Unfolding Explainable AI for Brain Tumor Segmentation

Brain tumor segmentation (BTS) has been studied from handcrafted engineered features to conventional machine learning (ML) methods, followed by the cutting-edge deep learning approaches. Each recent approach has attempted to overcome the challenges of previous methods and brought conveniences in efficacy, throughput, computation, explainability, investigation, and interpretability. Recently, deep learning (DL) algorithms show excellent performance regarding diverse fields, including image process, computer vision, health analytics, autonomous vehicles, and natural language processes; however, ultimately impediment in making the artificial intelligence explainable and interpretable to clinicians while dealing with critical health informatics and radiomics. Besides the sophisticated deep learning models for brain tumor segmentation, notorious notions like explainability, investigation, trust, and interpretability of DL raised significant concerns for clinicians in their domains. Among many DL methods, the neuro-symbolic learning (NSL) concept has gained more attention as it can contribute to explainable and interpretable AI. In the current study, we survey the prominent approaches, from handcrafted engineering conventional ML to deep learning algorithms, highlight the challenges in DL algorithms, and propose NSL architectures for BTS. Compared to existing surveys, our study not only outlines handcrafted to DL methods for BTS but also proposed explainable and interpretable pipelines appropriate for clinical practices. Our study can better facilitate novice learners in explainable AI and propose efficient, robust, interpretable DL models to facilitate the diagnosis, prognosis, and treatment of BTS.