Search Results for author:
Found 11 papers, 1 papers with code
Automated Segmentation and Recurrence Risk Prediction of Surgically Resected Lung Tumors with Adaptive Convolutional Neural Networks
Then, the identified tumors are passed to a second CNN for recurrence risk prediction.
Out of distribution detection for skin and malaria images
Deep neural networks have shown promising results in disease detection and classification using medical image data.
Estimation of BMI from Facial Images using Semantic Segmentation based Region-Aware Pooling
The recent works have either employed hand-crafted geometrical face features or face-level deep convolutional neural network features for face to BMI prediction.
Deep Detector Health Management under Adversarial Campaigns
Machine learning models are vulnerable to adversarial inputs that induce seemingly unjustifiable errors.
A Novel Extension to Fuzzy Connectivity for Body Composition Analysis: Applications in Thigh, Brain, and Whole Body Tissue Segmentation
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 radiation.
Lung and Pancreatic Tumor Characterization in the Deep Learning Era: Novel Supervised and Unsupervised Learning Approaches
We evaluate our proposed supervised and unsupervised learning algorithms on two different tumor diagnosis challenges: lung and pancreas with 1018 CT and 171 MRI scans, respectively, and obtain the state-of-the-art sensitivity and specificity results in both problems.
Deep Multi-Modal Classification of Intraductal Papillary Mucinous Neoplasms (IPMN) with Canonical Correlation Analysis
Intraductal Papillary Mucinous Neoplasms (IPMNs) are radiographically identifiable precursors to pancreatic cancer; hence, early detection and precise risk assessment of IPMN are vital.
How to Fool Radiologists with Generative Adversarial Networks? A Visual Turing Test for Lung Cancer Diagnosis
To address this challenge, radiologists need computer aided diagnosis (CAD) systems which can assist in learning discriminative imaging features corresponding to malignant and benign nodules.
Risk Stratification of Lung Nodules Using 3D CNN-Based Multi-task Learning
In order to address the need for a large amount for training data for CNN, we resort to transfer learning to obtain highly discriminative features.
TumorNet: Lung Nodule Characterization Using Multi-View Convolutional Neural Network with Gaussian Process
Characterization of lung nodules as benign or malignant is one of the most important tasks in lung cancer diagnosis, staging and treatment planning.
Context Driven Label Fusion for segmentation of Subcutaneous and Visceral Fat in CT Volumes
Quantification of adipose tissue (fat) from computed tomography (CT) scans is conducted mostly through manual or semi-automated image segmentation algorithms with limited efficacy.
