科研之友
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摘要
Objective: Cholangiocarcinoma is a rare but highly malignant tumor. Hyperspectral imaging (HSI), which originated from remote sensing, is an emerging image modality for diagnosis and image-guided surgery. HSI takes the advantage of acquiring 2D images across a wide range of electromagnetic spectrum. HSI can obtain spectral and optical properties of tissue and provide more information than RGB images. Redundant information will persist even though HSI contains tens the amount of data compared with RGB images with the same spatial dimension. Traditional dimensionality reduction methods, such as principal component analysis and kernel method, reduce the data by converting the original spectral space to a low-dimensional one, which is not suitable in end-to-end models. Recently, convolutional neural networks have demonstrated excellent performance on computer vision tasks, including classification, segmentation, and detection. Attention mechanism is used in convolutional neural network(CNN) to improve the representation of feature maps. Typical channel attention modules, such as squeeze-and-excitation net (SENet), squeezes the input features by global average pooling to produce a channel descriptor. However, different channels could have the same mean value. We proposed frequency selecting channel attention (FSCA) mechanism to address this issue. An Inception-FSCA network is also proposed for the segmentation of a hyperspectral image of cholangiocarcinoma tissues. Method: FSCA can exploit the information from different frequency components. This method consists of three steps. First, the input feature map is transformed in the frequency domain by Fourier transform. Second, a representative frequency amplitude is selected to efficiently use the obtained frequencies. These selected frequencies are arranged in a column of vectors. Third, these vectors are sent to two consecutive fully connected layers to obtain a channel weight vector. Then, a sigmoid function is used to scale each channel weight between zero and one. Every element in the channel weight vector is multiplied with the corresponding channel feature. FSCA can adjust the channel information, strengthen the important channels, and suppress the unimportant. This work uses a microscopic hyperspectral imaging system to obtain hyperspectral images of cholangiocarcinoma tissues. These images have a spectral bandwidth from 550 nm to 1 000 nm in 7.5 nm increments, producing a hypercube with 60 spectral bands. Spatial resolution of each image is 1 280×1 024 pixels. The ground truth label is manually annotated by experts. The method is implemented using Python3.6 and TensorFlow1.14.0 on NVDIA TITAN X GPU, Intel i7-9700KF CPU. The learning rate is 0.000 5, the batch size is 256, and the optimization strategy is Adam. Cancerous areas have different sizes, resulting in unbalanced positive and negative samples. Focal loss is chosen as a loss function. Result: We conducted comparative and ablation experiments on our dataset. We use several evaluation metrics to evaluate the performance of the inception-FSCA. The accuracy, precision, sensitivity, specificity, and Kappa are 0.978 0, 0.965 4, 0.958 6, 0.985 2, and 0.945 6, respectively. Conclusion: In this study, we proposed a Fourier transform frequency selecting channel attention mechanism. The proposed channel attention module can be conveniently inserted in CNN. An Inception-FSCA network is built for the segmentation of hyperspectral images of cholangiocarcinoma tissues. Quantitative results show that our method has excellent performance. Inception-FSCA can be applied in the outer image segmentation and classification tasks.
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