arXiv:2607. 16936v1 Announce Type: cross Abstract: Pediatric bone age prediction is a crucial task in clinical practice that can help diagnose endocrine disorders and provide insight into a child's growth and development.
By Al Zadid Sultan Bin Habib, Md. Ekramul Islam, Md Asif Bin Syed, Md Younus Ahamed, Tanpia Tasnim
RAM‑H1200 is a new public benchmark comprising 1,200 hand radiographs from six medical centers, each annotated with whole‑hand bone instance segmentation, pixel‑level bone erosion masks, joint regions of interest, and joint‑level Sharp‑van der Heijde (SvdH) scores for bone erosion and joint space narrowing. The dataset enables unified multi‑level analysis of anatomical structure, localized erosive pathology, and clinically standardized RA severity, filling a gap in existing resources that lack full‑hand coverage and fine‑grained annotations. Initial benchmark results show strong performance in bone segmentation but highlight that bone erosion segmentation remains a significant challenge, underscoring the dataset’s potential to advance quantitative RA analysis.
By Songxiao Yang, Haolin Wang, Yao Fu, Junmu Peng, Lin Fan, Hongruixuan Chen, Jian Song, Masayuki Ikebe, Shinya Takamaeda-Yamazaki, Masatoshi Okutomi, Tamotsu Kamishima, Yafei Ou
The paper introduces UniFLM, a unified framework for segmenting and measuring fetal long bones in ultrasound images. It presents the Fetal Limb Bones (FLB) dataset with high‑quality annotations for the humerus, femur, tibia‑fibula, and radius‑ulna. UniFLM employs a Semantic‑Aware Skip Connection, a Positive Sampling strategy, and a Point Regression Mapping module to improve segmentation accuracy and bone length measurement, achieving superior performance over existing models on the FLB dataset.
By Zeen Zhou, Qiuhua Chen, Xiaojun Cao, Changmao Chen, Chao Sun, Bo Du
arXiv:2604. 23435v2 Announce Type: replace-cross Abstract: Grading knee osteoarthritis (KOA) on plain radiographs is poorly reproducible across readers.
By Azmul A. Irfan, Nur Ahmad Khatim, Alfan Alfian Irfan, Achmad Zaki, Erike A. Suwarsono, Mansur M. Arief
arXiv:2609.16033v1 Announce Type: cross
Abstract: Cervical vertebral maturation (CVM) assessment plays a pivotal role in orthodontic diagnosis and determining the optimal timing of treatment, especia...
By Peng Wang, Wanzhen Song, Anli Wang, Xueshuo Xie, Xiaohang Guan, Tao Li
arXiv:2606. 15250v1 Announce Type: cross Abstract: Radiographic assessment of lower-limb alignment (LLA) is important for predicting joint health and surgical outcomes in total knee arthroplasty.
By Zhisen Hu, Antti Kemppainen, David Johnson, Egor Panfilov, Huy Hoang Nguyen, Timothy Cootes, Claudia Lindner, Aleksei Tiulpin
arXiv:2609.14703v1 Announce Type: new
Abstract: Dental caries and endodontic disease are among the most common health conditions worldwide, and intraoral periapical radiographs are central to their d...
By Md Jubaer Rahman, Ulas Bagci
The paper presents the first systematic evaluation of out‑of‑distribution generalization for congenital heart disease (CHD) segmentation, using the ImageCHD cohort as a held‑out target. It compares several segmentation architectures under different training regimes, showing that in‑distribution performance is a poor predictor of cross‑cohort robustness: nnU‑Net drops from 0.77 to 0.51 Dice, while SwinUNETR maintains higher performance at 0.67 Dice. Limited target‑domain adaptation with only 11 labeled ImageCHD cases boosts all SwinUNETR variants above 0.76 Dice, highlighting the importance of explicit cross‑dataset testing.
By Aniketh Vijesh, Shrisharanyan Vasu, Abhijit Ramesh, Clare Pomeroy-Ward, Harikrishnan Anil Maya, Sarin Xavier, Mahesh Kappanayil, Gilad Gressel
Congenital heart disease (CHD) diagnosis and surgical planning often require patient-specific 3D anatomical models, but manual segmentation is labor-intensive, particularly in complex anatomies. Altho...
The paper introduces a dual‑input, multi‑task learning framework that jointly segments and classifies bone tumors by applying bidirectional cross‑modal attention between a lesion crop and the full radiograph. Using a YOLO‑based detector and a dual‑stream DenseNet121 architecture, the model fuses fine‑grained lesion detail with global anatomical context through a novel cross‑modal attention fusion strategy and hierarchical multi‑scale feature fusion. On the multi‑institutional Bone Tumor X‑ray Radiograph Dataset, the approach outperforms single‑input baselines, achieving a Dice coefficient of 0.896 and a macro‑averaged F1‑score of 0.928, with an AUC of 0.999 for malignant osteosarcoma.
By S. M. Nasif Uddin, Rusab Sarmun, Muhammad E. H. Chowdhury, Adam Mushtak, Israa Al-Hashimi, Sohaib Bassam Zoghoul
Dental caries and endodontic disease are among the most common health conditions worldwide, and intraoral periapical radiographs are central to their detection, treatment planning, and follow-up. Auto...
arXiv:2609.28434v1 Announce Type: new
Abstract: Adolescent Idiopathic Scoliosis is defined as a lateral curvature of the spine that develops during adolescence, without known cause. The condition can...
By Owen Pullen, Amir Jamaludin, Andrew Zisserman