The performance of any task depends on the representation of the data. A good representation should capture the factors of variation relevant to the task at hand while discarding the nuisance variables. Since this is task-specific, the common way to …
In-vivo optical microscopy is advancing into routine clinical practice for non-invasively guiding diagnosis and treatment of cancer and other diseases, and thus beginning to reduce the need for traditional biopsy. However, reading and analysis of the …
In vivo reflectance confocal microscopy (RCM) enables clinicians to examine lesions’ morphological and cytological information in epidermal and dermal layers, while reducing the need for biopsies. As RCM is being adopted more widely, the workflow is …
We describe a new multiresolution 'nested encoder-decoder' convolutional network architecture and use it to annotate morphological patterns in reflectance confocal microscopy (RCM) images of human skin for aiding cancer diagnosis. Skin cancers are …
Morphological tissue patterns in RCM images are critical in diagnosis of melanocytic lesions. We present a multiresolution deep learning framework that can automatically annotate RCM images for these diagnostic patterns with high sensitivity and …
Reflectance confocal microscopy (RCM) is an effective, non-invasive pre-screening tool for skin cancer diagnosis, but it requires extensive training and experience to assess accurately. There are few quantitative tools available to standardize image …
Reflectance confocal microscopy (RCM) is an effective, non-invasive pre-screening tool for cancer diagnosis. However, acquiring and reading RCM images requires extensive training and experience, and novice clinicians exhibit high variance in …
Measuring the thickness of the stratum corneum (SC) in vivo is often required in pharmacological, dermatological, and cosmetological studies. Reflectance confocal microscopy (RCM) offers a non-invasive imaging-based approach. However, RCM-based …