Recent research shows that a multitasking approach to AI training allows models to be trained faster and more cost-effectively with less data. This is especially true in the context of data scarcity in medical imaging.
According to the World Health Organization, the number of cancer cases worldwide has increased significantly. Biomarkers that help with accurate diagnosis and successful treatment can be detected in pathological images using artificial intelligence.
Researchers from the Fraunhofer MEVIS Institute for Digital Medicine, RWTH Aachen University, University of Regensburg and Hannover Medical School have developed an effective artificial intelligence model that analyzes tissue samples quickly and reliably using only a portion of the usual training data.
This approach allows the AI model to benefit from pre-training and automatic data annotation, reducing time and resource costs. New AI models are expected to improve disease diagnosis and treatment in clinical practice.
Source: Ferra
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