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
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
The study evaluates lesion‑guided region‑of‑interest (ROI) deep learning for ovarian ultrasound classification, comparing it to global image, lesion contour, and contour‑based radiomics approaches across two public datasets. Using four deep‑learning architectures, the lesion‑guided ROI strategy achieved the highest accuracy (93.10% on MMOTU and 97.56% on OUD) with an AUC of 0.99, while requiring less annotation effort than contour‑based methods.
By Mehran Ahmad, Ali Abbasian Ardakani, Afshin Mohammadi, Alisa Mohebbi, Gernot Kronreif, Sepideh Hatamikia
arXiv:2607. 26580v1 Announce Type: cross Abstract: With the increase in the number of cases related to respiratory diseases, there is an urgent need to detect them early and diagnose them accurately.
By Nand Lal Yadav, Rajesh Kumar, Satyendra Singh, Sudhakar Singh
The paper introduces a dual cross‑attention deep learning framework for automated grading of Cervical Intraepithelial Neoplasia (CIN) and prediction of Swede scores, using a newly released BUET Multi‑Center Colposcopy Dataset. The architecture fuses paired multimodal cervigrams and employs a custom composite loss to handle class imbalance, achieving 71.85% accuracy and 86.23% AUC‑ROC for three‑class CIN grading, and AUC‑ROC values between 75.7% and 88.4% for individual Swede score components. The total predicted Swede Score has a mean absolute error of 1.489, indicating potential for AI‑assisted colposcopy screening in resource‑limited settings.
By Dania Khan, Nuzhat Aisha Shaikh, Asfina Hassan Juicy, Raiyun Kabir, S M Shahida, Taufiq Hasan
The paper presents an automated segmentation pipeline for whole‑slide histopathology images of colorectal cancer, labeling tumor grades 1‑3 and normal mucosa. It employs dense prediction transformers with multiple encoder backbones, overlapping patches, test‑time augmentation, and an adaptive augmentation policy guided by large language models. The approach, combined with soft‑voting ensembles and post‑processing refinements, raises the F1 score from 62.92 to 69.84 on a colorectal cancer grade dataset.
By \"Umit Mert \c{C}a\u{g}lar, Alptekin Temizel