Multiple Instance Learning in Medical Imaging

Multiple Instance Learning (MIL) is a weakly supervised machine learning approach that is particularly well suited to medical imaging tasks where detailed pixel- or region-level annotations are difficult, expensive, or impractical to obtain. In MIL, training data are organized into bags (e.g., whole-slide images or entire scans), each containing many instances (such as image patches), while labels are provided only at the bag level. The model learns to infer which instances are most relevant for the final prediction without requiring explicit instance-level labels.

In medical imaging, MIL has been widely applied to areas such as digital pathology, radiology, and cancer diagnosis, where only slide-level or scan-level diagnoses are available. By combining deep feature extraction with attention-based aggregation or pooling mechanisms, MIL models can identify clinically meaningful regions, improve diagnostic performance, and reduce annotation burden. This makes MIL a powerful and scalable framework for developing robust AI systems in real-world healthcare settings.

Видео Multiple Instance Learning in Medical Imaging канала Learn AI