Browsing by Author "Mishra, Rashika"
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Item Computer Aided Diagnosis Systems for Digital Analysis of Osteosarcoma and Skin Cancer(2019-12) Mishra, Rashika; 0000-0002-1812-1252 (Mishra, R); Daescu, OvidiuComputer-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.Item Viable and Necrotic Tumor Assessment from Whole Slide Images of Osteosarcoma Using Machine-Learning and Deep-Learning Models(Public Library of Science) Arunachalam, Harish Babu; Mishra, Rashika; Daescu, Ovidiu; Cederberg, K.; Rakheja, D.; Sengupta, A.; Leonard, D.; Hallac, R.; Leavey, P.; 0000-0001-8143-4107 (Arunachalam, HB); 0000-0002-0278-4174 (Daescu, O); 172508805 (Daescu, O); Arunachalam, Harish Babu; Mishra, Rashika; Daescu, OvidiuPathological estimation of tumor necrosis after chemotherapy is essential for patients with osteosarcoma. This study reports the first fully automated tool to assess viable and necrotic tumor in osteosarcoma, employing advances in histopathology digitization and automated learning. We selected 40 digitized whole slide images representing the heterogeneity of osteosarcoma and chemotherapy response. With the goal of labeling the diverse regions of the digitized tissue into viable tumor, necrotic tumor, and non-tumor, we trained 13 machine-learning models and selected the top performing one (a Support Vector Machine) based on reported accuracy. We also developed a deep-learning architecture and trained it on the same data set. We computed the receiver-operator characteristic for discrimination of non-tumor from tumor followed by conditional discrimination of necrotic from viable tumor and found our models performing exceptionally well. We then used the trained models to identify regions of interest on image-tiles generated from test whole slide images. The classification output is visualized as a tumor-prediction map, displaying the extent of viable and necrotic tumor in the slide image. Thus, we lay the foundation for a complete tumor assessment pipeline from original histology images to tumor-prediction map generation. The proposed pipeline can also be adopted for other types of tumor. ©2019 The Authors