

arXiv Machine Learning

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Radiomic Feature Selection Using Gradient Loss of Deep Neural Network for Lung Cancer Stage Detection

arXiv:2606. 04453v1 Announce Type: cross Abstract: Radiomics enables extraction of quantitative imaging biomarkers from medical images and has become an important tool for computer-aided cancer diagnosis.

By Hina Shakir, Mohammad Mohatram, Javeed Hussain, Syed Rizwan Ali, Muhammad Irfan Memon

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