The paper presents a generative AI model that predicts 3‑D radiation dose distributions based solely on user‑defined preference settings, allowing planners to balance trade‑offs between organs‑at‑risk and target volumes. This approach offers greater flexibility and personalization compared to traditional deep‑learning models that rely on institutional reference plans. The authors report that their method can outperform the Varian RapidPlan model in adaptability and plan quality in certain scenarios, and it is designed for easy integration with clinical treatment planning systems.
By Riqiang Gao, Simon Arberet, Martin Kraus, Han Liu, Wilko FAR Verbakel, Dorin Comaniciu, Florin-Cristian Ghesu, Ali Kamen
arXiv:2609.37648v1 Announce Type: new
Abstract: Preoperative liver-tumor assessment requires segmentation, physical-space measurement, visual evidence, and resection planning from the same three-dime...
By Binghong Qian, Xuanhe Liu, Yifan Xing, Wenjie Deng, Jian Wu, Haochao Ying
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.
By Wenxi Liu, Michael Trimboli, Xianqi Li
Dose-PlanNet is a physics-guided 3D deep learning architecture that predicts radiotherapy dose distributions for prostate cancer. In a prospective trial, the model achieved target coverage comparable to clinical plans while slightly reducing target homogeneity, yet it significantly improved high‑dose organ‑at‑risk sparing. Automated plans met clinical acceptance criteria in 11 of 14 Moderate Hypofraction and 9 of 12 SBRT plans, demonstrating that physics-informed deep learning can accelerate radiotherapy workflows without compromising dosimetric quality.
By Ankit Bhattacharjee, Sougata Maity, Santam Chakraborty, Indranil Mallick
Dose-PlanNet is a physics-guided 3D deep learning model that predicts radiotherapy dose distributions for prostate cancer, aiming to automate complex treatment planning. In a prospective trial, the model achieved comparable target coverage while slightly reducing target homogeneity, yet it significantly improved high-dose organ‑at‑risk sparing. Automated plans met clinical acceptance criteria in most cases across both moderate hypofraction and stereotactic body radiation therapy arms.
arXiv:2609.05484v1 Announce Type: cross
Abstract: Deep learning registration methods routinely stack two kinds of enhancement on a base network: architectural additions such as affine pre-alignment s...
By Nabira Rashid
UniReg is a conditional unified model for medical image registration that adapts deformation field estimation based on anatomical priors, registration type constraints, and instance-specific features. It combines the precision of task‑specific learning with the generalization of traditional optimization, enabling effective alignment across diverse CT and MR scenarios within a single framework. Experiments show UniReg outperforms state‑of‑the‑art learning‑based methods in accuracy while providing strong cross‑scenario generalization and reducing training cost and model redundancy.
By Zi Li, Jianpeng Zhang, Tai Ma, Tony C. W. Mok, Yan-Jie Zhou, Zeli Chen, Xianghua Ye, Le Lu, Cheng Chen, Dakai Jin
arXiv:2608. 01839v1 Announce Type: new Abstract: Transcranial focused ultrasound (tFUS) requires accurate estimation of the intracranial acoustic field, which is distorted by skull-induced aberrations.
By Minjee Seo, Haris Ghafoor, Minju Seol, Seonaeng Cho, Kyungho Yoon
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
The paper presents DualTTA, a model‑agnostic framework that improves the calibration of black‑box radiology AI systems by applying clinically grounded test‑time augmentations (geometric and physics‑inspired 3D CT perturbations) and learning probability‑level aggregation strategies. Without accessing model internals or training data, DualTTA achieved the best overall calibration across pulmonary embolism and intracranial hemorrhage detection tasks, reducing Expected Calibration Error by 54% and 43% respectively. It also outperformed traditional uncertainty estimation methods that require internal model access, such as Temperature Scaling, MC Dropout, and Deep Ensembles.
By Nathan Le, Magdalini Paschali, Arogya Koirala, Andrew Johnston, Zhongnan Fang, David B. Larson, Akshay S. Chaudhari, Camila Gonzalez
arXiv:2606. 09898v1 Announce Type: new Abstract: Cancer treatment planning requires decisions across multiple clinical dimensions at once.
By Sujoy Banik, Sayantan Chakraborty, Boishakhi Das Toma, Zainab Ghafoor, Ushashi Bhattacharjee, Koushik Howlader, Tirtho Roy
arXiv:2607. 08219v2 Announce Type: replace-cross Abstract: The privacy requirements of medical data and its substantial variations across organs and modalities hinder the clinical implementation of medical AI.
By Junbin Mao, Xu Tian, Jianchun Zhu, Ludi Li, Jin Liu