We investigate the design of an entire mobile imaging system for early detection of melanoma. Different from previous work, we focus on smartphone-captured visible light images. Our design addresses two major challenges. First, images acquired using a smartphone under loosely-controlled environmental conditions may be subject to various distortions, and this makes melanoma detection more difficult. Second, processing performed on a smartphone is subject to stringent computation and memory constraints. In our work, we propose a detection system that is optimized to run entirely on the resourceconstrained smartphone. Our system intends to localize the skin lesion by combining a lightweight method for skin detection with a hierarchical segmentation approach using two fast segmentation methods. Moreover, we study an extensive set of image features and propose new numerical features to characterize a skin lesion. Furthermore, we propose an improved feature selection algorithm to determine a small set of discriminative features used by the final lightweight system. In addition, we study the human-computer interface (HCI) design to understand the usability and acceptance issues of the proposed system. Our extensive evaluation on an image dataset provided by National Skin Center -Singapore (117 benign nevi and 67 malignant melanoma) confirms the effectiveness of the proposed system for melanoma detection: 89.09% sensitivity at specificity ≥ 90%.
ObjectiveThe aim of this study is to identify prognostic imaging biomarkers and create a radiogenomics nomogram to predict overall survival (OS) in gastric cancer (GC).MaterialRNA sequencing data from 407 patients with GC and contrast-enhanced computed tomography (CECT) imaging data from 46 patients obtained from The Cancer Genome Atlas (TCGA) and The Cancer Imaging Archive (TCIA) were utilized to identify radiogenomics biomarkers. A total of 392 patients with CECT images from the Nanfang Hospital database were obtained to create and validate a radiogenomics nomogram based on the biomarkers.MethodsThe prognostic imaging features that correlated with the prognostic gene modules (selected by weighted gene coexpression network analysis) were identified as imaging biomarkers. A nomogram that integrated the radiomics score and clinicopathological factors was created and validated in the Nanfang Hospital database. Nomogram discrimination, calibration, and clinical usefulness were evaluated.ResultsThree prognostic imaging biomarkers were identified and had a strong correlation with four prognostic gene modules (P < 0.05, FDR < 0.05). The radiogenomics nomogram (AUC = 0.838) resulted in better performance of the survival prediction than that of the TNM staging system (AUC = 0.765, P = 0.011; Delong et al.). In addition, the radiogenomics nomogram exhibited good discrimination, calibration, and clinical usefulness in both the training and validation cohorts.ConclusionsThe novel prognostic radiogenomics nomogram that was constructed achieved excellent correlation with prognosis in both the training and validation cohort of Nanfang Hospital patients with GC. It is anticipated that this work may assist in clinical preferential treatment decisions and promote the process of precision theranostics in the future.
Planar object tracking (POT) is the basis of many indoor AR applications. However, there still lacks a systematic way to assess AR trackers. Existing benchmarks usually focus on the tracking accuracy of an algorithm without sufficient details about its sensitivity to various object properties and user behaviors, shedding limited light on possible resulting usability issues. We therefore propose a comprehensive POT benchmark system to understand the weakness of a tracker and derive cues for system improvement. We first identify a set of objects that are commonly used as indoor mobile AR markers and specify their vision-related properties. We then construct a video collection to record typical user interactions with these markers, and statistically quantify the consequent changes as a result of individual or multiple basic manipulations. Evaluation shows that this work can expose a tracker's sensitiveness to different object properties and user behaviors, drawing insights for system improvement and algorithm design..
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