arXiv:2607. 03761v1 Announce Type: new Abstract: Computed Tomography (CT) is indispensable in clinical diagnostics, yet minimizing radiation dose without compromising image quality remains a critical challenge.
By Shunta Nonaga, Koji Tabata, Junya Honda, Hiroyuki Kudo, Wataru Yashiro, Tamiki Komatsuzaki
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. 10748v1 Announce Type: new Abstract: Computed Tomography (CT) diagnosis often relies on dynamic selection of imaging phases, such as non-contrast, arterial, or venous phases, based on preliminary findings, clinical suspicion, and diagnostic guidelines.
By Yanmeng Dong, Han Li, Yujia Li, Jingsong Liu, Xun Ma, Yanzhu Hu, Zhengyang Xu, Zhicheng Li, Nassir Navab, Shaohua Kevin Zhou
The paper introduces RA-CMF, a Region‑Adaptive Conditional MeanFlow framework for CT image reconstruction. It combines a conditional MeanFlow network that predicts flow fields for image refinement with a reinforcement‑learning driven policy that allocates tile‑wise refinement budgets. The method achieves high reconstruction quality, reporting a tumor ROI radiomic feature CCC of 0.93 ± 0.09, PSNR of 31.94 ± 2.64, SSIM of 0.97 ± 0.03, and overall PSNR of 34.23 ± 1.71 and SSIM of 0.95 ± 0.01.
By Md Shifatul Ahsan Apurba, Md Selim, Jin Chen
The paper introduces two lightweight spectral adapters—Directional Spectral Adapter (DiSECT) and Spectral Instance-Guided Adapter (SiGA)—to adapt the Segment Anything Model (SAM) for accurate segmentation of colorectal liver metastases in contrast‑enhanced CT scans. SiGA achieves the highest single‑point Dice score of 0.77 and performs comparably to a 3D nnU‑Net baseline under no‑prompt inference, while DiSECT requires only 0.14 million trainable parameters. The study evaluates the adapters on 446 CT volumes across various prompting regimes, demonstrating that spectral adapters can efficiently adapt SAM with limited trainable parameters while maintaining strong segmentation accuracy.
By Ramtin Mojtahedi, Mohammad Hamghalam, Jacob J. Peoples, Natalie Gangai, Mithat Gonen, Yun Shin Chun, HyunSeon Christine Kang, Richard K. G. Do, Amber L. Simpson
The paper introduces TopKSigLIP, a vision‑language model tailored for mammography that tackles two key challenges: high‑resolution imaging and homogeneous radiology reports. It replaces standard CLIP training with a TopK‑Patch module that selects sparse high‑resolution patches likely to contain lesions, and a Sup‑sigmoid loss that uses soft labels from structured data instead of contrastive loss. TopKSigLIP outperforms existing open‑source mammography and general medical VLMs on zero‑shot tasks such as density assessment, BI‑RADS classification, finding subtyping, and cancer prediction, while also providing better lesion localization than Grad‑CAM.
By Young Seok Jeon, Beatrice Brown-Mulry, Rohan Satya Isaac, Anjana Dissanayaka, Theo Dapamede, Mohammadreza Chavoshi, Judy Gichoya, Hari Trivedi