骨转移是转移性癌症患者最常见的疾病之一,如乳腺癌或前列腺癌。一种流行的诊断方法是骨闪烁扫描,其中扫描患者的整个身体。然而,扫描图像中出现的热点可能会产生误导,使得骨转移的准确可靠诊断成为挑战。人工智能可以作为决策支持工具发挥关键作用,以减轻在图像上生成手动注释的负担,从而防止医学专家的疏忽。到目前为止,几种最先进的卷积神经网络(CNN)已被用于解决骨转移诊断作为一个二元或多分类问题,达到足够的准确性(高于90%).然而,由于它们增加的复杂性(层数和自由参数),这些网络严重依赖于可用训练图像的数量,这些图像通常在医学领域内受到限制。我们的研究致力于使用一种新的深度学习架构,该架构通过使用卷积神经网络来克服计算负担,该网络的浮点运算(FLOP)和自由参数数量明显减少。实现了所提出的轻量级后视全卷积神经网络,并将其与几个著名的强大CNN进行了比较,例如ResNet50,VGG16,InceptionV3,Xception,和MobileNet在中等大小的成像数据集上(来自男性前列腺癌受试者的778张图像)。结果证明了所提出的方法在识别骨转移方面优于当前的最新技术。所提出的方法展示了彻底改变基于图像的诊断的独特潜力,从而为增强的癌症转移监测和治疗提供了新的可能性。
Bone metastasis is among the most frequent in diseases to patients suffering from metastatic cancer, such as breast or prostate cancer. A popular diagnostic method is bone scintigraphy where the whole body of the patient is scanned. However, hot spots that are presented in the scanned image can be misleading, making the accurate and reliable diagnosis of bone metastasis a challenge. Artificial intelligence can play a crucial role as a decision support tool to alleviate the burden of generating manual annotations on images and therefore prevent oversights by medical experts. So far, several state-of-the-art convolutional neural networks (CNN) have been employed to address bone metastasis diagnosis as a binary or multiclass classification problem achieving adequate accuracy (higher than 90%). However, due to their increased complexity (number of layers and free parameters), these networks are severely dependent on the number of available training images that are typically limited within the medical domain. Our study was dedicated to the use of a new deep learning architecture that overcomes the computational burden by using a convolutional neural network with a significantly lower number of floating-point operations (FLOPs) and free parameters. The proposed lightweight look-behind fully convolutional neural network was implemented and compared with several well-known powerful CNNs, such as ResNet50, VGG16, Inception V3, Xception, and MobileNet on an imaging dataset of moderate size (778 images from male subjects with prostate cancer). The results prove the superiority of the proposed methodology over the current state-of-the-art on identifying bone metastasis. The proposed methodology demonstrates a unique potential to revolutionize image-based diagnostics enabling new possibilities for enhanced cancer metastasis monitoring and treatment.