5 research outputs found
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
Resolution-enhanced Digital Epiluminescence Microscopy Using Deep Computational Optics
Melanoma is the most common type of cancer, and the standard practice used for examining skin lesions is dermoscopy, where dermatologists use an epiluminescence microscope (ELM) to visualize the skin's surface and subsurface structures for anomalies. Conventional ELM instruments are being replaced by digital ELM instruments that enable dermatologists and other health care practitioners to digitally capture, archive, and analyze skin lesions using computer-aided diagnosis (CAD) software. One of the limiting factors of digital ELMs is a trade-off between spatial resolution and field of view (FOV), where a large FOV, which is needed to allow for larger skin lesions to be examined in their entirety, can be achieved by reducing magnification at the cost of spatial resolution (leading to a loss of fine details that can be indicative of malignancy and disease). In this thesis, we introduced the deep computation optics (DCO) framework for the purpose of resolution-enhanced digital ELM to improve the balance between spatial resolution and FOV. More specifically, the multitude of parameters of a deep computational model for numerically magnifying digital ELM images were learned through a wealth of low-resolution and high-resolution digital ELM image pairs. The proposed DCO approaches were experimentally validated, demonstrating improvements in the spatial resolution of the resolution-enhanced digital ELM when compared to more conventional methods, such as bicubic interpolation. Furthermore, we have demonstrated that the spatial resolution-enhancement improvements can be made within the deep computational models themselves where the model's receptive field is of the utmost importance since the missing information is better estimated when there is a larger number of neighbouring pixels involved