Computed tomography (CT) imaging is pervasively utilized for detecting tumors and internal body injuries. CT image retargeting means to horizontally/vertically shrink the semantically non-salient regions (e.g, the normal organs) while preserving the salient ones (e.g., the diseased organs) inside a CT image, as exemplified in Fig. 1. In practice, retargeting can substantially facilitate CT image displaying, which can benefit the subsequent medical treatment. In this work, we propose a bio-inspired CT image retargeting pipeline by mimicking human gaze behavior. More specifically, for each CT image, we extract the gaze shifting path (GSP) to capture human gaze distribution during the visual perception toward each CT image. Afterward, a multi-attribute binary hashing (MABH) is formulated to exploit the semantics of these GSPs. Thereby, each graphlet can be converted into the binary hash codes. Finally, the hash codes corresponding to GSP from each CT image are quantized into a feature vector, which is leveraged to learn a Gaussian mixture model (GMM) that guides CT image shrinking. In the experiments, to evaluate how gaze allocation influencing CT image retargeting, a user study is designed to compare the GSPs produced by normal observers and Alzheimer’s patients respectively. Besides, a comparative study has verified the superiority of our method.

Retargeting aims to shrink a photo wherein the perceptually prominent regions are appropriately kept. In practice, optimally shrinking a high resolution (HR) aerial photo is a useful tool for smart navigation. Nowadays, vehicle drivers’ path planning is generally guided by an HR aerial photo recommended by a navigation App like Google Maps. Owing to the limited and various resolution of vehicle displays, we have to retarget each original HR aerial photo accordingly, wherein the navigation-aware regions can be well preserved. In practice, HR aerial photo retargeting is non-trivial due to three challenges: 1) the rich number of internal objects and their complex spatial layouts, 2) deriving the region-level semantics from potentially contaminated image labels, and 3) the inefficiency of retargeting each HR aerial photo with millions of pixels. To handle these problems, we propose a novel HR aerial photo retargeting pipeline that can intelligently avoid the negative effects from incorrect image labels. The key is a noise-tolerant hashing algorithm that converts image-level semantics into the hash codes corresponding to different regions, which guides the HR aerial photo shrinking. More specifically, for each HR aerial photo, we extract visually/semantically salient object patches inside it. To explicitly encode their spatial layout, we construct a graphlet by linking the spatially adjacent object patches into a small graph. Subsequently, a binary matrix factorization (MF) is designed to exploit the underlying semantics of these graphlets, wherein three attributes: i) binary hash codes learning, ii) noisy labels refinement, iii) deep image-level semantics, are collaboratively encoded. Such binary MF can be solved iteratively and each graphlet is subsequently converted into the binary hash codes. Finally, the hash codes corresponding to graphlets within each HR aerial photo are utilized to learn a Gaussian mixture model (GMM) that optimizes the HR aerial photo retargeting. During the experimental validation, we compiled a smart navigation dataset including 132743 planned paths annotated from 10132 HR aerial photos, based on which comparative study has demonstrated the superiority of our method.

BackgroundMolecular information about bladder cancer is significant for treatment and prognosis. The immunohistochemistry (IHC) method is widely used to analyze the specific biomarkers to determine molecular subtypes. However, procedures in IHC and plenty of reagents are time and labor-consuming and expensive. This study established a computer-aid diagnosis system for predicting molecular subtypes, p53 status, and programmed death-ligand 1 (PD-L1) status of bladder cancer with pathological images. Materials and MethodsWe collected 119 muscle-invasive bladder cancer (MIBC) patients who underwent radical cystectomy from January 2016 to September 2018. All the pathological sections are scanned into digital whole slide images (WSIs), and the IHC results of adjacent sections were recorded as the label of the corresponding slide. The tumor areas are first segmented, then molecular subtypes, p53 status, and PD-L1 status of those tumor-positive areas would be identified by three independent convolutional neural networks (CNNs). We measured the performance of this system for predicting molecular subtypes, p53 status, and PD-L1 status of bladder cancer with accuracy, sensitivity, and specificity. ResultsFor the recognition of molecular subtypes, the accuracy is 0.94, the sensitivity is 1.00, and the specificity is 0.909. For PD-L1 status recognition, the accuracy is 0.897, the sensitivity is 0.875, and the specificity is 0.913. For p53 status recognition, the accuracy is 0.846, the sensitivity is 0.857, and the specificity is 0.750. ConclusionOur computer-aided diagnosis system can provide a novel and simple assistant tool to obtain the molecular subtype, PD-L1 status, and p53 status. It can reduce the workload of pathologists and the medical cost.

Accurately recognizing aerial photographs is a useful technique in many domains like autonomous driving and environmental evaluation. In practice, both low-resolution and high-resolution aerial photos are captured asynchronistically for each region, as there are hundreds of Earth observation satellites orbitting the Earth. Realizing such multiresolution-based region semantic understanding is a difficult task due to three challenges: 1) mimicking human visual perception when they actively viewing the semantic objects inside each aerial photo; 2) deeply modeling the visually/semantically salient objects sequentially perceived by human visual system; and 3) developing a cross-resolution knowledge transferal module to enhance the feature representation for an area. To solve these challenges, we propose a cross-domain aerial photograph categorization system by leveraging the low-resolution spatial composition to enhance the deep encoding of human gaze shifting path (GSP) with a high-resolution. More specifically, we first use an active learning algorithm to discover multiple visually/semantically salient object patches for constructing GSP from a high-resolution aerial photo. Then, an aggregation-based deep model is formulated to sequentially link the deep features learned from the object patches inside each GSP. Subsequently, a novel knowledge transferal algorithm leverages the global spatial composition from low-resolution counterparts to upgrade the deeply-learned GSP feature of the high-resolution aerial photo. Using the upgraded deep GSP feature, a multilabel SVM classifier is trained for categorizing aerial photographs. Comparative studies on our million-scale aerial photograph set have demonstrated the competitiveness of our approach.

Background: A pink color change occasionally found by us under magnifying endoscopy with narrow-band imaging (ME-NBI) may be a special feature of early gastric cancer (EGC), and was designated the "pink pattern". The purposes of this study were to determine the relationship between the pink pattern and the cytopathological changes in gastric cancer cells and whether the pink pattern is useful for the diagnosis of EGC.Methods: The color features of ME-NBI images and pathological images of cancerous gastric mucosal surfaces were extracted and quantified. The cosine similarity was calculated to evaluate the correlation between the pink pattern and the nucleus-to-cytoplasm ratio of cancerous epithelial cells. Two diagnostic tests were performed by 12 endoscopists using stored ME-NBI images of 185 gastric lesions to investigate the diagnostic efficacy of the pink pattern for EGC. The diagnostic values, such as the area under the curve (AUC), the accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), of test 1 and test 2 were compared.Results: The cosine similarity between the color values of ME-NBI images and pathological images of 20 lesions was at least 0.744. The median AUC, accuracy, sensitivity, specificity, PPV, and NPV of test 2 were significantly better than those of test 1 for all endoscopists and for the junior and experienced groups.Conclusions: The pink pattern observed in ME-NBI images correlated strongly with the change in the nucleus-to-cytoplasm ratio of gastric epithelial cells, and could be considered a useful marker for the diagnosis of differentiated EGC.