Classification of Hematoma: Joint Learning of Semantic Segmentation and Classification

TL;DR

This paper proposes a joint learning approach combining semantic segmentation and classification to improve the accuracy of detecting rapidly growing cerebral hematomas in CT images, addressing challenges like data imbalance and deformability.

Contribution

It introduces a novel joint learning framework that enhances hematoma classification accuracy by leveraging segmentation, tackling issues like small datasets and deformable objects.

Findings

Improved classification accuracy over plain CNN models.

Effective handling of small and imbalanced datasets.

Enhanced detection of deformable hematomas.

Abstract

Cerebral hematoma grows rapidly in 6-24 hours and misprediction of the growth can be fatal if it is not operated by a brain surgeon. There are two types of cerebral hematomas: one that grows rapidly and the other that does not grow rapidly. We are developing the technique of artificial intelligence to determine whether the CT image includes the cerebral hematoma which leads to the rapid growth. This problem has various difficulties: the few positive cases in this classification problem of cerebral hematoma and the targeted hematoma has deformable object. Other difficulties include the imbalance classification, the covariate shift, the small data, and the spurious correlation problems. It is difficult with the plain CNN classification such as VGG. This paper proposes the joint learning of semantic segmentation and classification and evaluate the performance of this.