Mamba2MIL: State Space Duality Based Multiple Instance Learning for Computational Pathology

• URL: http://arxiv.org/abs/2408.15032v1
• Date: Tue, 27 Aug 2024 13:01:19 GMT
• Title: Mamba2MIL: State Space Duality Based Multiple Instance Learning for Computational Pathology
• Authors: Yuqi Zhang, Xiaoqian Zhang, Jiakai Wang, Yuancheng Yang, Taiying Peng, Chao Tong,
• Abstract summary: We propose a novel Multiple Instance Learning framework called Mamba2MIL. Mamba2MIL exploits order-related and order-independent features, resulting in suboptimal utilization of sequence information. We conduct extensive experiments across multiple datasets, achieving improvements in nearly all performance metrics.
• Score: 17.329498427735565
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Computational pathology (CPath) has significantly advanced the clinical practice of pathology. Despite the progress made, Multiple Instance Learning (MIL), a promising paradigm within CPath, continues to face challenges, particularly related to incomplete information utilization. Existing frameworks, such as those based on Convolutional Neural Networks (CNNs), attention, and selective scan space state sequential model (SSM), lack sufficient flexibility and scalability in fusing diverse features, and cannot effectively fuse diverse features. Additionally, current approaches do not adequately exploit order-related and order-independent features, resulting in suboptimal utilization of sequence information. To address these limitations, we propose a novel MIL framework called Mamba2MIL. Our framework utilizes the state space duality model (SSD) to model long sequences of patches of whole slide images (WSIs), which, combined with weighted feature selection, supports the fusion processing of more branching features and can be extended according to specific application needs. Moreover, we introduce a sequence transformation method tailored to varying WSI sizes, which enhances sequence-independent features while preserving local sequence information, thereby improving sequence information utilization. Extensive experiments demonstrate that Mamba2MIL surpasses state-of-the-art MIL methods. We conducted extensive experiments across multiple datasets, achieving improvements in nearly all performance metrics. Specifically, on the NSCLC dataset, Mamba2MIL achieves a binary tumor classification AUC of 0.9533 and an accuracy of 0.8794. On the BRACS dataset, it achieves a multiclass classification AUC of 0.7986 and an accuracy of 0.4981. The code is available at https://github.com/YuqiZhang-Buaa/Mamba2MIL.

Related papers

• Mamba-CL: Optimizing Selective State Space Model in Null Space for Continual Learning [54.19222454702032]
  Continual Learning aims to equip AI models with the ability to learn a sequence of tasks over time, without forgetting previously learned knowledge. State Space Models (SSMs) have achieved notable success in computer vision. We introduce Mamba-CL, a framework that continuously fine-tunes the core SSMs of the large-scale Mamba foundation model.
  arXiv  Detail & Related papers  (2024-11-23T06:36:16Z)
• MSVM-UNet: Multi-Scale Vision Mamba UNet for Medical Image Segmentation [3.64388407705261]
  We propose a Multi-Scale Vision Mamba UNet model for medical image segmentation, termed MSVM-UNet. Specifically, by introducing multi-scale convolutions in the VSS blocks, we can more effectively capture and aggregate multi-scale feature representations from the hierarchical features of the VMamba encoder.
  arXiv  Detail & Related papers  (2024-08-25T06:20:28Z)
• Bidirectional Gated Mamba for Sequential Recommendation [56.85338055215429]
  Mamba, a recent advancement, has exhibited exceptional performance in time series prediction. We introduce a new framework named Selective Gated Mamba ( SIGMA) for Sequential Recommendation. Our results indicate that SIGMA outperforms current models on five real-world datasets.
  arXiv  Detail & Related papers  (2024-08-21T09:12:59Z)
• GroupMamba: Parameter-Efficient and Accurate Group Visual State Space Model [66.35608254724566]
  State-space models (SSMs) have showcased effective performance in modeling long-range dependencies with subquadratic complexity. However, pure SSM-based models still face challenges related to stability and achieving optimal performance on computer vision tasks. Our paper addresses the challenges of scaling SSM-based models for computer vision, particularly the instability and inefficiency of large model sizes.
  arXiv  Detail & Related papers  (2024-07-18T17:59:58Z)
• MambaMIL: Enhancing Long Sequence Modeling with Sequence Reordering in Computational Pathology [10.933433327636918]
  Multiple Instance Learning (MIL) has emerged as a dominant paradigm to extract discriminative feature representations within Whole Slide Images (WSIs) in computational pathology. In this paper, we incorporate the Selective Scan Space State Sequential Model (Mamba) in Multiple Instance Learning (MIL) for long sequence modeling with linear complexity. Our proposed framework performs favorably against state-of-the-art MIL methods.
  arXiv  Detail & Related papers  (2024-03-11T15:17:25Z)
• MamMIL: Multiple Instance Learning for Whole Slide Images with State Space Models [56.37780601189795]
  We propose a framework named MamMIL for WSI analysis. We represent each WSI as an undirected graph. To address the problem that Mamba can only process 1D sequences, we propose a topology-aware scanning mechanism.
  arXiv  Detail & Related papers  (2024-03-08T09:02:13Z)
• Heterogenous Memory Augmented Neural Networks [84.29338268789684]
  We introduce a novel heterogeneous memory augmentation approach for neural networks. By introducing learnable memory tokens with attention mechanism, we can effectively boost performance without huge computational overhead. We show our approach on various image and graph-based tasks under both in-distribution (ID) and out-of-distribution (OOD) conditions.
  arXiv  Detail & Related papers  (2023-10-17T01:05:28Z)
• Dual-Query Multiple Instance Learning for Dynamic Meta-Embedding based Tumor Classification [5.121989578393729]
  Whole slide image (WSI) assessment is a challenging and crucial step in cancer diagnosis and treatment planning. Coarse-grained labels are easily accessible, which makes WSI classification an ideal use case for multiple instance learning (MIL) We propose a novel embedding-based Dual-Query MIL pipeline (DQ-MIL)
  arXiv  Detail & Related papers  (2023-07-14T17:06:49Z)
• Neural Attentive Circuits [93.95502541529115]
  We introduce a general purpose, yet modular neural architecture called Neural Attentive Circuits (NACs) NACs learn the parameterization and a sparse connectivity of neural modules without using domain knowledge. NACs achieve an 8x speedup at inference time while losing less than 3% performance.
  arXiv  Detail & Related papers  (2022-10-14T18:00:07Z)
• TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classication [38.58585442160062]
  Multiple instance learning (MIL) is a powerful tool to solve the weakly supervised classification in whole slide image (WSI) based pathology diagnosis. We proposed a new framework, called correlated MIL, and provided a proof for convergence. We conducted various experiments for three different computational pathology problems and achieved better performance and faster convergence compared with state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-06-02T02:57:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.