Chunyu Yuan

Chunyu Yuan, Dongfang Zhao, Sos S. Agaian

Skin cancer poses a significant public health challenge, necessitating efficient diagnostic tools. We introduce UCM-Net, a novel skin lesion segmentation model combining Multi-Layer Perceptrons (MLP) and Convolutional Neural Networks (CNN). This lightweight, efficient architecture, deviating from traditional UNet designs, dramatically reduces computational demands, making it ideal for mobile health applications. Evaluated on PH2, ISIC 2017, and ISIC 2018 datasets, UCM-Net demonstrates robust performance with fewer than 50KB parameters and requires less than 0.05 Giga Operations Per Second (GLOPs). Moreover, its minimal memory requirement is just 1.19MB in CPU environment positions. It is a potential benchmark for efficiency in skin lesion segmentation, suitable for deployment in resource-constrained settings. In order to facilitate accessibility and further research in the field, the UCM-Net source code is https://github.com/chunyuyuan/UCM-Net.

Chunyu Yuan, Dongfang Zhao, Sos S. Agaian

Skin lesion segmentation is key for early skin cancer detection. Challenges in automatic segmentation from dermoscopic images include variations in color, texture, and artifacts of indistinct lesion boundaries. Deep learning methods like CNNs and U-Net have shown promise in addressing these issues. To further aid early diagnosis, especially on mobile devices with limited computing power, we present MUCM-Net. This efficient model combines Mamba State-Space Models with our UCM-Net architecture for improved feature learning and segmentation. MUCM-Net's Mamba-UCM Layer is optimized for mobile deployment, offering high accuracy with low computational needs. Tested on ISIC datasets, it outperforms other methods in accuracy and computational efficiency, making it a scalable tool for early detection in settings with limited resources. Our MUCM-Net source code is available for research and collaboration, supporting advances in mobile health diagnostics and the fight against skin cancer. In order to facilitate accessibility and further research in the field, the MUCM-Net source code is https://github.com/chunyuyuan/MUCM-Net

Chunyu Yuan, Sos S. Agaian

Deep learning (DL) has recently changed the development of intelligent systems and is widely adopted in many real-life applications. Despite their various benefits and potentials, there is a high demand for DL processing in different computationally limited and energy-constrained devices. It is natural to study game-changing technologies such as Binary Neural Networks (BNN) to increase deep learning capabilities. Recently remarkable progress has been made in BNN since they can be implemented and embedded on tiny restricted devices and save a significant amount of storage, computation cost, and energy consumption. However, nearly all BNN acts trade with extra memory, computation cost, and higher performance. This article provides a complete overview of recent developments in BNN. This article focuses exclusively on 1-bit activations and weights 1-bit convolution networks, contrary to previous surveys in which low-bit works are mixed in. It conducted a complete investigation of BNN's development -from their predecessors to the latest BNN algorithms/techniques, presenting a broad design pipeline and discussing each module's variants. Along the way, it examines BNN (a) purpose: their early successes and challenges; (b) BNN optimization: selected representative works that contain essential optimization techniques; (c) deployment: open-source frameworks for BNN modeling and development; (d) terminal: efficient computing architectures and devices for BNN and (e) applications: diverse applications with BNN. Moreover, this paper discusses potential directions and future research opportunities in each section.

Nanyu Li, Yujuan Si, Duo Deng et al.

Recently, the Bag-Of-Word (BOW) algorithm provides efficient features and promotes the accuracy of the ECG classification system. However, BOW algorithm has two shortcomings: (1). it has large quantization errors and poor reconstruction performance; (2). it loses heart beat's time information, and may provide confusing features for different kinds of heart beats. Furthermore, ECG classification system can be used for long time monitoring and analysis of cardiovascular patients, while a huge amount of data will be produced, so we urgently need an efficient compression algorithm. In view of the above problems, we use the wavelet feature to construct the sparse dictionary, which lower the quantization error to a minimum. In order to reduce the complexity of our algorithm and adapt to large-scale heart beats operation, we combine the Online Dictionary Learning with Feature-sign algorithm to update the dictionary and coefficients. Coefficients matrix is used to represent ECG beats, which greatly reduces the memory consumption, and solve the problem of quantitative error simultaneously. Finally, we construct the pyramid to match coefficients of each ECG beat. Thus, we obtain the features that contain the beat time information by time stochastic pooling. It is efficient to solve the problem of losing time information. The experimental results show that: on the one hand, the proposed algorithm has advantages of high reconstruction performance for BOW, this storage method is high fidelity and low memory consumption; on the other hand, our algorithm yields highest accuracy in ECG beats classification; so this method is more suitable for large-scale heart beats data storage and classification.