权重剪枝-UNet：用于肾肿瘤语义分割的带深度可分离卷积的权重剪枝UNet
Weight Pruning-UNet: Weight Pruning UNet with Depth-wise Separable Convolutions for Semantic Segmentation of Kidney Tumors.
作者信息
Rao Patike Kiran, Chatterjee Subarna, Sharma Sreedhar
机构信息
Department of Computer Science and Engineering, MS Ramaiah University of Applied Sciences, Bengaluru, Karnataka, India.
Department of Computer Science and Engineering, Faculty of Engineering and Technology, MS Ramaiah University of Applied Sciences, Bengaluru, Karnataka, India.
出版信息
J Med Signals Sens. 2022 May 12;12(2):108-113. doi: 10.4103/jmss.jmss_108_21. eCollection 2022 Apr-Jun.
BACKGROUND
Accurate semantic segmentation of kidney tumors in computed tomography (CT) images is difficult because tumors feature varied forms and occasionally, look alike. The KiTs19 challenge sets the groundwork for future advances in kidney tumor segmentation.
METHODS
We present weight pruning (WP)-UNet, a deep network model that is lightweight with a small scale; it involves few parameters with a quick assumption time and a low floating-point computational complexity.
RESULTS
We trained and evaluated the model with CT images from 210 patients. The findings implied the dominance of our method on the training Dice score (0.98) for the kidney tumor region. The proposed model only uses 1,297,441 parameters and 7.2e floating-point operations, three times lower than those for other network models.
CONCLUSIONS
The results confirm that the proposed architecture is smaller than that of UNet, involves less computational complexity, and yields good accuracy, indicating its potential applicability in kidney tumor imaging.
背景
计算机断层扫描（CT）图像中肾肿瘤的精确语义分割具有挑战性，因为肿瘤形态各异，有时甚至外观相似。KiTs19挑战赛为肾肿瘤分割的未来进展奠定了基础。
方法
我们提出了权重剪枝（WP）-UNet，这是一种轻量级的深度网络模型，规模小；它涉及的参数较少，假设时间短，浮点计算复杂度低。
结果
我们使用来自210名患者的CT图像对该模型进行了训练和评估。结果表明，我们的方法在肾肿瘤区域的训练骰子分数（0.98）方面具有优势。所提出的模型仅使用1,297,441个参数和7.2e浮点运算，比其他网络模型低三倍。
结论
结果证实，所提出的架构比UNet小，计算复杂度更低，并且具有良好的准确性，表明其在肾肿瘤成像中的潜在适用性。
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