Discovery Radiomics via Deep Multi-Column Radiomic Sequencers for Skin Cancer Detection

While skin cancer is the most diagnosed form of cancer in men and women, with more cases diagnosed each year than all other cancers combined, sufficiently early diagnosis results in very good prognosis and as such makes early detection crucial. While radiomics have shown considerable promise as a powerful diagnostic tool for significantly improving oncological diagnostic accuracy and efficiency, current radiomics-driven methods have largely rely on pre-defined, hand-crafted quantitative features, which can greatly limit the ability to fully characterize unique cancer phenotype that distinguish it from healthy tissue. Recently, the notion of discovery radiomics was introduced, where a large amount of custom, quantitative radiomic features are directly discovered from the wealth of readily available medical imaging data. In this study, we present a novel discovery radiomics framework for skin cancer detection, where we leverage novel deep multi-column radiomic sequencers for high-throughput discovery and extraction of a large amount of custom radiomic features tailored for characterizing unique skin cancer tissue phenotype. The discovered radiomic sequencer was tested against 9,152 biopsy-proven clinical images comprising of different skin cancers such as melanoma and basal cell carcinoma, and demonstrated sensitivity and specificity of 91% and 75%, respectively, thus achieving dermatologist-level performance and \break hence can be a powerful tool for assisting general practitioners and dermatologists alike in improving the efficiency, consistency, and accuracy of skin cancer diagnosis

A Deep-structured Conditional Random Field Model for Object Silhouette Tracking

In this work, we introduce a deep-structured conditional random field (DS-CRF) model for the purpose of state-based object silhouette tracking. The proposed DS-CRF model consists of a series of state layers, where each state layer spatially characterizes the object silhouette at a particular point in time. The interactions between adjacent state layers are established by inter-layer connectivity dynamically determined based on inter-frame optical flow. By incorporate both spatial and temporal context in a dynamic fashion within such a deep-structured probabilistic graphical model, the proposed DS-CRF model allows us to develop a framework that can accurately and efficiently track object silhouettes that can change greatly over time, as well as under different situations such as occlusion and multiple targets within the scene. Experiment results using video surveillance datasets containing different scenarios such as occlusion and multiple targets showed that the proposed DS-CRF approach provides strong object silhouette tracking performance when compared to baseline methods such as mean-shift tracking, as well as state-of-the-art methods such as context tracking and boosted particle filtering.Comment: 17 page

One-pot preparation of N,N′-alkylidene bisamide derivatives catalyzed by silica supported polyphosphoric acid (SiO2-PPA)

AbstractSilica supported polyphosphoric acid (SiO2-PPA) as an efficient heterogeneous catalyst was found to be effective for the one-pot three-component condensation reaction of phenyl acetylene/1-hexyne, aromatic aldehyde and benzamide/acetamide to produce a series of N,N′-alkylidene bisamides. The desired products were obtained in good to high yields. The assistance of alkynes has been confirmed by using thin layer chromatographic (TLC) studies. All the reactions were done at 100 °C using 0.025 g of catalyst. The developed method is valid for either substituted aldehyde, thus it constitutes a general synthetic method for these kinds of compounds. In all the cases aromatic aldehydes containing electron-withdrawing groups gave shorter time than that with electron-donating groups. Additionally, the reaction of butyraldehyde with benzamide failed to have any product in the presence of phenyl acetylene but with 1-hexyne the product was formed in moderate yield