Colposcopic multimodal fusion for the classification of cervical lesions.

Objective: Cervical cancer is one of the two biggest killers of women and early detection of cervical precancerous lesions can effectively improve the survival rate of patients. Manual diagnosis by combining colposcopic images and clinical examination results is the main clinical diagnosis method at present. Developing an intelligent diagnosis algorithm based on artificial intelligence is an inevitable trend to solve the objectification of diagnosis and improve the quality and efficiency of diagnosis.Approach: A colposcopic multimodal fusion convolutional neural network (CMF-CNN) was proposed for the classification of cervical lesions. Mask region convolutional neural network was used to detect the cervical region while the encoding network EfficientNet-B3 was introduced to extract the multimodal image features from the acetic image and iodine image. Finally, Squeeze-and-Excitation, Atrous Spatial Pyramid Pooling, and convolution block were also adopted to encode and fuse the patient's clinical text information.Main results: The experimental results showed that in 7106 cases of colposcopy, the accuracy, macro F1-score, macro-areas under the curve of the proposed model were 92.70%, 92.74%, 98.56%, respectively. They are superior to the mainstream unimodal image classification models.Significance: CMF-CNN proposed in this paper combines multimodal information, which has high performance in the classification of cervical lesions in colposcopy, so it can provide comprehensive diagnostic aid.

A Foreground-Aware Framework for Local Face Attribute Transfer.

In the context of social media, large amounts of headshot photos are taken everyday. Unfortunately, in addition to laborious editing and modification, creating a visually compelling photographic masterpiece for sharing requires advanced professional skills, which are difficult for ordinary Internet users. Though there are many algorithms automatically and globally transferring the style from one image to another, they fail to respect the semantics of the scene and are unable to allow users to merely transfer the attributes of one or two face organs in the foreground region leaving the background region unchanged. To overcome this problem, we developed a novel framework for semantically meaningful local face attribute transfer, which can flexibly transfer the local attribute of a face organ from the reference image to a semantically equivalent organ in the input image, while preserving the background. Our method involves warping the reference photo to match the shape, pose, location, and expression of the input image. The fusion of the warped reference image and input image is then taken as the initialized image for a neural style transfer algorithm. Our method achieves better performance in terms of inception score (3.81) and Fréchet inception distance (80.31), which is about 10% higher than those of competitors, indicating that our framework is capable of producing high-quality and photorealistic attribute transfer results. Both theoretical findings and experimental results are provided to demonstrate the efficacy of the proposed framework, reveal its superiority over other state-of-the-art alternatives.

Unsupervised Exemplar-Domain Aware Image-to-Image Translation.

Image-to-image translation is used to convert an image of a certain style to another of the target style with the original content preserved. A desired translator should be capable of generating diverse results in a controllable many-to-many fashion. To this end, we design a novel deep translator, namely exemplar-domain aware image-to-image translator (EDIT for short). From a logical perspective, the translator needs to perform two main functions, i.e., feature extraction and style transfer. With consideration of logical network partition, the generator of our EDIT comprises of a part of blocks configured by shared parameters, and the rest by varied parameters exported by an exemplar-domain aware parameter network, for explicitly imitating the functionalities of extraction and mapping. The principle behind this is that, for images from multiple domains, the content features can be obtained by an extractor, while (re-)stylization is achieved by mapping the extracted features specifically to different purposes (domains and exemplars). In addition, a discriminator is equipped during the training phase to guarantee the output satisfying the distribution of the target domain. Our EDIT can flexibly and effectively work on multiple domains and arbitrary exemplars in a unified neat model. We conduct experiments to show the efficacy of our design, and reveal its advances over other state-of-the-art methods both quantitatively and qualitatively.

[Temporal and Spatial Variation Patterns of the Environmental Elements in the Sediments of the Liaohe Estuary and the Related Influencing Factors].

Remote sensing images, field survey data, and historical monitoring data are collected to analyze the historical change trend and spatial variation rules of sediment quality of the Liaohe Estuary and further investigate the effects of sea-area utilization type and vegetation succession stage on the variation patterns of environmental elements in the sediments. The results showed that the quality of the sediments in the Liaohe Estuary is overall favorable, and the average contents of various elements all satisfy the quality standards of first-grade sediments. The nutritive elements in the sediments exhibit obvious zonal distribution patterns, while heavy metals are randomly distributed and exhibit no obvious patterns. The effect of sea-area utilization type on the enrichment of pollutants in the sediments exhibits certain significant differences, suggesting that the distributions of heavy metals and pollutants are subject to human activities and exhibit certain randomness. The main control factors are significantly different at different vegetation succession stages. Specifically, the vegetation distribution in the initial succession stage is mainly affected by salinity; with the transition from halophytic vegetation to terrestrial vegetation, the inherent correlation between vegetation distribution characteristics and the contents of total organic carbon, total nitrogen, and total phosphorus increase gradually. Meanwhile, the content of heavy-metal pollutants exhibits no significantly inherent correlation between the distributions of the plant community.