PDF(Pubmed)
Abstract：
Common bile duct (CBD) stones caused diseases are life-threatening. Because CBD stones locate in the distal part of the CBD and have relatively small sizes, detecting CBD stones from CT scans is a challenging issue in the medical domain.
We propose a deep learning based weakly-supervised method called multiple field-of-view based attention driven network (MFADNet) to detect CBD stones from CT scans based on image-level labels. Three dominant modules including a multiple field-of-view encoder, an attention driven decoder and a classification network are collaborated in the network. The encoder learns the feature of multi-scale contextual information while the decoder with the classification network is applied to locate the CBD stones based on spatial-channel attentions. To drive the learning of the whole network in a weakly-supervised and end-to-end trainable manner, four losses including the foreground loss, background loss, consistency loss and classification loss are proposed.
Compared with state-of-the-art weakly-supervised methods in the experiments, the proposed method can accurately classify and locate CBD stones based on the quantitative and qualitative results.
We propose a novel multiple field-of-view based attention driven network for a new medical application of CBD stone detection from CT scans while only image-levels are required to reduce the burdens of labeling and help physicians automatically diagnose CBD stones. The source code is available at https://github.com/nchucvml/MFADNet after acceptance.
Our deep learning method can help physicians localize relatively small CBD stones for effectively diagnosing CBD stone caused diseases.
We propose a deep learning based weakly-supervised method called multiple field-of-view based attention driven network (MFADNet) to detect CBD stones from CT scans based on image-level labels. Three dominant modules including a multiple field-of-view encoder, an attention driven decoder and a classification network are collaborated in the network. The encoder learns the feature of multi-scale contextual information while the decoder with the classification network is applied to locate the CBD stones based on spatial-channel attentions. To drive the learning of the whole network in a weakly-supervised and end-to-end trainable manner, four losses including the foreground loss, background loss, consistency loss and classification loss are proposed.
Compared with state-of-the-art weakly-supervised methods in the experiments, the proposed method can accurately classify and locate CBD stones based on the quantitative and qualitative results.
We propose a novel multiple field-of-view based attention driven network for a new medical application of CBD stone detection from CT scans while only image-levels are required to reduce the burdens of labeling and help physicians automatically diagnose CBD stones. The source code is available at https://github.com/nchucvml/MFADNet after acceptance.
Our deep learning method can help physicians localize relatively small CBD stones for effectively diagnosing CBD stone caused diseases.
摘要：
目的：胆总管（CBD）结石引起的疾病危及生命。因为CBD结石位于CBD的远端部分，并且尺寸相对较小，从CT扫描中检测CBD结石在医学领域是一个具有挑战性的问题。
方法：我们提出了一种基于深度学习的弱监督方法，称为基于多视场的注意力驱动网络（MFADNet），用于根据图像级标签从CT扫描中检测CBD结石。三个主要模块，包括多视场编码器，注意力驱动解码器和分类网络在网络中协作。编码器学习多尺度上下文信息的特征，而具有分类网络的解码器用于基于空间通道注意力定位CBD宝石。以弱监督和端到端可训练的方式驱动整个网络的学习，四个损失，包括前景损失，背景损失,提出了一致性损失和分类损失。
结果：与实验中最先进的弱监督方法相比，该方法可以根据定量和定性结果准确地对CBD结石进行分类和定位。
结论：我们提出了一种新颖的基于多视野的注意力驱动网络，用于从CT扫描中进行CBD结石检测的新医学应用，同时仅需要图像水平来减轻标签负担并帮助医生自动诊断CBD结石。源代码在接受后可在https://github.com/nchucvml/MFADNet获得。
结论：我们的深度学习方法可以帮助医生定位相对较小的CBD结石，以有效诊断CBD结石引起的疾病。
方法：我们提出了一种基于深度学习的弱监督方法，称为基于多视场的注意力驱动网络（MFADNet），用于根据图像级标签从CT扫描中检测CBD结石。三个主要模块，包括多视场编码器，注意力驱动解码器和分类网络在网络中协作。编码器学习多尺度上下文信息的特征，而具有分类网络的解码器用于基于空间通道注意力定位CBD宝石。以弱监督和端到端可训练的方式驱动整个网络的学习，四个损失，包括前景损失，背景损失,提出了一致性损失和分类损失。
结果：与实验中最先进的弱监督方法相比，该方法可以根据定量和定性结果准确地对CBD结石进行分类和定位。
结论：我们提出了一种新颖的基于多视野的注意力驱动网络，用于从CT扫描中进行CBD结石检测的新医学应用，同时仅需要图像水平来减轻标签负担并帮助医生自动诊断CBD结石。源代码在接受后可在https://github.com/nchucvml/MFADNet获得。
结论：我们的深度学习方法可以帮助医生定位相对较小的CBD结石，以有效诊断CBD结石引起的疾病。
