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
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
arXiv:2607. 13826v1 Announce Type: cross Abstract: Accurate determination of pancreatic ductal adenocarcinoma (PDAC) resectability relies on evaluating how the tumor interacts with major peripancreatic vessels on CT imaging, yet expert assessment often shows substantial variability.
By Vincent Ochs, Christoph Kuemmerli, Florentin Bieder, Julia Wolleb, Joel L. Lavanchy, Julia Ruppel, Jan Liechti, Stephanie Taha-Mehlitz, Christian Andreas Nebiker, Beat Mueller, Giuseppe Kito Fusai, Joerg-Matthias Pollok, Anas Taha, Philippe C. Cattin, Sebastian Staubli
This study introduces a computationally efficient convolutional neural network (CNN) architecture enhanced with transfer learning for multi-cancer detection using biomedical images. The proposed lightweight CNN model is designed to reduce computational complexity while maintaining high classification performance, making it suitable for deployment in resource-constrained environments.
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:2606. 04453v1 Announce Type: cross Abstract: Radiomics enables extraction of quantitative imaging biomarkers from medical images and has become an important tool for computer-aided cancer diagnosis.
By Hina Shakir, Mohammad Mohatram, Javeed Hussain, Syed Rizwan Ali, Muhammad Irfan Memon