MTTM: Memory-Augmented with Mamba for 3D Medical Images Analysis

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Hongkai Wei, Yang Yang, Shijie Sun, Huansheng Song, Keyu Guo, Yongfeng Bu

Published: 2025, Last Modified: 12 Nov 2025ICASSP 2025EveryoneRevisionsBibTeXCC BY-SA 4.0
Abstract: The rapid advancement of artificial intelligence has propelled the healthcare industry into a new era of diagnostic precision. A pivotal component of this evolution is the accurate classification of 3D medical images, which necessitates extracting robust feature representations capable of effectively modeling long-range dependencies within the data. This paper introduces the Mamba Token Turing Machine (MTTM), a novel architecture that integrates the efficiency of Mamba with the memory mechanisms of the Token Turing Machine (TTM), effectively addressing limitations of Transformers in long-range dependency modeling. MTTM’s Memory-Augmented Processing Unit (MAPU) employs four blending methods, achieving state-of-the-art accuracy and efficiency on the MedMNIST v2 dataset, thereby advancing diagnostic precision in 3D medical image analysis. The code is available at https://github.com/hongkai-wei/MTTM.