arXiv:2607. 17789v1 Announce Type: cross Abstract: Early and timely screening of laryngeal cancer is crucial for improving clinical outcomes.
arXiv:2606. 16153v1 Announce Type: cross Abstract: Medical image segmentation plays a critical role in clinical diagnostics, treatment planning, disease monitoring, and neurological disorder identification.
arXiv:2607. 11533v1 Announce Type: cross Abstract: Perineural invasion (PNI) is a critical prognostic factor in cholangiocarcinoma.
arXiv:2509.25549v3 Announce Type: replace-cross Abstract: Choroidal nevi are common benign pigmented lesions in the eye, with a small risk of transforming into melanoma. Early detection is critical t...
arXiv:2508. 17728v2 Announce Type: replace-cross Abstract: Cervical cancer remains a significant global health concern and a leading cause of cancer-related deaths among women.
The article reviews recent applications of artificial intelligence and deep learning, particularly convolutional neural networks with transfer learning and data augmentation, for detecting lung cancer in medical images. It surveys 96 studies from 2015 onward, highlighting high sensitivity and specificity achieved by these methods. The authors also identify challenges such as data standardization, model explainability, patient privacy, and ethical concerns that must be addressed before clinical deployment.
arXiv:2606. 28419v1 Announce Type: cross Abstract: Limited data availability, class imbalance, and domain variability remain major barriers to reliable medical image classification.
arXiv:2606. 28419v2 Announce Type: replace-cross Abstract: Limited data availability, class imbalance, and domain variability remain major barriers to reliable medical image classification.
The paper introduces a Hybrid Cross-Modal Attention Network (HCMAN) that fuses mammogram images with structured clinical data using transformer-based cross‑modal attention. Trained on a locally collected dataset of 2,560 images from 1,024 Ethiopian patients, the model achieves 97.8% accuracy, 97.2% sensitivity, 98.3% specificity, and an AUC of 0.987, outperforming image‑only baselines and maintaining robustness to low‑quality images. Its lightweight design allows inference in under two seconds on a standard CPU, making it suitable for deployment in resource‑limited clinical settings.
arXiv:2610.08528v1 Announce Type: cross Abstract: Clinical diagnosis is inherently a structured reasoning process, yet existing deep learning models often bypass this structure by mapping image featu...
arXiv:2602. 04819v5 Announce Type: replace-cross Abstract: Accurate risk stratification of precancerous polyps during routine colonoscopy screening is a key strategy to reduce the incidence of colorectal cancer (CRC).
Colorectal cancer (CRC) is the second most deadly and third most common cancer, and the leading cause of death among gastrointestinal cancers. Early diagnosis is crucial for the treatment of this canc...
arXiv:2608. 15004v1 Announce Type: cross Abstract: Lung cancer remains one of the leading causes of cancer-related mortality worldwide, and Computed Tomography (CT) is a primary imaging tool for screening and followup assessment.