885 research outputs found
An Interpretable Deep Hierarchical Semantic Convolutional Neural Network for Lung Nodule Malignancy Classification
While deep learning methods are increasingly being applied to tasks such as
computer-aided diagnosis, these models are difficult to interpret, do not
incorporate prior domain knowledge, and are often considered as a "black-box."
The lack of model interpretability hinders them from being fully understood by
target users such as radiologists. In this paper, we present a novel
interpretable deep hierarchical semantic convolutional neural network (HSCNN)
to predict whether a given pulmonary nodule observed on a computed tomography
(CT) scan is malignant. Our network provides two levels of output: 1) low-level
radiologist semantic features, and 2) a high-level malignancy prediction score.
The low-level semantic outputs quantify the diagnostic features used by
radiologists and serve to explain how the model interprets the images in an
expert-driven manner. The information from these low-level tasks, along with
the representations learned by the convolutional layers, are then combined and
used to infer the high-level task of predicting nodule malignancy. This unified
architecture is trained by optimizing a global loss function including both
low- and high-level tasks, thereby learning all the parameters within a joint
framework. Our experimental results using the Lung Image Database Consortium
(LIDC) show that the proposed method not only produces interpretable lung
cancer predictions but also achieves significantly better results compared to
common 3D CNN approaches
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
Towards automatic pulmonary nodule management in lung cancer screening with deep learning
The introduction of lung cancer screening programs will produce an
unprecedented amount of chest CT scans in the near future, which radiologists
will have to read in order to decide on a patient follow-up strategy. According
to the current guidelines, the workup of screen-detected nodules strongly
relies on nodule size and nodule type. In this paper, we present a deep
learning system based on multi-stream multi-scale convolutional networks, which
automatically classifies all nodule types relevant for nodule workup. The
system processes raw CT data containing a nodule without the need for any
additional information such as nodule segmentation or nodule size and learns a
representation of 3D data by analyzing an arbitrary number of 2D views of a
given nodule. The deep learning system was trained with data from the Italian
MILD screening trial and validated on an independent set of data from the
Danish DLCST screening trial. We analyze the advantage of processing nodules at
multiple scales with a multi-stream convolutional network architecture, and we
show that the proposed deep learning system achieves performance at classifying
nodule type that surpasses the one of classical machine learning approaches and
is within the inter-observer variability among four experienced human
observers.Comment: Published on Scientific Report
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
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