The paper introduces a Mixture of Multicenter Experts (MoME) framework that leverages diverse clinical strategies to reduce bias in medical AI without sharing data across institutions. MoME integrates specialized expertise from multiple centers, improving generalizability and adaptability of a multimodal target volume delineation model for prostate cancer radiotherapy. The model, trained with few-shot imaging and clinical notes, outperformed baselines, especially in high inter‑center variability or limited data scenarios, and allows local customization without cross‑institutional data exchange.
By Yujin Oh, Sangjoon Park, Xiang Li, Pengfei Jin, Yi Wang, Jonathan Paly, Jason Efstathiou, Annie Chan, Jun Won Kim, Hwa Kyung Byun, Ik Jae Lee, Jaeho Cho, Chan Woo Wee, Peng Shu, Peilong Wang, Caiwen Jiang, Nathan Yu, Jason Holmes, Jong Chul Ye, Quanzheng Li, Wei Liu, Woong Sub Koom, Jin Sung Kim, Kyungsang Kim
The study explores how adding anatomical priors and active learning can improve the accuracy of deep learning models for segmenting the Clinical Target Volume (CTV) in gastric cancer radiotherapy. Using 100 retrospective CT scans, an nnU‑Net model trained on 10 expert‑contoured cases was enhanced with voxel‑wise anatomical prior maps and iterative active learning over four rounds. The combined approach raised the mean Dice Similarity Coefficient from 0.84 to 0.87, demonstrating that both techniques individually and together improve segmentation performance and generalizability.
By Phillip Chlap, Mark Lee, Trevor Leong, Matthew Field, Jason Dowling, Hang Min, Julie Chu, Jennifer Tan, Phillip K. Tran, Tomas Kron, Annette Haworth, Martin A. Ebert, Shalini K. Vinod, Lois Holloway
arXiv:2505.03380v2 Announce Type: replace
Abstract: Accurate delineation of tumors and surrounding organs-at-risk is essential for radiotherapy, surgery and treatment response assessment, yet remains...
By Haonan Wang, Jiaji Mao, Lehan Wang, Qixiang Zhang, Marawan Elbatel, Yi Qin, Huijun Hu, Baoxun Li, Wenhui Deng, Weifeng Qin, Hongrui Li, Jialin Liang, Jun Shen, Xiaomeng Li
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
The paper introduces an agentic AI Scientist workflow that automates the entire baseline development process for medical imaging by combining literature-guided reasoning, automated code generation, and hypothesis-driven experimentation. Evaluated on four public benchmarks covering segmentation, classification, and detection, the pipeline consistently improves validation performance, achieving competitive leaderboard results such as 6th place on both PUMA tracks and 31st on MILK10k. The approach also shows strong domain generalization on MIDOG25 across scanners, tumor types, and species, demonstrating that a skill-based, literature-guided agentic workflow can reduce engineering effort without task-specific redesign.
By Eugenia Moris, Jos\'e Ignacio Orlando
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.
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
arXiv:2609.26384v1 Announce Type: new
Abstract: Medical image interpretation is high-volume and time-consuming, and while AI interpretation can reduce workload, fully autonomous deployment carries po...
By Emma Sun, Joshua Strong, Alison Noble
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
Lung cancer tissue diagnostics is complex, as therapy decisions in precision oncology rely on the integration of histomorphological, immunohistochemical, and molecular features. Yet pathological asses...
arXiv:2604.16729v2 Announce Type: replace-cross
Abstract: State-of-the-art large language models (LLMs) show high performance in general visual question answering. However, a fundamental limitation r...
By Ayhan Can Erdur, Daniel Scholz, Jiazhen Pan, Benedikt Wiestler, Daniel Rueckert, Jan C. Peeken
arXiv:2608.21864v1 Announce Type: cross
Abstract: The current progress of Clinical Vision Large Language Models (C-VLLMs) has substantially improved digital diagnostics, still these frameworks often...
By Md Asaduzzaman Jabin, Zihao Wu, Tianming Liu