291 citations · 501 across the 10 of their papers we have counts for
13 papers
Joint Stream: Malignant Region Learning for Breast Cancer Diagnosis
Abdul Rehman, Sarfaraz Hussein, Waqas Sultani
Early diagnosis of breast cancer (BC) significantly contributes to reducing the mortality rate worldwide. The detection of different factors and biomarkers such as Estrogen recepto…
Automated Segmentation and Recurrence Risk Prediction of Surgically Resected Lung Tumors with Adaptive Convolutional Neural Networks
Marguerite B. Basta, Sarfaraz Hussein, Hsiang Hsu +1
Lung cancer is the leading cause of cancer related mortality by a significant margin. While new technologies, such as image segmentation, have been paramount to improved detection…
Out of distribution detection for skin and malaria images
Muhammad Zaida, Shafaqat Ali, Mohsen Ali +3
Deep neural networks have shown promising results in disease detection and classification using medical image data. However, they still suffer from the challenges of handling real-…
Estimation of BMI from Facial Images using Semantic Segmentation based Region-Aware Pooling
Nadeem Yousaf, Sarfaraz Hussein, Waqas Sultani
Body-Mass-Index (BMI) conveys important information about one's life such as health and socio-economic conditions. Large-scale automatic estimation of BMIs can help predict several…
Deep Detector Health Management under Adversarial Campaigns
Javier Echauz, Keith Kenemer, Sarfaraz Hussein +4
Machine learning models are vulnerable to adversarial inputs that induce seemingly unjustifiable errors. As automated classifiers are increasingly used in industrial control system…
Intelligent Wide-band Spectrum Classifier
M. O. Mughal, Behrad Toghi, Sarfaraz Hussein +1
We introduce a new technique for narrow-band (NB) signal classification in sparsely populated wide-band (WB) spectrum using supervised learning approach. For WB spectrum acquisitio…
A Novel Extension to Fuzzy Connectivity for Body Composition Analysis: Applications in Thigh, Brain, and Whole Body Tissue Segmentation
Ismail Irmakci, Sarfaraz Hussein, Aydogan Savran +7
Magnetic resonance imaging (MRI) is the non-invasive modality of choice for body tissue composition analysis due to its excellent soft tissue contrast and lack of ionizing radiatio…
Lung and Pancreatic Tumor Characterization in the Deep Learning Era: Novel Supervised and Unsupervised Learning Approaches
Sarfaraz Hussein, Pujan Kandel, Candice W. Bolan +2
Risk stratification (characterization) of tumors from radiology images can be more accurate and faster with computer-aided diagnosis (CAD) tools. Tumor characterization through suc…
Deep Multi-Modal Classification of Intraductal Papillary Mucinous Neoplasms (IPMN) with Canonical Correlation Analysis
Sarfaraz Hussein, Pujan Kandel, Juan E. Corral +3
Pancreatic cancer has the poorest prognosis among all cancer types. Intraductal Papillary Mucinous Neoplasms (IPMNs) are radiographically identifiable precursors to pancreatic canc…
How to Fool Radiologists with Generative Adversarial Networks? A Visual Turing Test for Lung Cancer Diagnosis
Maria J. M. Chuquicusma, Sarfaraz Hussein, Jeremy Burt +1
Discriminating lung nodules as malignant or benign is still an underlying challenge. To address this challenge, radiologists need computer aided diagnosis (CAD) systems which can a…
Risk Stratification of Lung Nodules Using 3D CNN-Based Multi-task Learning
Sarfaraz Hussein, Kunlin Cao, Qi Song +1
Risk stratification of lung nodules is a task of primary importance in lung cancer diagnosis. Any improvement in robust and accurate nodule characterization can assist in identifyi…
TumorNet: Lung Nodule Characterization Using Multi-View Convolutional Neural Network with Gaussian Process
Sarfaraz Hussein, Robert Gillies, Kunlin Cao +2
Characterization of lung nodules as benign or malignant is one of the most important tasks in lung cancer diagnosis, staging and treatment planning. While the variation in the appe…
Context Driven Label Fusion for segmentation of Subcutaneous and Visceral Fat in CT Volumes
Sarfaraz Hussein, Aileen Green, Arjun Watane +3
Quantification of adipose tissue (fat) from computed tomography (CT) scans is conducted mostly through manual or semi-automated image segmentation algorithms with limited efficacy.…