Mamba-Based Ensemble learning for White Blood Cell Classification

• URL: http://arxiv.org/abs/2504.11438v1
• Date: Tue, 15 Apr 2025 17:53:18 GMT
• Title: Mamba-Based Ensemble learning for White Blood Cell Classification
• Abstract summary: White blood cell (WBC) classification assists in assessing immune health and diagnosing various diseases.<n>Recent advancements in deep learning have shown promise over traditional methods.<n>This paper introduces a novel framework that leverages Mamba models integrated with ensemble learning to improve WBC classification.
• Score: 6.09628686715121
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: White blood cell (WBC) classification assists in assessing immune health and diagnosing various diseases, yet manual classification is labor-intensive and prone to inconsistencies. Recent advancements in deep learning have shown promise over traditional methods; however, challenges such as data imbalance and the computational demands of modern technologies, such as Transformer-based models which do not scale well with input size, limit their practical application. This paper introduces a novel framework that leverages Mamba models integrated with ensemble learning to improve WBC classification. Mamba models, known for their linear complexity, provide a scalable alternative to Transformer-based approaches, making them suitable for deployment in resource-constrained environments. Additionally, we introduce a new WBC dataset, Chula-WBC-8, for benchmarking. Our approach not only validates the effectiveness of Mamba models in this domain but also demonstrates their potential to significantly enhance classification efficiency without compromising accuracy. The source code can be found at https://github.com/LewisClifton/Mamba-WBC-Classification.

Related papers

• Clustering-Guided Spatial-Spectral Mamba for Hyperspectral Image Classification [2.320960021702736]
  This paper presents CSSMamba (Clustering-guided Spatial-Spectral Mamba) framework to better address the challenges.<n>To achieve efficient and adaptive token sequences for improved Mamba performance, we integrate the clustering mechanism into a spatial Mamba architecture.<n>Experiments on the Pavia University, Indian Pines, and Liao-Ning 01 datasets demonstrate that CSSMamba achieves higher accuracy and better boundary preservation.
  arXiv  Detail & Related papers  (2026-01-22T16:47:07Z)
• Can Mamba Learn In Context with Outliers? A Theoretical Generalization Analysis [88.05636819649804]
  The Mamba model has gained significant attention for its computational advantages over Transformer-based models.<n>This paper presents the first theoretical analysis of the training dynamics of a one-layer Mamba model.<n>We show that although Mamba may require more training to converge, it maintains accurate predictions even when the proportion of outliers exceeds the threshold that a linear Transformer can tolerate.
  arXiv  Detail & Related papers  (2025-10-01T01:25:01Z)
• Trained Mamba Emulates Online Gradient Descent in In-Context Linear Regression [90.93281146423378]
  Mamba is an efficient Transformer alternative with linear complexity for long-sequence modeling.<n>Recent empirical works demonstrate Mamba's in-context learning (ICL) competitive with Transformers.<n>This paper studies the training dynamics of Mamba on the linear regression ICL task.
  arXiv  Detail & Related papers  (2025-09-28T09:48:49Z)
• Mammo-Mamba: A Hybrid State-Space and Transformer Architecture with Sequential Mixture of Experts for Multi-View Mammography [5.211860566766601]
  Mammo-Mamba is a novel framework that integrates Transformer-based attention, SSMs, and expert-driven feature refinement.<n>MambaVision is a modified MambaVision block that enhances representation learning in high-resolution mammographic images.<n>Mamba-Mamba achieves superior classification performance across all key metrics while maintaining computational efficiency.
  arXiv  Detail & Related papers  (2025-07-23T16:29:46Z)
• Mamba-OTR: a Mamba-based Solution for Online Take and Release Detection from Untrimmed Egocentric Video [57.805927523341516]
  Mamba-OTR is designed to exploit temporal recurrence during inference while being trained on short video clips.<n>Mamba-OTR achieves a noteworthy mp-mAP of 45.48 when operating in a sliding-window fashion.<n>We will publicly release the source code of Mamba-OTR to support future research.
  arXiv  Detail & Related papers  (2025-07-22T08:23:51Z)
• MambaMoE: Mixture-of-Spectral-Spatial-Experts State Space Model for Hyperspectral Image Classification [32.34863609876265]
  Mamba model has strong potential in hyperspectral image (HSI) classification. We propose MambaMoE, a novel spectral-spatial mixture-of-experts framework. We show that MambaMoE achieves state-of-the-art performance in both accuracy and efficiency.
  arXiv  Detail & Related papers  (2025-04-29T07:50:36Z)
• Binarized Mamba-Transformer for Lightweight Quad Bayer HybridEVS Demosaicing [21.15110217419682]
  We propose a lightweight Mamba-based binary neural network for efficient demosaicing of HybridEVS RAW images.<n>Bi-Mamba binarizes all projections while retaining the core Selective Scan in full precision.<n>We conduct quantitative and qualitative experiments to demonstrate the effectiveness of BMTNet in both performance and computational efficiency.
  arXiv  Detail & Related papers  (2025-03-20T13:32:27Z)
• TransMamba: Fast Universal Architecture Adaption from Transformers to Mamba [88.31117598044725]
  We explore cross-architecture training to transfer the ready knowledge in existing Transformer models to alternative architecture Mamba, termed TransMamba. Our approach employs a two-stage strategy to expedite training new Mamba models, ensuring effectiveness in across uni-modal and cross-modal tasks. For cross-modal learning, we propose a cross-Mamba module that integrates language awareness into Mamba's visual features, enhancing the cross-modal interaction capabilities of Mamba architecture.
  arXiv  Detail & Related papers  (2025-02-21T01:22:01Z)
• Mamba-SEUNet: Mamba UNet for Monaural Speech Enhancement [54.427965535613886]
  Mamba, as a novel state-space model (SSM), has gained widespread application in natural language processing and computer vision. In this work, we introduce Mamba-SEUNet, an innovative architecture that integrates Mamba with U-Net for SE tasks.
  arXiv  Detail & Related papers  (2024-12-21T13:43:51Z)
• The Mamba in the Llama: Distilling and Accelerating Hybrid Models [76.64055251296548]
  We show how to distill large Transformers into linear RNNs by reusing the linear projection weights from attention layers with academic GPU resources.<n>The resulting hybrid model achieves performance comparable to the original Transformer in chat benchmarks.<n>We also introduce a hardware-aware speculative decoding algorithm that accelerates the inference speed of Mamba and hybrid models.
  arXiv  Detail & Related papers  (2024-08-27T17:56:11Z)
• Vision Mamba for Classification of Breast Ultrasound Images [9.90112908284836]
  Mamba-based models, VMamba and Vim, are a recent family of vision encoders that offer promising performance improvements in many computer vision tasks. This paper compares Mamba-based models with traditional Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) using the breast ultrasound BUSI dataset and Breast Ultrasound B dataset.
  arXiv  Detail & Related papers  (2024-07-04T00:21:47Z)
• CLIP-Mamba: CLIP Pretrained Mamba Models with OOD and Hessian Evaluation [18.383760896304604]
  This report introduces the first attempt to train a Mamba model utilizing contrastive technical-image pretraining (CLIP) A Mamba model 67 million parameters is on par with a 307 million- parameters Vision Transformer (ViT) model in zero-shot classification tasks.
  arXiv  Detail & Related papers  (2024-04-30T09:40:07Z)
• MedMamba: Vision Mamba for Medical Image Classification [0.0]
  Vision transformers (ViTs) and convolutional neural networks (CNNs) have been extensively studied and widely used in medical image classification tasks. Recent studies have shown that state space models (SSMs) represented by Mamba can effectively model long-range dependencies. We propose MedMamba, the first Vision Mamba for generalized medical image classification.
  arXiv  Detail & Related papers  (2024-03-06T16:49:33Z)
• Swin-UMamba: Mamba-based UNet with ImageNet-based pretraining [85.08169822181685]
  This paper introduces a novel Mamba-based model, Swin-UMamba, designed specifically for medical image segmentation tasks. Swin-UMamba demonstrates superior performance with a large margin compared to CNNs, ViTs, and latest Mamba-based models.
  arXiv  Detail & Related papers  (2024-02-05T18:58:11Z)
• Is Mamba Capable of In-Context Learning? [63.682741783013306]
  State of the art foundation models such as GPT-4 perform surprisingly well at in-context learning (ICL) This work provides empirical evidence that Mamba, a newly proposed state space model, has similar ICL capabilities.
  arXiv  Detail & Related papers  (2024-02-05T16:39:12Z)
• Rectified Meta-Learning from Noisy Labels for Robust Image-based Plant Disease Diagnosis [64.82680813427054]
  Plant diseases serve as one of main threats to food security and crop production. One popular approach is to transform this problem as a leaf image classification task, which can be addressed by the powerful convolutional neural networks (CNNs) We propose a novel framework that incorporates rectified meta-learning module into common CNN paradigm to train a noise-robust deep network without using extra supervision information.
  arXiv  Detail & Related papers  (2020-03-17T09:51:30Z)

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.