\section{Conclusion}
Our research aims to solve the constraints that currently impede the successful implementation of multiclass WSI MIL in real-world clinical settings. With the formulation of the class hierarchy in two alternative ways, our proposed method offers key components effective for each. Inter-hierarchy alignment of predictions across the vertical hierarchy contributes to improved performance. The predictions of the fine-grained hierarchy are influenced by the coarse-grained hierarchy, thus having fine probabilities adjusted to ensure consistency with the coarse. Implicit feature remix allows the model to understand diagnostic urgency in mixed-symptom inference environments, relying solely on a weak label. The results of the experiment have shown that the feature remix improved the quantitative performance and allowed it to focus on more prioritized diagnoses. Furthermore, we have explored the applicability of the class hierarchy, a novel concept in MIL, by comparing it with various methods. Our proposed method mitigates the challenges of multiclass MIL diagnosis of previous approaches, broadening its applicability to practical use in clinical settings.

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\noindent\textbf{Acknowledgment} This research was supported by the Seegene Medical Foundation, South Korea, under the project “Development of a Multimodal Artificial Intelligence-Based Computer-Aided Diagnosis System for Gastrointestinal Endoscopic Biopsies” (Grant Number: G01240151).

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\noindent\textbf{Disclosure of Interests} The authors have no competing interests to declare that are relevant to the content of this article.