The paper introduces a dual‑stage deep learning system for detecting dental caries in panoramic radiographs. It first localizes teeth using Faster R‑CNN, then applies U‑Net for pixel‑wise caries segmentation, converting polygon annotations into high‑resolution binary masks. Trained on 3,000 images with both expert and algorithmic labels, the model achieves an IoU of 0.9013, Dice of 0.9482, Recall of 0.9433, and Precision of 0.9774, outperforming existing methods and reducing false positives.
By Jihun Kim, Kyeonghun Kim, Jong-yeol Lee, Yeongseok Seo, Dohyun Chun
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
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...
The study evaluates four deep‑learning segmentation architectures—Unet, PSPNet, Linknet, and FPN—paired with six pre‑trained encoders to predict COVID‑19 lesions in CT images. Experiments on three COVID‑19 CT datasets show high accuracy, achieving a maximum binary F1‑score of 98% and multi‑class F1‑scores of 75% and 77%. The work aims to provide a standardized performance benchmark for medical image segmentation and a reference for other imaging scenarios.
By Sarmad Khan, Basim Azam, Arslan Shaukat
arXiv:2608.31052v1 Announce Type: cross
Abstract: Semantic segmentation decomposes an image into distinct mask regions corresponding to different object categories, such as people, cars, signs or bui...
By Keith G. Mills, Evan B. Sanders, Gregory J. Matthews, Juliet K. Brophy
arXiv:2607. 22139v1 Announce Type: cross Abstract: Accurate pixel-level classification of coronary angiograms is critical for cardiovascular disease assessment, yet the field lacks standardized evaluation protocols.
By Dominik Bernard Lau, Hubert Malinowski, Jerzy Szyjut, Adam Brzeski, Tomasz Dziubich, Rados{\l}aw Targo\'nski, Tomasz Figatowski, Natalia Zieli\'nska
arXiv:2608. 15915v1 Announce Type: cross Abstract: Lung cancer remains the leading cause of cancer-related mortality worldwide, while histopathological diagnosis is often affected by inter-observer variability and the substantial workload associated with manual slide examination.
By Hadi Hasan, Safaa Salman, Lama Sleem, Ralph Mouawad, Ali Chehab
arXiv:1812.00877v2 Announce Type: replace-cross
Abstract: Segmentation of skin lesion boundaries in dermoscopic imaging is an important prerequisite step for computer-aided diagnosis of malignant mel...
By Glib Kechyn
arXiv:2608.21583v1 Announce Type: new
Abstract: Oral cancer is a leading cause of mortality in low-to-middle-income countries, where a shortage of specialists delays diagnosis. While point-of-care sc...
By Siddhant Bharadwaj, Aakash Shedsale, Tejashree Subramanya, Mohd. Azfar, Praveen Birur, Debnath Pal, Shankararama Sharma, Anupama Shetty, Rajesh Sundaresan
This study presents a clinically relevant framework for evaluating deep neural networks that segment lymphoma lesions in PET/CT images, addressing gaps such as out‑of‑distribution testing and comparison with expert annotators. Using 611 multi‑institutional cases, the authors assess four networks (ResUNet, SegResNet, DynUNet, SwinUNETR) with lesion‑specific metrics, detection criteria, and metabolic‑characteristic‑based thresholds, finding that models perform best on large, intense lesions. The work also demonstrates that network errors mirror those of physicians, highlighting shared challenges with small, faint lesions.
By Shadab Ahamed, Yixi Xu, Sara Kurkowska, Claire Gowdy, Joo H. O, Ingrid Bloise, Don Wilson, Patrick Martineau, Fran\c{c}ois B\'enard, Fereshteh Yousefirizi, Rahul Dodhia, Juan M. Lavista, William B. Weeks, Carlos F. Uribe, Arman Rahmim
The paper presents TLNM, a Mask R‑CNN based system that detects, numbers, and segments teeth in smartphone photographs. It incorporates a masked gray‑world white‑balancing step and an anatomically constrained detection layer to handle patient‑generated variability. Evaluated on internal and external datasets, the model achieved high AP50, PQ, and F1 scores, demonstrating robust performance across diverse populations and imaging conditions.
By Arash Nedaei, Henna Tiensuu, Elina V\"ayrynen, Saujanya Karki, Jaakko Suutala
arXiv:2607. 02185v1 Announce Type: cross Abstract: Deep learning has achieved remarkable performance in medical image segmentation, yet it suffers from critical limitations: mathematical intractability, substantial parameter requirements, and lack of clinical interpretability.
By Mohammad Amanour Rahman