Learning Classifiers for Computer Aided Diagnosis Using Local Correlations Glenn Fung, Computer-Aided Diagnosis and Therapy Siemens Medical Solutions,

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

2 ©2007 Siemens Medical Solutions. All rights reserved. Computer-aided Diagnosis & Therapy Outline Brief Overview of CAD systems Assumption in traditional classifier design are Often, not valid in CAD problems Convex algorithms for Multiple Instance Learning (MIL) Bayesian algorithms for Batch-wise classification Faster, approximate algorithms via mathematical programming Summary / Conclusions

Siemens Medical Solutions, Inc. 1D*: EKG 2D: X-ray, Mammo, Pap... 2D+Time: Echo 3D: CT, MRI, PET... 3D+Time: 4D Cardiac US/CT, Gated PET/CT, Dynamic MRI... Imaging Data: Growing Possibilities, Growing Challenges *signal acquired in time

Siemens Medical Solutions, Inc. Computer-Aided Intelligent Imaging Interpretaton The Goal 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);

Siemens Medical Solutions, Inc. 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”; Sensitivity = 3/5 = 60% Specificity = 3/4 = 75% False Positive Rate (= 1 – specificity) True Positive Rate (= Sensitivity) Receiver operating characteristic (ROC) curve Computer-Aided Intelligent Imaging Interpretaton The Goal

Siemens Medical Solutions, Inc. Segmentation “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 Interpretaton Basic Tools and Approaches

Siemens Medical Solutions, Inc. Detection 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 Interpretaton Basic Tools and Approaches

Siemens Medical Solutions, Inc. Classification 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 Interpretaton Basic Tools and Approaches

Siemens Medical Solutions, Inc. 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? Regulatory challenges Computer-Aided Intelligent Imaging Interpretaton Challenges

Siemens Medical Solutions, Inc. CAD Algorithms

Siemens Medical Solutions, Inc. CAD Workflow: Core Tasks Collect individual patient’s data Feature extraction Inference Decision support for physician Feature Extraction from free text Feature Extraction from images Feature Extraction from omics data Image Registration Segmentation & quantification Combine info from multiple sources Model Optimization Causal prob. inference Fusion & Classification Evidential inference Temporal Reasoning Low-level image processing Knowledge-based modeling Predictive modeling Modeling / Candidate generation Classification (for candidate pruning)

Siemens Medical Solutions, Inc. General Detection Examples Vol 1 Time 1 Detect / Analyze Results1 Chest CT Detect Nodules Results Colon CT Detect Polyps Results Chest CT Detect Emboli Results

Siemens Medical Solutions, Inc. Lung CAD

Siemens Medical Solutions, Inc. Motivation 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.

Siemens Medical Solutions, Inc. 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

Siemens Medical Solutions, Inc. 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 Quantify nodules - volume, calcification, morphology, localization Classify nodules as benign or malignant Lung CAD: Introduction

Siemens Medical Solutions, Inc. Detecting Lung Cancer is hard: Part of a Single CT study of Lung

Siemens Medical Solutions, Inc. Where is the nodule?

Siemens Medical Solutions, Inc. Where is the lung cancer?

Siemens Medical Solutions, Inc. Computer aided detection automatic detection scheme acts as a second reader Computer Aided Detection

Siemens Medical Solutions, Inc. Fly around interactive visualization of the nodule, and even fly around movies are possible... CAD Viewing Modes

Siemens Medical Solutions, Inc. Colon CAD

Siemens Medical Solutions, Inc. Motivation Colorectal cancer is the 3rd most common diagnosed cancer in USA: - 135,000 new cases forecast for 2001 - 48,000 deaths forecast in 2001 - 95% 5-year mortality rate for patients whose colorectal cancer has spread to other body parts - 10% 5-year mortality rate if treated at early stage Source: American Cancer Society

Siemens Medical Solutions, Inc. CT Colonography: Exciting opportunity Invasive colonoscopy remains the Gold Standard CT Colonography: a promising non-invasive method - 0.8 mm slices of abdomen possible in 9 sec breath-hold with a 16- slice CT - CT has been shown capable of down to 6 mm polyp visualization - CT exam is more acceptable and comfortable for patients

Siemens Medical Solutions, Inc. Colon CAD Summary GOAL High sensitivity (Low specificity is acceptable) Colon Segmentation (pre-processing) Polyp Candidate Generation Pruning/Filtering CT Volume Pre-processed Volume Candidate List Final List Feature Extractions Features for Candidate List GOAL High sensitivity High specificity

Siemens Medical Solutions, Inc. Shown in endo-view (bottom right) example of located polyp. This polyp was missed by the physician prospectively Detection missed by physician

30 ©2007 Siemens Medical Solutions. All rights reserved. Computer-aided Diagnosis & Therapy General paradigm for CAD systems Candidate generation Image Candidates Feature Extraction Numerical attributes for each candidate Classification Final Marks on Image

31 ©2007 Siemens Medical Solutions. All rights reserved. Computer-aided Diagnosis & Therapy Properties of the data used for designing classifiers for CAD systems The training data is highly unbalanced There is a form of stochastic dependence among the labeling errors of a group of candidates that are closer to a radiologist mark. The features used to describe spatially close samples are highly correlated The CG algorithm tends to have varying levels of sensitivity to different types of structures. Some training images tend to contain far more false positive candidates as compared to the rest of the training dataset.

32 ©2007 Siemens Medical Solutions. All rights reserved. Computer-aided Diagnosis & Therapy 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.

34 ©2007 Siemens Medical Solutions. All rights reserved. Computer-aided Diagnosis & Therapy Correlations among patients from the same hospital scanner type, patient preparation, geographical location etc Correlations among samples from the same patient: samples pointing to the same structure, samples from different orientations, image characteristics – e.g., contrast/artifacts/noise Hierarchical Correlation Among Samples

35 ©2007 Siemens Medical Solutions. All rights reserved. Computer-aided Diagnosis & Therapy Initial Idea: Additive Random Effect Models The classification is treated as iid, but only if given both Fixed effects (unique to sample) Random effects (shared among samples) Simple additive model to explain the correlations P(y i |x i,w,r i,v)=1/(1+exp(-w T x i –v T r i )) P(y i |x i,w,r i )=s P(y i |x i,w,r i,v) p(v|D) dv Sharing v T r i among many samples  correlated prediction …But only small improvements in real-life applications

37 ©2007 Siemens Medical Solutions. All rights reserved. Computer-aided Diagnosis & Therapy CAD algorithms: Other examples of correlations between samples Multiple (correlated) views: one detection is sufficient Systemic treatment of diseases: e.g. detecting one PE sufficient Modeling the data acquisition mechanism Errors in labeling for training set.

38 ©2007 Siemens Medical Solutions. All rights reserved. Computer-aided Diagnosis & Therapy The Multiple Instance Learning Problem (NIPS 2006): Motivation 4 Candidates pointing to the same polyp Only ONE candidate needs to be correctly classified!!! Bag of candidates

39 ©2007 Siemens Medical Solutions. All rights reserved. Computer-aided Diagnosis & Therapy The Multiple Instance Learning Problem (NIPS 2006) A bag is a collection of many instances (samples) The class label is provided for bags, not instances Positive bag has at least one positive instance in it Examples of “bag” definition for CAD applications: Bag=samples from multiple views, for the same region Bag=all candidates referring to same underlying structure Bag=all candidates from a patient

41 ©2007 Siemens Medical Solutions. All rights reserved. Computer-aided Diagnosis & Therapy CH-MIL Algorithm for Fisher’s Discriminant Easy implementation via Alternating Optimization Scales well to very large datasets Convex problem with unique optima

45 ©2007 Siemens Medical Solutions. All rights reserved. Computer-aided Diagnosis & Therapy Classifying a Correlated Batch of Samples (ECML 2006) : Motivation The candidates that belong to the same patient’s medical images are highly correlated There is not any correlation between candidates from different patients The level of correlation is a function of the pair wise distance between candidates The samples (candidates) are collected naturally in batches All the samples that belong to the same image constitute a batch

46 ©2007 Siemens Medical Solutions. All rights reserved. Computer-aided Diagnosis & Therapy Classifying a Correlated Batch of Samples (ECML 2006) Let classification of individual samples x i be based on u i Eg. Linear u i = w T x i ; or kernel-predictor u i =  j=1 N  j k(x i,x j ) Instead of basing the classification on u i, we will base it on an unobserved (latent) random variable z i Prior: Even before observing any features x i (thus before u i ), z i are known to be correlated a-priori, p(z)=N(z|0,  ) Eg. due to spatial adjacency  = exp(-  D), Matrix D=pair-wise dist. between samples

47 ©2007 Siemens Medical Solutions. All rights reserved. Computer-aided Diagnosis & Therapy Classifying a Correlated Batch of Samples Prior: Even before observing any features x i (thus before u i ), z i are known to be correlated a-priori, p(z)=N(z|0,  ) Likelihood: Let us claim that u i is really a noisy observation of a random variable z i : p(u i |z i )=N(u i |z i,  2 ) Posterior: remains correlated, even after observing the features x i P(z|u)=N(z|(  -1  2 +I) -1 u, (  -1 +  2 I) -1 ) Intuition: E[z i ]=  j=1 N A ij u j ; A=(  -1  2 +I) -1

48 ©2007 Siemens Medical Solutions. All rights reserved. Computer-aided Diagnosis & Therapy Related Work Conditional Random Fields and Maximum Margin Markov Networks used for Natural Language Processing Computationally expensive Multiple Instance Learning (MIL) MILBatch Same label is assigned to the entire batch (bag) of related samples Individuals in the same batch may have different labels Samples in the same bag are assumed to be equally related More fine grained differences in the level of correlation

50 ©2007 Siemens Medical Solutions. All rights reserved. Computer-aided Diagnosis & Therapy Algebra of the Classification Problem 2-Category Linearly Separable Case  Given m points in n dimensional space Represented by an m-by-n matrix A  More succinctly: where e is a vector of ones.  Separate by two bounding planes,  An m-by-m diagonal matrix D with +1 & -1 entries  Membership of each in class +1 or –1 specified by:

51 ©2007 Siemens Medical Solutions. All rights reserved. Computer-aided Diagnosis & Therapy Support Vector Machines Linear Programming Formulation  Use the 1-norm instead of the 2-norm: min s.t.  This is equivalent to the following linear program: min s.t.

52 ©2007 Siemens Medical Solutions. All rights reserved. Computer-aided Diagnosis & Therapy Mathematical Programming formulation (cont.) To be learned during training Standard SVM constraint replaced by the proposed equation Probabilistic-inspired approach replaced by a simpler approximation

53 ©2007 Siemens Medical Solutions. All rights reserved. Computer-aided Diagnosis & Therapy Testing in Batch Classification Decision function for standard SVM: Samples are tested one at a time Decision function for batch classification: Samples are tested in batches Contribution of other samples in the same batch

54 ©2007 Siemens Medical Solutions. All rights reserved. Computer-aided Diagnosis & Therapy SVM-like Approximate Algorithm Intuition: classify using E[z i ]=  j=1 N A ij u j ; A=(  -1  2 +I) -1 What if we used A=(   + I) instead? Reduces computation by avoiding inversion. Not principled, but a heuristic for speed. Yields an SVM-like mathematical programming algorithm:

55 ©2007 Siemens Medical Solutions. All rights reserved. Computer-aided Diagnosis & Therapy Mathematical Programming Formulation: Nonlinear version A “kernelized” version can be also easily derived using the usual duality relation: min s.t.

58 ©2007 Siemens Medical Solutions. All rights reserved. Computer-aided Diagnosis & Therapy Toy Example III Poin t BatchLabelSVMPre- classifier Final classifier 1234512345 1111111111 ++-+-++-+- 0.2826 0.2621 - 0.2398 - 0.3188 - 0.4787 0.1723 0.1315 0.0153 -0.0259 -0.0857 0.1918 0.2122 -0.0781 0.2909 -0.0276 6 7 8 9 10 2222222222 +-+--+-+-- 0.2397 0.2329 0.1490 - 0.2525 - 0.2399 0.0659 0.0432 0.0042 -0.0752 -0.1135 0.0372 -0.0888 0.0680 -0.1079 -0.1671

62 ©2007 Siemens Medical Solutions. All rights reserved. Computer-aided Diagnosis & Therapy Conclusions IID assumption is universal in ML Often violated in real life, but ignored Explicit modeling can substantially improve accuracy Described 3 models in this talk, utilizing varying levels of information Additive Random Effects Models: weak correlation information Multiple Instance Learning: stronger correlations enforced Batch-wise classification models: explicit information Statistically significant improvement in accuracy Only starting to scratch the surface, lots to improve!

64 ©2007 Siemens Medical Solutions. All rights reserved. Computer-aided Diagnosis & Therapy We are hiring! Research Scientists (Machine Learning / Probabilistic Inference) Entry Level to Senior Level Opportunities Computer-Aided Diagnosis & Therapy Solutions Group Siemens 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.

Learning Classifiers for Computer Aided Diagnosis Using Local Correlations Glenn Fung, Computer-Aided Diagnosis and Therapy Siemens Medical Solutions,

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Learning Classifiers for Computer Aided Diagnosis Using Local Correlations Glenn Fung, Computer-Aided Diagnosis and Therapy Siemens Medical Solutions,

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