IEEE BioCAS 2025 Grand Challenge on Pediatric Leukemia Diagnosis

The AI recognition of bone marrow\/blood cell images has become one of the important research directions in the field of medical imaging. By analyzing bone marrow images, doctors can quickly and accurately diagnose and monitor a range of blood diseases such as leukemia, anemia, and others. However, traditional bone marrow image analysis usually requires doctors to spend a lot of time and effort, and the results are subject to subjective factors, leading to issues such as inconsistent diagnoses.In recent years, with the development of artificial intelligence and machine learning technologies, AI recognition technology based on deep learning for bone marrow images has gradually matured. A large body of research indicates that using deep learning algorithms can effectively automatically identify abnormal cells, blood components, and structural features in bone marrow images, thereby assisting doctors in diagnosis and treatment decisions.However, despite significant progress in deep learning for bone marrow image recognition, there are still some challenges. These include difficulties in dataset annotation, insufficient generalization ability of models, and limitations in hardware resources. Especially when deploying large deep learning models on mobile medical devices and edge computing platforms, the limited availability of hardware resources becomes a constraining factor.In order to further promote the development and application of bone marrow image AI recognition technology, we will hold a challenge competition for bone marrow cell image AI recognition. This event aims to invite researchers, scholars, and medical experts from around the world to jointly explore and innovate bone marrow image AI recognition technology, improve the accuracy and efficiency of bone marrow disease diagnosis. Through this challenge competition, we hope to leverage artificial intelligence technology to bring faster, more accurate, and reliable diagnostic solutions to the field of medical imaging, provide better auxiliary tools for clinical doctors, and promote the development of healthcare.