Multi-instance learning based on spatial continuous category representation for case-level meningioma grading in MRI images.

Meningiomas have the highest incidence rate of all primary intracranial and central nervous system tumors. Accurate preoperative grading of meningiomas is extraordinarily meaningful for treatment strategy and patient prognosis. Magnetic resonance imaging (MRI) is the most common method for meningioma grading. Existing methods are typically two-stage models and require image-level classifications or region of interest (ROI) annotations for assistant diagnosis, thereby adding massive manual annotations and time costs. Meanwhile, most of these methods use only a single MRI slice, which may lose the overall meningioma information and are inconsistent with the actual clinical diagnosis process. To address the above problems, a multi-instance learning (MIL) method based on spatial continuous category representation is proposed for case-level meningioma grading. It considers the MRI case and corresponding slices as a bag and instances, respectively, and requires only a case-level label to diagnose a patient. To make the most of the serialization characteristics of MRI images, this method selects continuous instance-feature sequences under each category that are most suspected to contain tumors and further integrates these sequences into bag-level features for classification. In addition, an end-to-end meningioma grading architecture is designed to support the proposed MIL method, which directly takes the original MRI images of the patient as input and outputs the meningioma grade prediction. To train and evaluate the proposed method, datasets with different slice thicknesses are constructed. The experimental results demonstrate that our method performs satisfactorily compared with other related methods for meningioma grading. [ABSTRACT FROM AUTHOR]