Lung and pancreatic tumor characterization in the deep learning era: novel supervised and unsupervised learning approaches
Risk stratification (characterization) of tumors from radiology images can be more accurate
and faster with computer-aided diagnosis (CAD) tools. Tumor characterization through such …
and faster with computer-aided diagnosis (CAD) tools. Tumor characterization through such …
Highly accurate model for prediction of lung nodule malignancy with CT scans
Computed tomography (CT) examinations are commonly used to predict lung nodule
malignancy in patients, which are shown to improve noninvasive early diagnosis of lung …
malignancy in patients, which are shown to improve noninvasive early diagnosis of lung …
FUIQA: fetal ultrasound image quality assessment with deep convolutional networks
The quality of ultrasound (US) images for the obstetric examination is crucial for accurate
biometric measurement. However, manual quality control is a labor intensive process and …
biometric measurement. However, manual quality control is a labor intensive process and …
Risk stratification of lung nodules using 3D CNN-based multi-task learning
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 identifying …
Any improvement in robust and accurate nodule characterization can assist in identifying …
Dense deconvolutional network for skin lesion segmentation
Automatic delineation of skin lesion contours from dermoscopy images is a basic step in the
process of diagnosis and treatment of skin lesions. However, it is a challenging task due to …
process of diagnosis and treatment of skin lesions. However, it is a challenging task due to …
Multi-task deep model with margin ranking loss for lung nodule analysis
Lung cancer is the leading cause of cancer deaths worldwide and early diagnosis of lung
nodule is of great importance for therapeutic treatment and saving lives. Automated lung …
nodule is of great importance for therapeutic treatment and saving lives. Automated lung …
Ultrasound standard plane detection using a composite neural network framework
Ultrasound (US) imaging is a widely used screening tool for obstetric examination and
diagnosis. Accurate acquisition of fetal standard planes with key anatomical structures is …
diagnosis. Accurate acquisition of fetal standard planes with key anatomical structures is …
Class-specific mutual information variation for feature selection
Feature selection plays a critical role in pattern recognition. Feature selection aims to
eliminate irrelevant and redundant features. A drawback of traditional feature selection …
eliminate irrelevant and redundant features. A drawback of traditional feature selection …
Automatic scoring of multiple semantic attributes with multi-task feature leverage: a study on pulmonary nodules in CT images
The gap between the computational and semantic features is the one of major factors that
bottlenecks the computer-aided diagnosis (CAD) performance from clinical usage. To bridge …
bottlenecks the computer-aided diagnosis (CAD) performance from clinical usage. To bridge …
Stochastic filter groups for multi-task cnns: Learning specialist and generalist convolution kernels
The performance of multi-task learning in Convolutional Neural Networks (CNNs) hinges on
the design of feature sharing between tasks within the architecture. The number of possible …
the design of feature sharing between tasks within the architecture. The number of possible …