Triplet Contrastive Learning for Brain Tumor Classification
This work addresses the problem of accurate brain tumor classification for medical diagnosis, which is crucial for treatment planning, but it is incremental as it builds on existing deep learning and contrastive learning methods.
The paper tackled brain tumor classification by proposing a novel approach that learns deep embeddings using triplet loss variants and contrastive learning with rare-case data augmentation, achieving effectiveness across all metrics on a dataset with 27 tumor classes, including 13 rare ones.
Brain tumor is a common and fatal form of cancer which affects both adults and children. The classification of brain tumors into different types is hence a crucial task, as it greatly influences the treatment that physicians will prescribe. In light of this, medical imaging techniques, especially those applying deep convolutional networks followed by a classification layer, have been developed to make possible computer-aided classification of brain tumor types. In this paper, we present a novel approach of directly learning deep embeddings for brain tumor types, which can be used for downstream tasks such as classification. Along with using triplet loss variants, our approach applies contrastive learning to performing unsupervised pre-training, combined with a rare-case data augmentation module to effectively ameliorate the lack of data problem in the brain tumor imaging analysis domain. We evaluate our method on an extensive brain tumor dataset which consists of 27 different tumor classes, out of which 13 are defined as rare. With a common encoder during all the experiments, we compare our approach with a baseline classification-layer based model, and the results well prove the effectiveness of our approach across all measured metrics.