6,701 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
Case-based lung image categorization and retrieval for interstitial lung diseases: clinical workflows
Purpose: Clinical workflows and user interfaces of image-based computer-aided diagnosis (CAD) for interstitial lung diseases in high-resolution computed tomography are introduced and discussed. Methods: Three use cases are implemented to assist students, radiologists, and physicians in the diagnosis workup of interstitial lung diseases. Results: In a first step, the proposed system shows a three-dimensional map of categorized lung tissue patterns with quantification of the diseases based on texture analysis of the lung parenchyma. Then, based on the proportions of abnormal and normal lung tissue as well as clinical data of the patients, retrieval of similar cases is enabled using a multimodal distance aggregating content-based image retrieval (CBIR) and text-based information search. The global system leads to a hybrid detection-CBIR-based CAD, where detection-based and CBIR-based CAD show to be complementary both on the user's side and on the algorithmic side. Conclusions: The proposed approach is in accordance with the classical workflow of clinicians searching for similar cases in textbooks and personal collections. The developed system enables objective and customizable inter-case similarity assessment, and the performance measures obtained with a leave-one-patient-out cross-validation (LOPO CV) are representative of a clinical usage of the syste
On Classification with Bags, Groups and Sets
Many classification problems can be difficult to formulate directly in terms
of the traditional supervised setting, where both training and test samples are
individual feature vectors. There are cases in which samples are better
described by sets of feature vectors, that labels are only available for sets
rather than individual samples, or, if individual labels are available, that
these are not independent. To better deal with such problems, several
extensions of supervised learning have been proposed, where either training
and/or test objects are sets of feature vectors. However, having been proposed
rather independently of each other, their mutual similarities and differences
have hitherto not been mapped out. In this work, we provide an overview of such
learning scenarios, propose a taxonomy to illustrate the relationships between
them, and discuss directions for further research in these areas
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