Renal cell carcinoma accounts for nearly 90 % of kidney cancers, and accurate histopathological subtyping is crucial for diagnosis, prognosis, and treatment. However, the gigapixel size of WSIs and the lack of pixel‑wise annotations make traditional deep‑learning approaches challenging. Most existing multiple instance learning methods treat tissue patches independently, losing global contextual dependencies and struggling with class imbalance or indistinct tumour boundaries. A research team from Chongqing University, Peking University School and Hospital of Stomatology & NHC Key Laboratory of Digital Stomatology, and The Hong Kong Polytechnic University has developed TSMIL-a Transformer‑based structured low‑rank multiple instance learning network. Published in Intelligent Oncology.

Recently, the Editorial Office of Intelligent Oncology received a notification from the DOAJ Team that, after rigorous evaluation, the journal has been officially indexed in DOAJ, the world’s most influential open access journal database.

A new review published in Intelligent Oncology (Volume 2, Issue 2, 2026) highlights a powerful convergence of molecular imaging and artificial intelligence that could reshape how we detect, visualize, and treat cancer. Led by David B. Olawade (University of East London) and an international research team, this work explores the emerging role of hNQO1-activatable NIR-II fluorescent probes in precision oncology.