In order to mine information from medical health data and develop intelligent applicationrelated issues, the multi-modal medical health data feature representation learning related content was studied, and several feature learning models were proposed for disease risk assessment. In the aspect of medical text feature learning, a medical text feature learning model based on convolutional neural network is proposed. The convolutional neural network text analysis technology is applied to the disease risk assessment application. The medical data feature representation adopts the deep learning method. The learning and extraction of different disease characteristics use the same method to realize the versatility of the model. A simple preprocessing of the experimental data samples, including its power frequency denoising and lead convolution regularization, constructs a convolutional neural network for medical data feature advancement and intelligent recognition. On the basis of it, several sets of experiments were carried out to discuss the influence of the convolution kernel and the choice of learning rate on the experimental results. In addition, comparative experiments with support vector machine, BP neural network and RBF neural network are carried out. The results show that the convolutional neural network used in this paper shows obvious advantages in recognition rate and training speed compared with other methods. In the aspect of time series data feature learning, a multi-channel convolutional self-encoding neural network is proposed. Analyze the connection between fatigue and emotional abnormalities and define the concept of emotional fatigue. The proposed multi-channel convolutional neural network is used to learn the data features, and the convolutional self-encoding neural network is used to learn the facial image data features. These two characteristics and the collected physiological data are combined to perform emotional fatigue detection. An emotional fatigue detection demonstration platform for multi-modal data feature fusion is established to realize data acquisition, emotional fatigue detection and emotional feedback. The experimental results verify the validity, versatility and stability of the model.

Purpose As affordable equipment, electronic portal imaging devices (EPIDs) are wildly used in radiation therapy departments to verify patients’ positions for accurate radiotherapy. However, these devices tend to produce visually ambiguous and low‐contrast planar digital radiographs under megavoltage x ray (MV‐DRs), which poses a tremendous challenge for clinicians to perform multimodal registration between the MV‐DRs and the kilovoltage digital reconstructed radiographs (KV‐DRRs) developed from the planning computed tomography. Furthermore, the existent of strong appearance variations also makes accurate registration beyond the reach of current automatic algorithms. Methods We propose a novel modality conversion approach to this task that first synthesizes KV images from MV‐DRs, and then registers the synthesized and real KV‐DRRs. We focus on the synthesis technique and develop a conditional generative adversarial network with information bottleneck extension (IB‐cGAN) that takes MV‐DRs and nonaligned KV‐DRRs as inputs and outputs synthesized KV images. IB‐cGAN is designed to address two main challenges in deep‐learning‐based synthesis: (a) training with a roughly aligned dataset suffering from noisy correspondences; (b) making synthesized images have real clinical meanings that faithfully reflects MV‐DRs rather than nonaligned KV‐DRRs. Accordingly, IB‐cGAN employs (a) an adversarial loss to provide training supervision at semantic level rather than the imprecise pixel level; (b) an IB to constrain the information from the nonaligned KV‐DRRs. Results We collected 2698 patient scans to train the model and 208 scans to test its performance. The qualitative results demonstrate realistic KV images can be synthesized allowing clinicians to perform the visual registration. The quantitative results show it significantly outperforms current nonmodality conversion methods by 22.37% (P = 0.0401) in terms of registration accuracy. Conclusions The modality conversion approach facilitates the downstream MV–KV registration for both clinicians and off‐the‐shelf registration algorithms. With this approach, it is possible to benefit the developing countries where inexpensive EPIDs are widely used for the image‐guided radiation therapy.

Radiotherapy has become a common treatment option for head and neck (H&N) cancer, and organs at risk (OARs) need to be delineated to implement a high conformal dose distribution. Manual drawing of OARs is time consuming and inaccurate, so automatic drawing based on deep learning models has been proposed to accurately delineate the OARs. However, state-of-the-art performance usually requires a decent amount of delineation, but collecting pixel-level manual delineations is labor intensive and may not be necessary for representation learning. Encouraged by the recent progress in self-supervised learning, this study proposes and evaluates a novel multiview contrastive representation learning to boost the models from unlabelled data. The proposed learning architecture leverages three views of CTs (coronal, sagittal, and transverse plane) to collect positive and negative training samples. Specifically, a CT in 3D is first projected into three 2D views (coronal, sagittal, and transverse planes), then a convolutional neural network takes 3 views as inputs and outputs three individual representations in latent space, and finally, a contrastive loss is used to pull representation of different views of the same image closer (“positive pairs”) and push representations of views from different images (“negative pairs”) apart. To evaluate performance, we collected 220 CT images in H&N cancer patients. The experiment demonstrates that our method significantly improves quantitative performance over the state-of-the-art (from 83% to 86% in absolute Dice scores). Thus, our method provides a powerful and principled means to deal with the label-scarce problem.