Learning Classifiers for Computer Aided Diagnosis Using Local Correlations Glenn Fung, Computer-Aided Diagnosis and Therapy Siemens Medical Solutions, Inc. Collaborators: Volkan Vural, Jennifer Dy [Northeastern University] Murat Dundar, Balaji Krishnapuram, Bharat Rao [Siemens] Feb 13, 2008
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Learning Classifiers for Computer Aided Diagnosis Using Local Correlations
Learning Classifiers for Computer Aided Diagnosis Using Local Correlations. Glenn Fung, Computer-Aided Diagnosis and Therapy Siemens Medical Solutions, Inc. Collaborators: Volkan Vural, Jennifer Dy [Northeastern University] Murat Dundar, Balaji Krishnapuram, Bharat Rao [Siemens] - PowerPoint PPT Presentation
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Learning Classifiers for Computer Aided Diagnosis Using Local Correlations
Glenn Fung, Computer-Aided Diagnosis and TherapySiemens Medical Solutions, Inc.
Collaborators: Volkan Vural, Jennifer Dy [Northeastern University]Murat Dundar, Balaji Krishnapuram, Bharat Rao [Siemens]
For computer to “see” (or do) what medical experts see (or do)- To automate routine, mind-numbing, and time-consuming tasks;- To improve consistency (by reducing intra- and inter-expert variability);
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For computer to “see” what doctors may miss - To improve sensitivity for disease detection and diagnosis;- To perform quantitative assessment not achievable by “eyeballing” or “guesstimate”;
“Segmentation is the partition of a digital image into multiple regions (sets of pixels), according to some criterion.” – wikipedia.org
At the low level, the criterion can be uniformity, which is determined according to pixel intensity, texture (repetitive patterns), etc.
At a semantic level, the criterion can be object(s) and the background.
In medical imaging, it usually refers to the delineation of different tissues or organs.
Computer-Aided Intelligent Imaging InterpretatonBasic Tools and Approaches
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DetectionDetection
Detection is the process of finding one or more object or region of interest.
In medical imaging, detection of abnormalities is often a primary goal. Examples include the detection of lung nodules, colon polyps, or breast lesions, all of which can be precursors to cancer; or the detection of abnormality of the brain (e.g., Alzheimer's disease) or pathological deformation of the heart (e.g., ventricular enlargement).
Computer-Aided Intelligent Imaging InterpretatonBasic Tools and Approaches
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ClassificationClassification
Classification is the separation of objects into different classes.
In medical imaging, classification is often performed on a tissue or organ to distinguish between its healthy and diseased state, or different stages of the disease.
A classifier is often trained using a training set, where one or more experts have assigned labels to a set of objects.
Computer-Aided Intelligent Imaging InterpretatonBasic Tools and Approaches
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• More and more data available,
• It is the prediction and early detection of diseases that saves most lives. However, “early” usually means more subtle signs and weaker signals in the images. Doctor often use a complex set of features that are often hard to formulate in computational forms;
• If doctors miss them, who will teach the computer?
• How do we know that we are doing better, if doctors do not agree among themselves?
1. Lung cancer is the most commonly diagnosed cancer worldwide, accounting for 1.2 million new cases annually. Lung cancer is an exceptionally deadly disease: 6 out of 10 people will die within one year of being diagnosed
2. The expected 5-year survival rate for all patients with a diagnosis of lung cancer is merely 15%
3. In the United States, lung cancer is the leading cause of cancer death for both men and women, causes more deaths than the next three most common cancers combined, and costs $9.6 Billion to treat annually.
4. However, lung cancer prognosis varies greatly depending on how early the disease is diagnosed; as with all cancers, early detection provides the best prognosis.
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1. Every pulmonary nodule, independent of size and location may be malign and needs to be looked at (20 - 50% of resected nodules are malignant)
2. The smaller the nodule the better the prognosis after nodule resection with respect to 5 year survival rate
3. There is need for a screening method, as it is already available for mammography.
The need for lung CAD
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CAD in plain words :
Find nodules in a large volume data set- solitary or attached to anatomical structures
Segment nodules correctly- remove structures like vessel, bronchus and pleura consistently and anatomically correct
Shortcomings in standard classification algorithms
Tend to underestimate minority class when problems are very unbalanced
Assume that the training examples or instances are drawn identically and independently from an underlying unknown distribution
Assume that the appropriate measure for evaluating the classifiers is based only on the accuracy of the system on a per-lesion basis
Correct classification of every candidate instance is the main goal, instead of the ability to detect at least one candidate to points to each malignant lesion.
We are hiring!Research Scientists (Machine Learning / Probabilistic Inference)Entry Level to Senior Level Opportunities
Computer-Aided Diagnosis & Therapy Solutions GroupSiemens Medical Solutions USA, Inc.Multiple open positions for candidates with a Ph.D. (or graduating with a PhD in ‘07)
to perform leading-edge R&D in activities involving all areas of probabilistic inference (Bayesian methods, temporal reasoning, graphical models) and/or machine learning (classification, statistical learning theory, optimization). We seek outstanding scientists who can solve challenging medical problems and continue to publish in leading journals and conferences.
Qualifications:Ph.D. in CS/EE/Statistics/Applied Math or an engineering discipline with an
interdisciplinary background.Strong publication record in leading conferences and journals in machine learning /
probabilistic inference.The ability to learn new technologies and apply them to challenging problems
involving reasoning from incomplete and unstructured medical patient data, classification of patients/diseases, as well as machine learning for automatically extracting patterns from massive amounts of free text, numeric, imaging, and symbolic data; combine imaging and clinical information; and other related areas. NLP is a plus.
We are located in Malvern, PA, less than an hour from Center City Philadelphia in the suburban Main Line area. Siemens offers a competitive salary and benefits package that reflects our leadership status.