In medical imaging, particularly in early disease detection and prognosis tasks, discerning the rationale behind an AI model's predictions is crucial for evaluating the reliability of its decisions. Conventional explanation methods face challenges in identifying discernible decisive features in medical image classifications, where discriminative features are subtle or not immediately apparent. To bridge this gap, we propose an explainable model that is equipped with both decision reasoning and feature identification capabilities. Our approach not only detects influential image patterns but also uncovers the decisive features that drive the model's final predictions. By implementing our method, we can efficiently identify and visualise class-specific features leveraged by the data-driven model, providing insights into the decision-making processes of deep learning models. We validated our model in the demanding realm of medical prognosis task, demonstrating its efficacy and potential in enhancing the reliability of AI in healthcare and in discovering new knowledge in diseases where prognostic understanding is limited.

Crossmodal knowledge distillation (KD) extends traditional knowledge distillation to the area of multimodal learning and demonstrates great success in various applications. To achieve knowledge transfer across modalities, a pretrained network from one modality is adopted as the teacher to provide supervision signals to a student network learning from another modality. In contrast to the empirical success reported in prior works, the working mechanism of crossmodal KD remains a mystery. In this paper, we present a thorough understanding of crossmodal KD. We begin with two case studies and demonstrate that KD is not a universal cure in crossmodal knowledge transfer. We then present the modality Venn diagram (MVD) to understand modality relationships and the modality focusing hypothesis (MFH) revealing the decisive factor in the efficacy of crossmodal KD. Experimental results on 6 multimodal datasets help justify our hypothesis, diagnose failure cases, and point directions to improve crossmodal knowledge transfer in the future. Knowledge distillation (KD) is an effective technique to transfer knowledge from one neural network to another (Wang & Yoon, 2021; Gou et al., 2021). Its core mechanism is a teacher-student learning framework, where the student network is trained to mimic the teacher through a loss. The loss function, initially proposed by (Hinton et al., 2015) as the KL divergence between teacher and student soft labels, has been extended in many ways (Zagoruyko & Komodakis, 2016; Tung & Mori, 2019; Park et al., 2019; Peng et al., 2019; Tian et al., 2019). KD has been successfully applied to various fields and demonstrates its high practical value. The wide applicability of KD stems from its generality: any student can learn from any teacher. To be more precise, the student and teacher network may differ in several ways. Three common scenarios are: (1) model capacity difference: Many works (Zagoruyko & Komodakis, 2016; Tung & Mori, 2019; Park et al., 2019; Peng et al., 2019) on model compression aim to learn a lightweight student matching the performance of its cumbersome teacher for deployment benefits.