1,228 research outputs found
A Survey on Deep Learning in Medical Image Analysis
Deep learning algorithms, in particular convolutional networks, have rapidly
become a methodology of choice for analyzing medical images. This paper reviews
the major deep learning concepts pertinent to medical image analysis and
summarizes over 300 contributions to the field, most of which appeared in the
last year. We survey the use of deep learning for image classification, object
detection, segmentation, registration, and other tasks and provide concise
overviews of studies per application area. Open challenges and directions for
future research are discussed.Comment: Revised survey includes expanded discussion section and reworked
introductory section on common deep architectures. Added missed papers from
before Feb 1st 201
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 cancer. It remains challenging for computational approaches
to achieve performance comparable to experienced radiologists. Here we present
NoduleX, a systematic approach to predict lung nodule malignancy from CT data,
based on deep learning convolutional neural networks (CNN). For training and
validation, we analyze >1000 lung nodules in images from the LIDC/IDRI cohort.
All nodules were identified and classified by four experienced thoracic
radiologists who participated in the LIDC project. NoduleX achieves high
accuracy for nodule malignancy classification, with an AUC of ~0.99. This is
commensurate with the analysis of the dataset by experienced radiologists. Our
approach, NoduleX, provides an effective framework for highly accurate nodule
malignancy prediction with the model trained on a large patient population. Our
results are replicable with software available at
http://bioinformatics.astate.edu/NoduleX
Learning to detect chest radiographs containing lung nodules using visual attention networks
Machine learning approaches hold great potential for the automated detection
of lung nodules in chest radiographs, but training the algorithms requires vary
large amounts of manually annotated images, which are difficult to obtain. Weak
labels indicating whether a radiograph is likely to contain pulmonary nodules
are typically easier to obtain at scale by parsing historical free-text
radiological reports associated to the radiographs. Using a repositotory of
over 700,000 chest radiographs, in this study we demonstrate that promising
nodule detection performance can be achieved using weak labels through
convolutional neural networks for radiograph classification. We propose two
network architectures for the classification of images likely to contain
pulmonary nodules using both weak labels and manually-delineated bounding
boxes, when these are available. Annotated nodules are used at training time to
deliver a visual attention mechanism informing the model about its localisation
performance. The first architecture extracts saliency maps from high-level
convolutional layers and compares the estimated position of a nodule against
the ground truth, when this is available. A corresponding localisation error is
then back-propagated along with the softmax classification error. The second
approach consists of a recurrent attention model that learns to observe a short
sequence of smaller image portions through reinforcement learning. When a
nodule annotation is available at training time, the reward function is
modified accordingly so that exploring portions of the radiographs away from a
nodule incurs a larger penalty. Our empirical results demonstrate the potential
advantages of these architectures in comparison to competing methodologies
An Innovative Method for Lung Cancer Identification Using Machine Learning Algorithms
Biological community and the healthcare sector have greatly benefited from technological advancements in biomedical imaging. These advantages include early cancer identification and categorization, prognostication of patients' clinical outcomes following cancer surgery, and prognostication of survival for various cancer types. Medical professionals must spend a lot of time and effort gathering, analyzing, and evaluating enormous amounts of wellness data, such as scan results. Although radiologists spend a lot of time carefully reviewing several scans, tiny nodule diagnosis is incredibly prone to inaccuracy. Low dose computed tomography (LDCT) scans are used to categorize benign (Noncancerous) and malignant (Cancerous) nodules in order to study the issue of lung cancer (LC) diagnosis. Machine learning (ML), Deep learning (DL), and Artificial intelligence (AI) applications aid in the rapid identification of a number of infectious and malignant diseases, including lung cancer, using cutting-edge convolutional neural network (CNN) and Deep CNN architectures, we propose three unique detection models in this study: SEQUENTIAL 1 (Model-1), SEQUENTIAL 2 (Model-2), and transfer learning model Visual Geometry Group, VGG 16 (Model-3). The best accuracy model and methodology that are proposedas an effective and non-invasive diagnostic tool, outperforms other models trained with similar labels using lung CT scans to identify malignant nodules. Using a standard LIDC-IDRI data set that is freely available, the deep learning models are verified. The results of the experiment show a decrease in false positives while an increase in accuracy
Learning Algorithms for Fat Quantification and Tumor Characterization
Obesity is one of the most prevalent health conditions. About 30% of the world\u27s and over 70% of the United States\u27 adult populations are either overweight or obese, causing an increased risk for cardiovascular diseases, diabetes, and certain types of cancer. Among all cancers, lung cancer is the leading cause of death, whereas pancreatic cancer has the poorest prognosis among all major cancers. Early diagnosis of these cancers can save lives. This dissertation contributes towards the development of computer-aided diagnosis tools in order to aid clinicians in establishing the quantitative relationship between obesity and cancers. With respect to obesity and metabolism, in the first part of the dissertation, we specifically focus on the segmentation and quantification of white and brown adipose tissue. For cancer diagnosis, we perform analysis on two important cases: lung cancer and Intraductal Papillary Mucinous Neoplasm (IPMN), a precursor to pancreatic cancer. This dissertation proposes an automatic body region detection method trained with only a single example. Then a new fat quantification approach is proposed which is based on geometric and appearance characteristics. For the segmentation of brown fat, a PET-guided CT co-segmentation method is presented. With different variants of Convolutional Neural Networks (CNN), supervised learning strategies are proposed for the automatic diagnosis of lung nodules and IPMN. In order to address the unavailability of a large number of labeled examples required for training, unsupervised learning approaches for cancer diagnosis without explicit labeling are proposed. We evaluate our proposed approaches (both supervised and unsupervised) on two different tumor diagnosis challenges: lung and pancreas with 1018 CT and 171 MRI scans respectively. The proposed segmentation, quantification and diagnosis approaches explore the important adiposity-cancer association and help pave the way towards improved diagnostic decision making in routine clinical practice
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