Prompt Deep Light-Weight Vessel Segmentation Network (PLVS-Net)

Arsalan, Muhammad; Khan, Tariq M.; Naqvi, Syed S.; Nawaz, Mehmood; Razzak, Imran

doi:10.1109/tcbb.2022.3211936

Cited by 22 publications

(8 citation statements)

References 63 publications

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“…Nonetheless, its inference speed is low. G-Net Light [44], PLVS-Net [48], and MKIS-Net [49] are effective CNN architectures for segmenting retinal blood vessels, while also being lightweight.…”

Section: Related Workmentioning

confidence: 99%

“…For comparative analysis, we include several wellestablished methods, namely MobileNet-V3-small [86], ERFNet [87], MultiRes UNet [88], VessNet [89], PLVS-Net [48], M2U-Net [60], and G-Net Light [44]. These methods have been widely recognized in the field and are commonly used as benchmarks for performance evaluation.…”

Section: A Comparison With Sota Lightweight Networkmentioning

confidence: 99%

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MDA-Net: Multiscale dual attention-based network for breast lesion segmentation using ultrasound images

Iqbal

Sharif

2022

Journal of King Saud University - Computer and Information Scie

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Section: Related Workmentioning

confidence: 99%

Section: A Comparison With Sota Lightweight Networkmentioning

confidence: 99%

MDA-Net: Multiscale dual attention-based network for breast lesion segmentation using ultrasound images

Iqbal

Sharif

2022

Journal of King Saud University - Computer and Information Scie

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“…MMS-Net [15] is specifically designed for pixel-level detection, incorporating multipath and multiscale convolutional operations, as well as multiple deep feature aggregation techniques. PLVS-Net was proposed for accurate segmentation of retinal vessels by [9]. Using prompt blocks, PLVS-Net achieves improved feature extraction and segmentation performance with fewer than a million parameters, making it suitable for resource-constrained devices.…”

Section: Related Workmentioning

confidence: 99%

“…boundaries, inherent uncertainty in segmented regions, diverse textures, nonuniform intensity distribution, and significant contrast variations commonly encountered in medical images [5], [6], [7], [8], [9], [10]. These complexities emphasise the importance of developing advanced segmentation techniques to facilitate clinical diagnosis.…”

mentioning

confidence: 99%

“…For example, MobileNet-V3 [32] has a large number of pooling kernels and a large stride, which limit its memory footprint but contribute to its low performance when segmenting high-frequency features. T-Net [12], GLAN [16], G-Net [13], PLVS-Net [9], and MKIS-Net [14], on the other hand, employ small filter kernels to extract lowfrequency information, but due to their shallow structures, they perform poorly when dealing with datasets that exhibit significant feature variation. Hence, an architecture is required that can extract robust features for all inherent frequencies in medical images, encode feature variations for varying datasets and conform with the computational requirements of POC diagnosis.…”

mentioning

confidence: 99%

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Leveraging Image Complexity in Macro-Level Neural Network Design for Medical Image Segmentation

Khan

Naqvi

Meijering

2022

Preprint

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Recent progress in encoder-decoder neural network architecture design has led to significant performance improvements in a wide range of medical image segmentation tasks. However, state-of-the-art networks for a given task may be too computationally demanding to run on affordable hardware, and thus users often resort to practical workarounds by modifying various macro-level design aspects. Two common examples are downsampling of the input images and reducing the network depth or size to meet computer memory constraints. In this paper, we investigate the effects of these changes on segmentation performance and show that image complexity can be used as a guideline in choosing what is best for a given dataset. We consider four statistical measures to quantify image complexity and evaluate their suitability on ten different public datasets. For the purpose of our illustrative experiments, we use DeepLabV3+ (deep large-size), M2U-Net (deep lightweight), U-Net (shallow large-size), and U-Net Lite (shallow lightweight). Our results suggest that median frequency is the best complexity measure when deciding on an acceptable input down-sampling factor and using a deep versus shallow, large-size versus lightweight network. For high-complexity datasets, a lightweight network running on the original images may yield better segmentation results than a large-size network running on downsampled images, whereas the opposite may be the case for low-complexity images.

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BranchFusionNet: An energy-efficient lightweight framework for superior retinal vessel segmentation

Qin,

Xiao

2024

Peer-to-Peer Netw. Appl.

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Prompt Deep Light-Weight Vessel Segmentation Network (PLVS-Net)

Cited by 22 publications

References 63 publications

MDA-Net: Multiscale dual attention-based network for breast lesion segmentation using ultrasound images

MDA-Net: Multiscale dual attention-based network for breast lesion segmentation using ultrasound images

Leveraging Image Complexity in Macro-Level Neural Network Design for Medical Image Segmentation

BranchFusionNet: An energy-efficient lightweight framework for superior retinal vessel segmentation

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