April 2026 in “Scientific Reports” In this study, the proposed MSF-VMDNet, combining dual encoder networks with a multi-frequency domain mechanism, significantly outperformed existing methods in segmenting skin cancer tissues from histological slide images, achieving high accuracy with an MIoU of 95.37% and a Dice coefficient of 95.11%.
November 2022 in “bioRxiv (Cold Spring Harbor Laboratory)” In this study, deep learning models accurately predicted gene expression in whole slide images of colorectal cancer, with convolutional neural networks outperforming transformer and graph-based approaches in spatial RNA pattern prediction.
January 2024 in “Wiadomości Lekarskie” This study developed an AI-driven method for classifying cells in Follicular Lymphoma cases, achieving a 63% F1-score, precision, and recall in distinguishing centroblasts from other cell types using whole slide images at x20 resolution.
January 2026 in “Scientific Reports” This study assessed Palestinian women newly diagnosed with breast cancer and found that higher spiritual well-being is associated with improved body image and sexual functioning, and reduced distress from treatment side effects, suggesting spiritual support should be integrated into cancer care.
January 2025 in “ARC Journal of Urology” This review reports that treatments for urological cancers, such as chemotherapy and immunotherapy, often result in significant dermatological side effects that negatively affect patients' quality of life and treatment adherence, highlighting a need for better management strategies and further research.