将EfficientNet扩展为向量值模型，提升医学图像分类效率与精度。
V-EfficientNets: Vector-Valued Efficiently Scaled Convolutional Neural Network Models
- 基于向量值设计改进EfficientNet，显式建模通道间关系
- 在ALL-IDB2数据集上达99.46%准确率，参数显著减少
- 适合需要高效处理多维信号的医疗图像分析场景
EfficientNet模型通过协同平衡网络宽度、深度和分辨率，实现参数分配的优化，以卓越的准确性成为图像分类任务的标准。传统神经网络在训练中学习特征通道间的相关性，而向量值神经网络则天然将多维数据视为整体，默认保留通道间关系。本文提出向量值EfficientNets（V-EfficientNets），一种用于处理任意向量值数据的新模型。在急性淋巴细胞白血病检测任务中，V-EfficientNets在ALL-IDB2数据集上达到平均99.46%的准确率。该模型展现出显著效率优势，在减少参数的同时超越现有最优模型，包括原始EfficientNet。源代码已公开于https://github.com/mevalle/v-nets。
原文摘要 · Abstract (English)
EfficientNet models are convolutional neural networks optimized for parameter allocation by jointly balancing network width, depth, and resolution. Renowned for their exceptional accuracy, these models have become a standard for image classification tasks across diverse computer vision benchmarks. While traditional neural networks learn correlations between feature channels during training, vector-valued neural networks inherently treat multidimensional data as coherent entities, taking for granted the inter-channel relationships. This paper introduces vector-valued EfficientNets (V-EfficientNets), a novel extension of EfficientNet designed to process arbitrary vector-valued data. The proposed models are evaluated on a medical image classification task, achieving an average accuracy of 99.46% on the ALL-IDB2 dataset for detecting acute lymphoblastic leukemia. V-EfficientNets demonstrate remarkable efficiency, significantly reducing parameters while outperforming state-of-the-art models, including the original EfficientNet. The source code is available at https://github.com/mevalle/v-nets.
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