Computer Aided Diagnosis Systems for Digital Analysis of Osteosarcoma and Skin Cancer




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Computer-aided detection/diagnosis (CAD) systems assist medical professionals in the interpretation of medical images. Many image modalities such as X-ray, MRI, and ultrasound already have diagnostics systems that process digital images for regions of interest and compute diagnostic patterns to provide supporting information in the decision making process for a possible diagnosis. The development of a CAD system is an interdisciplinary process combining computer vision algorithms/models with medical domain knowledge. Although such systems exist for radiology digital images, the advent of diagnosis systems in other modalities such as histology and dermoscopy is very recent. This dissertation focuses on the development of CAD systems for these modalities and includes (1) NAS: Deep Learning-Based Necrosis Assessment System for Osteosarcoma Histology Images, and (2) AlgoDerm: Deep Learning Framework for Skin Lesion Analysis and Tracking. Osteosarcoma is a type of bone cancer in children with an estimated 400-900 new cases each year in the United States. The current treatment plan for osteosarcoma involves a histopathology analysis after ten weeks of chemotherapy. Pathologists manually evaluate Haemotoxylin and Eosin (H&E) stained glass slides to estimate the percentage of tumor necrosis. Determination of the extent of tumor necrosis in a patient case can provide useful information for treatment outcome and prognosis. The manual process is time-consuming and is subject to observer bias, which could impact subsequent treatment procedures. The dissertation proposes a computational diagnosis framework with a custom deep learning model for digital histology images that can support the tumor analysis process, thereby saving time and providing a more objective evaluation. The proposed models achieve high accuracy in the necrosis estimation task and produce a tumor guide map with viable and necrotic tumor for pathologists. Skin cancer is one of the most prominent skin diseases, with 1 in 5 Americans being diagnosed with skin cancer in their lifetime. In the past few years, various computer-aided diagnosis systems have been proposed to facilitate accurate diagnosis of skin cancer but most are limited to cancer diagnosis from dermoscopic images in a clinical setting, with only a few considering cell phone/clinical photos. Smartphones equipped with applications for analyzing skin lesions can detect skin cancer early and increase the survival rate. The second part of this dissertation proposes a cell phone application where the patient uses a cell phone at home to take pictures of skin lesions and has the images interpreted by a specialized CAD system. Such an application aims to save time and money, with only patients that need further investigation through biopsy or cross-examination having to visit a medical office. Successful implementation and validation of this application can also increase the availability of skilled systems and extend the reach of dermatologists to remote areas. The proposed application shows high accuracy for both dermoscopy and digital images. The research presented in this dissertation focuses on the development of methods for effective representation and classification of regions of interest in histology and dermoscopy datasets using state of the art techniques.



Pathology, Osteosarcoma, Tumors -- Classification, Tumor necrosis factor, Skin -- Cancer, Skin -- Biopsy, Diagnostic imaging


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