30 research outputs found
Going Deep in Medical Image Analysis: Concepts, Methods, Challenges and Future Directions
Medical Image Analysis is currently experiencing a paradigm shift due to Deep
Learning. This technology has recently attracted so much interest of the
Medical Imaging community that it led to a specialized conference in `Medical
Imaging with Deep Learning' in the year 2018. This article surveys the recent
developments in this direction, and provides a critical review of the related
major aspects. We organize the reviewed literature according to the underlying
Pattern Recognition tasks, and further sub-categorize it following a taxonomy
based on human anatomy. This article does not assume prior knowledge of Deep
Learning and makes a significant contribution in explaining the core Deep
Learning concepts to the non-experts in the Medical community. Unique to this
study is the Computer Vision/Machine Learning perspective taken on the advances
of Deep Learning in Medical Imaging. This enables us to single out `lack of
appropriately annotated large-scale datasets' as the core challenge (among
other challenges) in this research direction. We draw on the insights from the
sister research fields of Computer Vision, Pattern Recognition and Machine
Learning etc.; where the techniques of dealing with such challenges have
already matured, to provide promising directions for the Medical Imaging
community to fully harness Deep Learning in the future
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
Visual attention models and arse representations for morphometrical image analysis
Abstract. Medical diagnosis, treatment, follow-up and research activities are nowadays strongly supported on different types of diagnostic images, whose main goal is to provide an useful exchange of medical knowledge. This multi-modal information needs to be processed in order to extract information exploitable within the context of a particular medical task. In despite of the relevance of these complementary sources of medical knowledge, medical images are rarely further processed in actual clinical practice, so the specialists take decisions only based in the raw data. A new trend in the development of medical image processing and analysis tools follows the idea of biologically-inspired methods, which resemble the performance of the human vision system. Visual attention models and sparse representations are examples of this tendency. Based on this, the aim of this thesis was the development of a set of computational methods for automatic morph metrical analysis, combining the relevant region extraction power of visual attention models with the incorporation of a priori information capabilities of sparse representations. The combination of these biologically inspired tools with common machine learning techniques allowed the identification of visual patterns relevant for pathology discrimination, improving the accuracy and interpretability of morph metric measures and comparisons. After extensive validations with different image data sets, the computational methods proposed in this thesis seems to be promising tools for the definition of anatomical biomarkers, based on visual pattern analysis, and suitable for patient's diagnosis, prognosis and follow-up.Las actividades de diagn贸stico, tratamiento, seguimiento e investigaci贸n en medicina est谩n actualmente soportadas en diferentes clases de im谩genes diagn贸sticas, cuyo objetivo principal es el de proveer un intercambio efectivo de conocimiento m茅dico. Esta informaci贸n multimodal necesita ser procesada con el objetivo de extraer informaci贸n aprovechable en el contexto de una tarea m茅dica particular. A pesar de la relevancia de estas fuentes complementarias de informaci贸n cl铆nica, las im谩genes m茅dicas son raramente procesadas en la pr谩ctica cl铆nica actual, de forma que los especialistas s贸lo toman decisiones basados en los datos crudos. Una nueva tendencia en el desarrollo de herramientas de an谩lisis y procesamiento de im谩genes m茅dicas persigue la idea de m茅todos biol贸gicamente inspirados, que se asemejan al sistema de visi贸n humana. Son ejemplos de esta tendencia los modelos de atenci贸n visual y las representaciones escasas (sparse representations). Con base en esto, el objetivo de esta tesis fue el desarrollo de un conjunto de m茅todos computacionales para soportar autom谩ticamente los an谩lisis morfo m茅tricos, combinando el poder de extracci贸n de regiones relevantes de los modelos de atenci贸n visual junto con la capacidad de incorporaci贸n de informaci贸n a priori de las representaciones escasas. La combinaci贸n de estos m茅todos biol贸gicamente inspirados con t茅cnicas de aprendizaje de maquina facilito la identificaci贸n de patrones visuales relevantes para discriminar patolog铆as cerebrales, mejorando la precisi贸n e interpretabilidad de las medidas y comparaciones morfo m茅tricas. Despu茅s de extensivas validaciones con diferentes conjuntos de im谩genes, los m茅todos computacionales propuestos en esta tesis se perfilan como herramientas prometedoras para la definici贸n de biomarcadores anat贸micos, basados en el an谩lisis visual de patrones, y convenientes para el diagn贸stico, pron贸stico y seguimiento del paciente.Doctorad
Segmentation of colon glands by object graphs
Ankara : The Department of Computer Engineering and the Institute of Engineering and Science of Bilkent University, 2008.Thesis (Master's) -- Bilkent University, 2008.Includes bibliographical references leaves 72-79.Histopathological examination is the most frequently used technique for clinical
diagnosis of a large group of diseases including cancer. In order to reduce the
observer variability and the manual effort involving in this visual examination,
many computational methods have been proposed. These methods represent
a tissue with a set of mathematical features and use these features in further
analysis of the biopsy. For the tissue types that contain glandular structures, one
of these analyses is to examine the changes in these glandular structures. For
such analyses, the very first step is to segment the tissue into its glands.
In this thesis, we present an object-based method for the segmentation of
colon glands. In this method, we propose to decompose the image into a set of
primitive objects and use the spatial distribution of these objects to determine
the locations of glands. In the proposed method, pixels are first clustered into
different histological structures with respect to their color intensities. Then, the
clustered image is decomposed into a set of circular primitive objects (white
objects for luminal regions and black objects for nuclear regions) and a graph
is constructed on these primitive objects to quantify their spatial distribution.
Next, the features are extracted from this graph and these features are used to
determine the seed points of gland candidates. Starting from these seed points,
the inner glandular regions are grown considering the locations of black objects.
Finally, false glands are eliminated based on another set of features extracted from
the identified inner regions and exact boundaries of the remaining true glands are
determined considering the black objects that are located near the inner glandular
regions.
Our experiments on the images of colon biopsies have demonstrated that
our proposed method leads to high sensitivity, specificity, and accuracy rates.and that it greatly improves the performance of the previous pixel-based gland
segmentation algorithms. Our experiments have also shown that the object-based
structure of the method provides tolerance to artifacts resulting from variances
in biopsy staining and sectioning procedures. This proposed method offers an
infrastructure for further analysis of glands for the purpose of automated cancer
diagnosis and grading.Kandemir, MelihM.S
On Improving Generalization of CNN-Based Image Classification with Delineation Maps Using the CORF Push-Pull Inhibition Operator
Deployed image classification pipelines are typically dependent on the images captured in real-world environments. This means that images might be affected by different sources of perturbations (e.g. sensor noise in low-light environments). The main challenge arises by the fact that image quality directly impacts the reliability and consistency of classification tasks. This challenge has, hence, attracted wide interest within the computer vision communities. We propose a transformation step that attempts to enhance the generalization ability of CNN models in the presence of unseen noise in the test set. Concretely, the delineation maps of given images are determined using the CORF push-pull inhibition operator. Such an operation transforms an input image into a space that is more robust to noise before being processed by a CNN. We evaluated our approach on the Fashion MNIST data set with an AlexNet model. It turned out that the proposed CORF-augmented pipeline achieved comparable results on noise-free images to those of a conventional AlexNet classification model without CORF delineation maps, but it consistently achieved significantly superior performance on test images perturbed with different levels of Gaussian and uniform noise