Correctly classifying a skin lesion is one of the first steps towards treatment. We propose a novel convolutional neural network (CNN) architecture for skin lesion classification designed to learn based on information from multiple image resolutions while leveraging pretrained CNNs. While traditional CNNs are generally trained on a single resolution image, our CNN is composed of multiple tracts...

We have developed a family of quantitative descriptors in order to provide noninvasive, reliable means of distinguishing benign from malignant breast lesions. These include acoustic descriptors (“echogenicity,” “heterogeneity,” “shadowing”) and morphometric descriptors (“area,” “aspect ratio,” “border irregularity,” “margin definition”). These quantitative descriptors are designed to be indepen...

We have developed a family of quantitative descriptors in order to provide noninvasive, reliable means of distinguishing benign from malignant breast lesions. These include acoustic descriptors (“echogenicity,” “heterogeneity,” “shadowing”) and morphometric descriptors (“area,” “aspect ratio,” “border irregularity,” “margin definition”). These quantitative descriptors are designed to be indepen...

Accurate detection of the borders of skin lesions is a vital first step for computer aided diagnostic systems. This paper presents a novel automatic approach to segmentation of skin lesions that is particularly suitable for analysis of dermoscopic images. Assumptions about the image acquisition, in particular, the approximate location and color, are used to derive an automatic rule to select sm...

Developing an automatic system for detection, segmentation, and classification of skin lesions is very useful to aid well-timed diagnosis diseases. Lesion segmentation a crucial task automated cancers, as it affects significantly the accuracy subsequent steps. Varieties in sizes locations lesions, with low-contrast boundaries make this challenging. In paper, three-stage CNN-based method present...

In this study, a multi-task deep neural network is proposed for skin lesion analysis. The proposed multi-task learning model solves different tasks (e.g., lesion segmentation and two independent binary lesion classifications) at the same time by exploiting commonalities and differences across tasks. This results in improved learning efficiency and potential prediction accuracy for the task-spec...

the histopathologic diagnosis of orbital and ocular adnexal lymphoproliferative lesions is difficult, resulting controversy in classification, determining benignity or malignancy of them and treatment modality selection. we designed the following study to evaluate clinical, histopathologic and if necessary immunochemical features of them in decreasing indeterminate cases. the study includes 51 ...

introduction: striae distensae (sd) are a frequent skin condition for which treatment remains a challenge. the 1540-nm non-ablative fractional laser (star lux 500) has been shown to improve atrophic scars by increasing the amount of dermal collagen. to assess the safety and efficacy of the star lux 500 laser in the treatment of mature hypopigmented striae in persian people (striae alba). method...

this case is a 19-year-old soldier who suffered a combined elbow dislocation and posterior monteggia fracture and dislocation (type ii). the ulna fracture was managed by orif. the elbow dislocation was managed by closed reduction and immobilization in 900 flexion. the posteriorly dislocated radial head was kept reduced by a transarticular pin. after 6 months elbow and forearm motion was restric...

There have been considerable recent advances in the understanding and management of femoroacetabular impingement and associated labral and chondral pathology. We have developed a classification system for acetabular chondral lesions. In our system, we use the six acetabular zones previously described by Ilizaliturri et al. The cartilage is then graded on a scale of 0 to 4 as follows: grade 0, n...