Comparative exploration of whole-body MR through locally rigid transforms Purpose Whole-body MRI is seeing increasing use in the study and diagnosis of disease progression. In this, a central task is the visual assessment of the progressive changes that occur between two whole-body MRI datasets, taken at baseline and follow-up. Current radiological workflow for this consists in manual search of each organ of interest on both scans, usually on multiple data channels, for further visual comparison. Large size of datasets, significant posture differences, and changes in patient anatomy turn manual matching in an extremely labor-intensive task that requires from radiologists high concentration for long period of time. This strongly limits the productivity and increases risk of underdiagnosis.We present a novel approach to the comparative visual analysis of whole-body MRI followup data. Our method is based on interactive derivation of locally rigid transforms from a pre-computed whole-body deformable registration. Using this approach, baseline and follow-up slices can be interactively matched with a single mouse click in the anatomical region of interest. In addition to the synchronized side-by-side baseline and matched followup slices, we have integrated four techniques to further facilitate the visual comparison of the two datasets: the "deformation sphere", the color fusion view, the magic lens, and a set of uncertainty iso-contours around the current region of interest.Results We have applied our method to the study of cancerous bone lesions over time in patients with Kahler's disease. During these studies, the radiologist carefully visually examines a large number of anatomical sites for changes. Our interactive locally rigid matching approach was found helpful in localization of cancerous lesions and visual assessment of changes between different scans. Furthermore, each of the features integrated in our software was separately evaluated by the experts. Conclusion We demonstrated how our method significantly facilitates examination of whole-body MR datasets in followup studies by enabling the rapid interactive matching of regions of interest and by the explicit visualization of change. The rapid progress in MR scanning technology increasingly enables imaging the whole body, and clinical applications of whole-body MRI are rapidly emerging [1] [2] [3] . For instance, vascular imaging protocols have been developed that enable a vascular checkup and risk assessment for cardiovascular disease [4, 5] . These do enable imaging not only the entire vascular system (including carotids, aorta, renal arteries, and leg vasculature) [6] , but also the amount of body fat and its distribution over the body [7, 8] . The presence of excessive fat in the abdomen is an important risk factor for the onset of vascular diseases, and in combination with the vasculature diagnostic data, whole-body MR may provide a good assessment of the status and risk factors for a patient. Also for oncological applications, diffusion-weighted and Short TI Inversion Recovery (STIR) whole-body MR protocols have been developed for the detection of cancerous lesions [3, [9] [10] [11] and for cancer staging based on, for instance, bone marrow involvement in hematological cancers. These protocols have been shown to enable evaluation of treatment effect, shortly after the onset of chemotherapy regimen or radiotherapy.. The introduction of these new whole-body MR imaging techniques provides important clinical benefits. However, there are several technical challenges inherent to whole-body imaging that so far have only been sparsely addressed due to the relative novelty of whole-body MR acquisition. In particular, comparative visual analysis of whole-body MR follow-up data is difficult. Although the number of methods available for medical image visualization is constantly increasing and even whole-body explorative visualization is becoming a reality [12] , only very few groups have addressed the problems inherent to comparative whole-body scanning: the inevitable differences in patient posture between followup scans, in combination with the massive amount of data. This, in particular, is a problem in radiological evaluation of metastatic cancers, where metastases can appear throughout the body. For instance, breast cancer, prostate cancer, and Kahler's disease tend to metastasize to bone tissue, where lesions can occur in each individual bone. Such patients may undergo periodic evaluation using whole-body MRI. In reading, comparing and reporting these scans, the radiologist has to answer three main questions: 1) Are there any new lesions compared to the previous scan in any of the bones of the whole skeleton?, 2) How did previous lesions evolve over time as a result of disease progression or treatment?, and 3) Is there risk of bone fracture due to progressive bone loss?. To answer these questions, the radiologist has to perform a side-by-side comparison of each individual bone from head to toe, for two (or more) follow-up scans. This is a cumbersome process, because the posture of the patient usually is different between the scans. This greatly complicates localizing corresponding locations with respect to bone anatomy. In addition to comparing both T 1 -weighted (T 1 W) MR images for anatomical detail, other MR sequences, e.g., STIR images that provide better definition of the cancerous lesions [9] , often have to be investigated. As a result, the reporting process of a follow-up whole-body scan may easily take the radiologist 1 h, since it is particularly important to make sure that no lesions are missed due to the detrimental consequences for patient prognosis and quality of life. As such, there is a great demand for more automated visual analysis methods that aid in identifying the identical anatomical location and slice view in follow-up whole-body MR scans.. In this work, we present several methods to facilitate the visual analysis of whole-body MR data for evaluation of oncological follow-up studies. Our contributions are threefold:. • We present a novel comparative visualization approach based on interactive derivation of locally rigid transforms in a region of interest from a pre-computed whole-body deformable registration. These are subsequently applied to the coordinated visualization of baseline, follow-up, and fused whole-body slices. As bones are inherently rigid, these transforms form a good basis for compensating for posture differences by matching follow-up to baseline by simply selecting one bone at a time. Using this locally rigid transform approach, baseline and follow-up anatomical regions can be interactively matched, based on a single click of the mouse in the anatomical region of interest; see Fig. 1 for an illustration.. • Besides visualizing the synchronized side-by-side baseline and matched follow-up slices, we present four ways to facilitate a details-on-demand-based visual comparison of the two datasets: 1) The deformation sphere [13] is a spherical cloud of points that follows the mouse cursor, with each of the points being warped by the local deformation field. The sphere assists comprehension of the actual local deformation field.. 2) The color fusion view combines the baseline and follow-up in one view by making use of two (perceptually motivated) color channels.. 3) We render a set of uncertainty iso-contours around the current region of interest [14] , explicitly visualizing the approximation error of the locally rigid transform. 4) We have integrated a magic lens [15] that displays an alternate MR modality so that areas of change can be further studied.. • Finally, we investigate the role and utility of our methods toward improving the radiologists' workflow in the assessment of tumor burden in patients suffering from Kahler's disease by means of a case study with two experienced skeletal radiologists.. In this paper, we focus on the analysis of oncological follow-up whole-body MR data. However, the presented methods can be used for other applications as well, given a dense non-rigid registration between two follow-up time points. Both radiologists were clearly positive about the interactive whole-body MRI comparative visualization tool, agreeing strongly with our proposition that it sped up the localization and focused study of bone tumors. In this, the interactive matching was deemed to be centrally important, and the sideby-side and color fusion views to be most intuitive in studying the changes between baseline and follow-up.. The deformation sphere was seen by one user as useful in understanding the deformation and perhaps in studying, for example, compression in the spine. However, both users agreed that it would probably not find use in the clinical workflow due to the extra interaction it required, and mainly due to the fact that the other views sufficed for facilitating comparison. This conclusion can possibly be explained by the novelty of these concepts to both our users.. Based on this case study, we conclude that the tool facilitates the study of bone tumor progression, and visual comparison in morphometrics, primarily through the rapid interactive matching and the color fusion view. We have presented a novel method for comparative visual analysis of whole-body MR datasets, focusing, in particular, on the local comparison of rigid structures. The prototype implementation is geared toward radiologists and uses accepted concepts from their standard workflow, while complementing it with the ability to perform coordinated visual assessment of disease progression.. The main contribution of this work is the development of the method that allows interactive alignment of follow-up and baseline images in a locally rigid manner based on the pre-computed global deformation. The mentioned properties of our method, being fast and rigid, are critical for successful assessment of tumor healing or progression.. We have also shown how our method is applied in a radiological case study targeting the progression of bone lesions in patients with Kahler's disease. Although the evaluation of the developed methodology was performed on a single type of data, the developed methodology is very general and thus can be applied for various comparative studies. Its usage, in general, is not restricted to rigid structures: we expect that this approach is also fully or partially applicable to visualization of changes in, e.g., soft tissue. This, however, might require deriving local transforms with more degrees of freedom than the currently used rigid transforms to better reflect the physical properties of that particular tissue. In the future, we are planning to set up an additional case study that would allow quantification of several measures associated with our method, in particular, the inspection time gain per dataset provided by our method and the lesions detection rate. In this study, the results obtained with our tool will be compared with the ground truth in the form of clinical reports available for each of the scans.. A limitation of our application is that it currently only handles a single pair of datasets, while the underlying methods can easily scale to multiple timepoints. If a registration of each timepoint to the baseline scan is made, extending the number of parallel views should be a straightforward and welcome addition. This would allow for the examination of an anatomical structure of interest through an arbitrary number of timepoints.. To further reduce the memory requirements, as would be necessary when loading multiple timepoints, we propose to reduce the memory taken up by the deformation field. Instead of fully storing the deformation field, we could store only the B-spline control grid, vastly reducing memory usage. However, this would exclude the use of other nonparametric registration methods such as optic flow techniques.. The promising results in localizing and assessing changes in bone resorption encourage us to further evaluate the applicability of our method in future clinical studies. For example, the progression of tumor growth in the brain is difficult to assess visually, and non-rigid registration introduces unwanted deformations. Locally rigid transforms may be able to overcome these problems.. In the future, we foresee the need to provide a form of guided exploration, where visual cues help to perform a topdown search for regions containing large intensity changes. This would speed up the work of the radiologist, as it allows him to skip over areas where no changes are detected. Also, from a radiological perspective, there is a high demand to further extend the measurement capabilities of the application. A physical measurement of the volume of change would help in tracking the progression.. This study demonstrates the need for visual analysis methods for whole-body MR. Enabling posture-independent visualization in whole-body MR is a key step toward more effective total body oncological evaluation in clinical practice. The work presented in this paper focuses on enabling the efficient comparative visualization of two whole-body MRI datasets, being mostly baseline and follow-up datasets of A single click there has segmented part of the bone, resulting in a rigid transform that maps the follow-up to the baseline in this region. Note that even though the rest of the body is aligned differently, the local correspondence is well maintained, allowing for a coordinated comparison within a region of interest the same patient. Our technique involves image registration, interactive segmentation, and comparative visualization. In this section, we briefly survey related work on image registration, whereas we go into more depth summarizing comparative visualization, as that is where the contributions of this work lie.. Image registration refers to the process of transforming different images into one coordinate system, often for the purpose of comparison. More specifically, image registration refers to the derivation of a transformation that maps a moving image onto a fixed image, so that pixels from the two images can be directly compared. A number of excellent surveys have been written on the topic [16] [17] [18] . In this work, we make use of an efficient implementation of non-rigid registration with mutual information [19] and B-splines [20, 21] . This specific registration approach is used often in deformation-based morphometry [22] , i.e., the study of biological shape changes in image data.. Comparative visualization refers to the visual representation of multiple data sources with the express purpose of studying the similarities and differences in those data sources [23] . Examples include side-by-side comparison [23, 24] , coupled side-by-side views [25, 26] , image superposition [27] , and more refined methods such as that by Schneider et al. [28] , where they used contour trees for matching and comparing different scalar fields in a flow dataset. Recently, Rieder et al. [29] have developed a tumor visualization tool that incorporates lesion segmentation and manual local registration of two regions of interest. The work by Kok et al. [30] is related to this, in that the skeletons were extracted from small animal imaging datasets and registered, taking into account joint articulation, allowing for the direct comparative visualization of the sub-volumes containing the various limbs.. Image superposition, for example, checkerboard or color fusion displays, is quite popular in medical practice and research [27] . The work in this paper combines a color fusion display with a new type of interactive model-based comparison, where the user implicitly specifies the model by clicking on a part of the anatomy that interests him/her. Based on this expression of interest, the method calculates a transformation that is optimized for the comparison of the region around the selected point. The method by Kok et al. [30] offered a topdown approach to comparison, in which exploration of each bone of interest requires fitting the entire skeleton atlas to the data. Our method, on the contrary, allows exploration of the complete body volume from its local parts. It thereby utilizes interactively specified subsets of the skeleton, which is more suitable to the radiological workflow than atlas-based methods.. The contribution of this work is the interactive matching of datasets according to user-specified regions, and especially the real-time derivation of this matching based on the real-time segmentation and locally rigid approximation of a global deformable registration. Furthermore, this is the first application of comparative visualization to whole-body MRI datasets.

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Model-based reliability analysis Modeling, in conjunction with testing, is a rich source of insight. Model parameters are easily controlled and monitoring can be done unobtrusively. The ability to inject faults without otherwise affecting performance is particularly critical. Many iterations can be done quickly with a model while varying parameters and conditions based on a small number of validation tests. The objective of model-based reliability analysis (MBRA) is to identify ways to capitalize on the insights gained from modeling to make both qualitative and quantitative statements about product reliability. MBRA is developed and exercised in the realm of weapon system development and maintenance, where the challenges of severe environmental requirements, limited production quantities, and use of oneshot devices can make testing prohibitively expensive. However, the general principles are also applicable to other product types. The role of modeling in the reliability analysis process at Sandia National Laboratories is clriven by the nature of the product being analyzed, the specific way in which Sandia defines reliability, the product test program, and the current assessment approach. The produc:ts are weapon systems that spend much of their multidecade: lives in dormant storage. Although some of the subassemblies can be operated repeatedly, the weapon system is essentially a one-shot device. Further complicating the reliability analysis is that, due to treaty limitations, the weapon systems cannot be fully exercised.. The customer for these systems wants to know the reliability of the weapon systems in the operational context. Thus reliability is defined as:. The probability of achieving:. at the target,

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Inferring the biggest and best: A measurement model for applying recognition to evoke consideration sets and judge between multiple alternatives It has long been supposed that preference judgments between sets of to-be-considered possibilities are made by means of initially winnowing down the most promising-looking alternatives to form smaller "consideration sets" (Howard, 1963; Wright & Barbour, 1977) . In preference choices with >2 options, it is standard to assume that a "consideration set", based upon some simple criterion, is established to reduce the options available. Inferential judgments, in contrast, have more frequently been investigated in situations in which only two possibilities need to be considered (e.g., which of these two cities is the larger?) Proponents of the "fast and frugal" approach to decision-making suggest that such judgments are also made on the basis of limited, simple criteria. For example, if only one of two cities is recognized and the task is to judge which city has the larger population, the recognition heuristic states that the recognized city should be selected. A multinomial processing tree model is outlined which provides the basis for estimating the extent to which recognition is used as a criterion in establishing a consideration set for inferential judgments between three possible options. Research on consumer behavior and marketing has long made use of the concept of an "evoked" (Howard, 1963) or "consideration" set (Wright & Barbour, 1977) when consumers are making preference, and purchasing, decisions between the multiple brands available on the market. A typical assumption is that the formation of a consideration set is part of a multi-stage process ending in choice, a component of the choice process that relives what might otherwise be an excessive cognitive load upon the consumer (e.g., Bettman, 1979; Hauser & Wernerfeld, 1990; Wright & Barbour, 1977) . By reducing the number of options to be considered, the consumer limits the time and computational resource to be expended upon forming the preference or making the choice, in line with the concept of "bounded rationality" (Simon, 1955) A similar attempt to describe human judgments and decision-making in a manner consistent with the assumption of boundedly-rational agents is the "fast and frugal" heuristic framework advocated by Gigerenzer and colleagues (e.g., Gigerenzer, Hertwig & Pachur, 2011) .. This framework aims to provide both normative and descriptive accounts of how judgments are made in the real world, where time and computational resources may preclude optimizing strategies (e.g., multiple regression) which consult all possible sources of information and weight combinations of these appropriately to form the judgments made. For example, a heuristic known as "Take-the-Best" (Gigerenzer & Goldstein, 1996) takes advantage of the fact that the majority of cues possessing any degree of validity for a paired-choice criterion inference task are likely to point in the direction of the same option (e.g., when considering the relative size of two German cities, considering whether the city has a soccer team in the major league and whether it is on the intercity line are likely to give similar indications that the city is reasonably substantial). Cues, or reasons to conclude in favor of a particular option, are searched in order of validity (defined as the relative frequency with which the cue predicts the criterion with respect to the reference class) until a cue is found that discriminates between the two options currently being considered -at which point comparison ceases and an inference is drawn. As an example, in considering the size of German cities, whether the city is the national capital has perfect validity (because Berlin is the largest city in Germany) but is not very discriminating (because Berlin is the only option to which the cue applies). Whether the city is a state capital is a less valid cue (if used as a cue, when considering all German cities with >100,000 inhabitants, it will succeed only 77% of the time) but it can be applied more frequently, so is more discriminating, than the "national capital" cue. National capital status will be used as a cue when possible, without checking any other details, but when it cannot be used then other cues (perhaps including state capital status) will be consulted in descending order of validity (Gigerenzer & Goldstein, 1996) . This framework has proved to be a profitable one in the domains to which it has been applied (e.g., Gigerenzer et al., 2011) , and has the cognitively desirable feature of being computational in nature, a feature missing from many other propositions in the literature of human judgement (e.g., the heuristics and biases programme; Kahneman, Slovic & Tversky, 1982 ) except insofar as they record deviations from a normative (typically Bayesian) model. However, the fast and frugal heuristic framework has largely concerned itself with accounting for data taken from tasks where the individual is asked to make a judgment between only two items (albeit often repeatedly, with the identity of the items varying).. One of the most basic principles underlying the fast and frugal approach is the Recognition Principle, later formalized as the Recognition Heuristic (RH; Goldstein & Gigerenzer, 2002) and implemented in ACT-R (Marewski, & Mehlhorn, 2011; Schooler & Hertwig, 2005) . The RH acts as a stopping rule for more complex heuristics (e.g., Take-the-Best, Gigerenzer & Goldstein, 1996) and states simply that, "If the task is to judge which of two options scores highest on a given criterion, and only one of the two options is recognized, infer that the recognized option scores highest on the criterion". For example, the heuristic might be used to judge which of two cities has the larger population (Goldstein & Gigerenzer, 2002) , which of two individuals is the wealthiest (Frosch, Beaman & McCloy, 2007) or which of two diseases occurs most frequently per year (Pachur & Hertwig, 2006 ).. The heuristic is applied when the criterion in question is known to correlate positively with the probability of recognition (Volz, Schooler, Schubotz, Raab, Gigerenzer & von Cramon, 2006) , although the reverse inference can also be made in the unlikely event that the correlation is known to be negative (Goldstein & Gigerenzer, 2002; see also McCloy, Beaman, Frosch & Goddard, 2010 , for an example of reversing the recognition-criterion correlation by inverting the question). In either case, the heuristic takes advantage of information latent in the environment to inform inference and minimize the amount of knowledge sought or retrieved from memory (or other, external sources) about the items in question prior to making a judgment.. Two studies that expand the scope of investigation beyond looking simply at pairedchoices are those by Marewski, Gaissmaier, Schooler, Goldstein, & Gigerenzer (2010) and Frosch et al. (2007) . Marweski et al. (2010) examined the success of different attempts to judge likelihood of winning electoral office amongst named politicians and Frosch et al. (2007) presented 3-or 4-alternatives to choose from in a wealth judgment task. Both of these studies examined the use of the Recognition Heuristic. Recognition would seem, at first blush, to be an appropriate basis on which to reduce a large number of possible options down to an evoked or consideration set of more tractable size. Indeed, in some circumstances, the distinction between criterion-inference judgments and the preference choices investigated in consumer choice studies may become blurred (e.g., subjects asked to indicate which politician they believe will win a given election (a criterion question), as in Marewski et al.'s (2010) data, are de facto also being asked which candidate they believe the public as a whole will prefer to win the election).. The fast-and-frugal framework, and the RH in particular, have not been universally accepted as descriptive accounts of human inference, however (e.g., Hilbig & Richter, 2011; Newell, Weston & Shanks, 2002) . Hilbig and colleagues, in particular, have drawn attention to the difficulty in identifying when recognition per se is used to inform judgment rather than knowledge which accords with recognition (Hilbig, 2010; Hilbig, Erdfelder & Pohl, 2010; Hilbig & Pohl, 2008; Hilbig, Pohl & Bröder, 2009; Hilbig & Richter, 2011) . Given the positive correlation between the probability of recognition and the criterion in question, it is inevitable that many of the other cues that could in principle be consulted (even if only one of the items is recognized) will result in the same inference as recognition alone. The difficulty in assessing the RH as a descriptive model of human inference then becomes one of determining when recognition is the only cue used. Choosing the single recognized option in the inference task is a necessary, but not sufficient, basis for concluding that the RH has been employed. One way of addressing this problem is applying a multinomial processing tree (MPT) model (e.g., the one proposed by Hilbig et al., 2010) . MPT models assume sequential, independent operations which can be expressed as a tree structure, with alternative processes at each branch point each associated with a parameter that represents the probability of traversing that particular branch.. The tree structure terminates with an observable outcome, and the models are compared to the data by estimating the best-fitting parameters and comparing the frequency counts of each outcome in experimental data to the expected outcomes given the parameters estimated.. As a concrete example, consider the position in a two-alternative forced choice task (2-AFC) where the subject is asked to indicate which of the two options scores highest on the criterion of interest. Here, three trees need to be drawn. In the scenario described by the first tree, the subject recognizes neither of the options and is forced to guess between them. There are therefore only two possible outcomes, a correct guess or an incorrect guess, and hence only two branches to the tree. The probability of a correct guess (the branch leading to the frequency count of correct answers) is associated with the parameter g, and the probability of an incorrect guess is therefore given by (1-g). Where guessing is truly random, g=.5. The scenario described by the third tree is similarly of little direct interest to the study of the RH. Here, the subject recognizes both options and uses knowledge to choose between them, with probability correct given by parameter b ("knowledge validity") and the same two-branch tree-structure as previously. This tree is potentially open to expansion, assuming that suitable candidate knowledge processes can be identified. Of more immediate interest is the second decision tree, which describes the scenario in which exactly one of the two options is recognized. Here, there are four possible outcomes: the recognized item might be chosen or not, and the choice might be correct or incorrect. There are also multiple ways in achieving these outcomes.. Suppose the RH is applied uncritically, then the probability that the RH produces the correct answer is given by parameter a ("recognition validity"). However, whether the RH is applied uncritically is itself governed by a parameter, r. Thus, the probability the RH is applied and is correct is given by r.a and the probability the RH is applied but results in an incorrect inference is given by r. (1-a) . If the RH is not applied uncritically, then the inference that the recognized item is correct might be made by the alternative route of applying knowledge. The probability that this occurs is the joint probability of the RH being valid, knowledge being valid, and knowledge being used, given by (1-r).b.a. The routes by which decisions might be made according to this MPT model are given exhaustively in Table 1. TABLE ONE ABOUT HERE From this table, it is clear that MPT models are well-suited to answering the question of the extent to which the RH is employed in any given 2-AFC inference task. Estimation of the best-fitting set of parameters from the data provides the most likely rate of application of the RH in terms of the r parameter. This can be done across the sample of all subjects within the experiment or, where a sufficiently large data-set is available for estimations to be made on an individual basis, it can be done per individual. This may be useful if, for example, r and a are not independent for some participants (see, e.g., Beaman et al., 2010) . However, because of the limited data available, in the study reported below only estimation across the full dataset was attempted. The modeling results reported here indicate the viability of applying the recognition principle as a means of forming a consideration set in criterion judgment as well as preference judgment, when there are more than two alternatives to consider. The particular tree structures proposed for an independent series of decisions can also be fit to the data with estimated parameters providing expected results that do not differ significantly from those observed in the data (Table 2) . Although the particular data-set modeled here (relative wealth judgments) clearly require a criterion judgment, the distinction between criterion and preference judgments is less distinct with other stimuli. For example, Marewski et al. (2010) apply the RH to understanding judgments about the relative success of candidates in German elections. Although this is most obviously a criterion judgment ("who is most likely to win?"), it can also be framed as a preference judgment ("who is the best candidate?") whilst still potentially meeting the prerequisites for applying the recognition heuristic or recognition principle ("infer that the recognized option scores highest on the criterion"). Elucidating the means by which multialternative inferences may be made, even if the expansion is the relatively modest one from 2-AFC to 3-AFC, helps indicate where there are possible similarities between preference choice (where multiple alternatives are frequently presented) and criterion inference (where, typically, paired choices are presented, as in the "city size" task popularized by Gigerenzer and Goldstein (1996) .. One interesting feature that became apparent when modeling these data was the need for multiple different parameters to represent knowledge validity, if not recognition validity. This both supports Smith et al.'s (2011) arguments that knowledge validity should vary as a function of the number of items recognized (see also Beaman et al., 2010) and highlights the need for different types of knowledge to be consulted to choose that the correct item is one of the recognized set (e.g., b 2 ) and which member of the recognized set the correct item may be (e.g.,. The model also indicates that, at least for the data-set considered here, there may be little or no difference between the application and validity of a "recognition inclusion rule" and the recognition heuristic per se. This is something of a surprise as, a priori, one would assume that a recognition inclusion rule might be employed, as a cognitive short-cut, even more frequently than the RH itself. However, it may simply be that the 3-AFC was too similar to 2-AFC for any differences to become apparent. The data also show that recognition adherence rate is not a good measure of use of the recognition principle when more than one item is recognized and a recognized item is chosen. The resulting model, where r 2 is set at the adherence rate, differs significantly from both the baseline, best-fitting model and the data. Setting r 1 , the incidence of employing the RH, also produces a model that differs significantly from the baseline model although, in this case, both models fit the data.. Finally, it is worth noting some limitations of the current modeling approach. Like all formal approaches, it relies upon a number of background assumptions. The need for multiple parameters and the relative independence of certain parameters have already been alluded to, but of equal concern is the lack of an underlying process model. The framework as it currently exists blurs the boundaries between the existence of cognitive processes and their efficacy. For example, the MPT model treats the probability that recognition is used a cue as an outcome probability, qualitatively identical to the probability that recognition is a valid cue. The challenge is to provide an underlying, computable, process model which both draws clear distinctions between the two and shows how, and when, knowledge is consulted. .30 (.17) Table 3 . Parameter values for the version of the 3-AFC model fit to the data in Table 2 . Standard errors for all of the estimates are given in parentheses. All g (guessing) values were fixed at chance. (1-a 1 ) a 1 (1-r 1 ) b 1. (1-a 1 ) g 1. (

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Automatic document metadata extraction using support vector machines Abstract


Feasibility and impact of a computerised clinical decision support system on investigation and initial management of new onset chest pain: a mixed methods study Background: Clinical decision support systems (CDSS) can modify clinician behaviour, yet the factors influencing their effect remain poorly understood. This study assesses the feasibility and acceptability of a CDSS supporting diagnostic and treatment decisions for patients with suspected stable angina. The OMA CDSS, and its underpinning NICE guidance, required contextualised and nuanced clinical information Fig. 9 Problems with entry of patient information into the CDSS to be categorised in a way that did not fit with clinicians' ways of working. This, in combination with a belief on the part of participating clinicians that clinical decision making should not be driven by technology, meant that the CDSS did not substantially inform individual patient management. Qualitative methods, proved essential in deepening our understanding of the factors affecting CDSS use. Use of the CDSS may have been improved by the provision of guidance for both practitioners and patients [5] and a more intensive training and monitoring programme [22] . CDSSs for the assessment and management of new onset chest pain may be more successful if they reflect more closely the ways in which clinicians assess chest pain. Our study has articulated barriers to CDSS use that need to be addressed in the design of future interventions. Our study contributes new findings to the understanding of how a CDSS is used in the initial management of a common symptom. Despite use of the OMA CDSS in 86 % of consultations for which it was available, there was little evidence of impact on medication prescribing or on investigation choice. Through qualitative data analysis we gained an in-depth understanding of the reasons for this discrepancy. We identified problems in entering patient data into the CDSS. These included difficulties in classification of symptoms and risk factors, and in the incorporation of all available clinical information emerging during the consultation. In the majority of observed cases, the CDSS was used after the patient had left the room. Structural and practical barriers to the use of the CDSS included the availability of investigations and the prescribing competencies of the clinicians. Analysis of observational data revealed how clinicians privileged their clinical expertise over CDSS advice, responding to CDSS recommendations as 'decision feedback' rather than 'decision support'. There was most disagreement with CDSS recommendations for patients whose pain was categorised as atypical, the group for which mapping patient symptoms to the CDSS had proved most challenging. Chest pain clinicians were rarely observed prescribing medication from the clinic. Taken together these data indicate little chance of the OMA CDSS influencing clinical outcomes, and as such we will not proceed to a cluster randomized controlled trial of the OMA CDSS.. Observing consultations in a chest pain clinic, Somerville and colleagues [12] found that doctors were engaged in practical interpretive work, actively restructuring patient narratives to meet an accepted diagnostic classification. Pain narratives were sometimes unstable and changing, and symptoms outside the canon were investigated less frequently. We have observed clinicians engaged in this interpretive work, but also struggling to use a classification that was sometimes at odds with their usual way of working, despite our efforts during CDSS development to understand clincians' working practices. Our clinicians worked creatively with the constraints of the tool, in line with Berg's description of how protocols for care can be circumvented, tinkered with and interpreted in many different ways [13] . We observed clinicians developing 'workarounds' to enable the system to be used, introducing the potential for unintended consequences of CDSS use [14] . Previous qualitative studies have identified CDSSs having little impact on decisions either through non-use of the CDSS [15] , or use after the consultation has been completed [16] . A study of nurses using CDSS identified how they were primarily used to confirm decisions that had already been made [17] .. The OMA CDSS delivered a national clinical guideline through patient specific recommendations. The constraining effects of guidelines have been previously articulated, including ignoring data that cannot easily be measured, and failing to allow the tailoring of advice to individual patients [18, 19] . A major limitation of guidelines is their poor fit to complex clinical problems, particularly in the presence of Fig. 6 Classification of chest pain. Source: NICE CG95 [9] multi-morbidity, being more suited to relatively straightforward clinical decisions, [20] . From our findings, this limitation extends to CDSSs.. A strength of our study lies in its mixed methods approach. Although the quantitative data suggested that the CDSS was used, the qualitative data were essential in developing a more complete understanding of how it was used. A limitation of the quantitative study was its relatively small sample size, precluding statistical analysis. It is possible that some appropriate investigations may have happened after six months, at which point our follow-up date was censored, so this was a conservative assessment of adherence to investigation recommendations. We have measured medication prescription in general practice six Fig. 7 Problems with entry of patient information into the CDSS months after the chest pain clinic visit, as a proxy for successful commencement or recommendation of long term secondary prevention medication at the time of the clinic. There are many other factors affecting whether medication is continued at six months, such as emergent indications for medication. Although CDSS use in both trusts was high, it appeared to be significantly different between Trust A and B. A limitation of the qualitative study is that were unable to observe Trust B after the introduction of the CDSS.. The OMA CDSS was introduced at a time of transition for chest pain services, the NICE guidance on which it was Fig. 8 Problems with entry of patient information into the CDSS based having been published four months earlier. At this time, the ETT test played a central role in chest pain clinic routines, and our main qualitative study site did not routinely use computerised tomography or computer tomography coronary angiography for the diagnosis of angina. The NICE guidance advised against the use of the ETT, and recommended increased numbers of non-invasive tests including computerised tomography investigations. We found that the failure of the guidance to incorporate a test that was an entrenched part of chest pain clinic practice [21] added to the difficulties of the clinicians in using the CDSS. It is possible that the CDSS would have been more able to impact upon clinic processes when chest pain clinics were more able to follow the guidance. We have reported how often clinicians' choice to investigate agreed with the CDSS recommendations (concordance). Although it might be assumed that concordance with NICE guidance reflects appropriate management, we acknowledge the limitations of concordance in the assessment of CDSS. Concordance may be affected by many and diverse things, for example the structural and practical barriers identified in this study, or the perceived appropriateness of the underpinning guidance for a specific clinical scenario. Before and after study 294 patients were recruited to the quantitative study between November 2009 and May 2011; 285 were included in the analysis at six months (Fig. 3) : 106 patients seen in a clinic that had not received the OMA programme (Before OMA) and 179 patients seen in a clinic that had received the OMA programme (After OMA). Characteristics at baseline (chest pain clinic consultation) including cardiovascular risk factors and current medications are shown in Table 2 . Clinicians seeing patients in the Before OMA group were cardiologists (3), specialist cardiology nurses (2) and a cardiac physiologist (1), and in the After OMA group were cardiologists (4), specialist cardiology nurses (3) and a cardiac physiologist (1). The CDSS was used for 86 % (154/179 95 CI 81-91 %) of patients in the After OMA group. CDSS was used for 81 out of 100 patients (81 %) in Trust A, and 73 out of 79 patients (92 %) in Trust B (difference 11, 95 CI 2-21 %).. The CDSS recommended investigation in 34 % (95 CI 25-43 %) of patients Before OMA and 41 % (95 CI 34-48 %) of patients After OMA. Table 3 shows the number of patients having any investigation done within six months, by CDSS recommendation. The proportion of patients for whom the CDSS recommendation was followed was 74 % (95 CI 65-82 %) in the Before OMA group and 75 % (95 CI 69-81 %) in the After OMA group. The proportion of patients for which the CDSS recommended no investigation that did not have one was the same before and after the OMA intervention. Where the CDSS recommended an investigation, the proportion receiving an investigation was 50 % [CI 34-66 %] in the Before OMA group and 59 % [95 CI 48-70 %] in the After OMA group. The CDSS recommended an increase in the number of medication classes for 25 patients (24, 95 CI 16-32 %) Before OMA. Of these, eight (32, 95 CI 14-50 %) patients received at least one additional medication class, two (8 95 CI −3-19 %) received fewer medication classes and 15 (60, 95 CI 41-79 %) had no change. In the After OMA group, the CDSS recommended an increase in the number of medication classes for 34 (19, 95 CI 13-25 %) participants. Of these, 17 (50, 95 CI 33-67 %) were prescribed at least 1 additional medication class, 7 (21, 95 CI 7-35 %) were prescribed at least 1 fewer medication classes and 10 (29, 95 CI 14-44 %) were unchanged. Clinicians need to use the best available evidence to inform patient management decisions. Clinical decision support systems (CDSSs) generate patient-specific assessments or recommendations to aid clinical decision making. CDSSs can improve practitioner performance and health care processes in a range of clinical scenarios including disease management, drug-dosing, prescribing and preventive care [1] [2] [3] . The effect of CDSSs on clinical outcomes is less frequently studied. In a systematic review of 148 randomised controlled trials of CDSSs implemented in clinical settings only 29 assessed clinical outcomes [1] . There was moderate evidence in support of an effect of CDSSs on morbidity from 16 studies; evidence for other clinical outcomes was of low quality.. Few studies have sought rigorous evidence to determine what factors contribute to the effectiveness of decision support systems [4] . A recent meta-regression of computerised CDSSs found sufficient quality evidence for the assessment of only six factors. Of these, success of CDSSs was positively associated with systems developed by the investigators of the primary studies, systems providing advice to patients and practitioners, and systems requiring a reason for overriding advice [5] . It has been argued that research based on randomised controlled trials is not sufficient to allow rigorous assessment of the factors that make CDSSs successful, and that qualitative contextual evaluation and observation are needed [4] .. In the United Kingdom (UK), initial specialist assessment of suspected angina is undertaken in chest pain clinics. Chest pain clinics may incorrectly diagnose some patients, and may not initiate secondary prevention in those with angina. In a cohort of 8762 patients followed for three years after attending a chest pain clinic, 33 % of all cardiovascular events occurred in patients with a diagnosis of non-cardiac chest pain [6] . This study reported under-prescription of effective secondary prevention: of those diagnosed with angina only 28 % were taking a statin [7] .. Our Optimising the Management of Angina (OMA) programme includes a web-based computerised CDSS to support investigation and medication decisions for patients with new onset stable chest pain. We hypothesised that appropriate investigation, leading to accurate diagnosis and effective secondary prevention, would lead to a reduction in cardiovascular events. Here we present a mixed methods study that aimed to understand the factors influencing the feasibility of the OMA CDSS in clinical practice, and the impact of the CDSS on investigation and prescribing behaviour, to inform potential progression to a cluster randomised trial of the intervention. The OMA programme is a complex intervention delivered at the level of the clinic. The programme has 3 stages: preparation, training and clinic tools (Fig. 1) . The preparation and training stages facilitate use of the OMA CDSS. The components of the intervention were developed using behavioural change domains identified by Michie and colleagues [8] .

O O O O O O O O O O O O O O O O O O B I O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O B O O O O O O O O O O O O O O O B O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O B O O O B I O B I O O O O O O O O O O O O O O O B O O O O O O O O O O O O O B I O O O O O O O O O O O O B O O B I O O O O O O O O O O O O O O B O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O B I O O O O O O O O B O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


ON THE INFLUENCE OF WORD REPRESENTATIONS FOR HANDWRITTEN WORD SPOTTING IN HISTORICAL DOCUMENTS Word spotting is the process of retrieving all instances of a queried keyword from a digital library of document images. In this paper we evaluate the performance of different word descriptors to assess the advantages and disadvantages of statistical and structural models in a framework of query-by-example word spotting in historical documents. We compare four word representation models, namely sequence alignment using DTW as a baseline reference, a bag of visual words approach as statistical model, a pseudostructural model based on a Loci features representation, and a structural approach where words are represented by graphs. The four approaches have been tested with two collections of historical data: the George Washington database and the marriage records from the Barcelona Cathedral. We experimentally demonstrate that statistical representations generally give a better performance, however it cannot be neglected that large descriptors are difficult to be implemented in a retrieval scenario where word spotting requires the indexation of data with million word images. There is a huge amount of old manuscripts stored in libraries and archives. These documents have a historical value not only for their physical appearance but also for their contents. Such contents are daily studied by scholars and communities of use for history research. Many digitization initiatives exist worldwide to convert historical documents to digital libraries. The digitization process and the use of standard formats to store the images in digital libraries ensures the long-term preservation of the documents. On the other hand, the society at large can browse through collections by web-enabled technologies. But just browsing through milliards of digitized document images is not enough. Digital libraries have to provide tools to the users for mining the contained contents and unlock the wealth of information in these resources. Manually typing all the information and recording into databases is a te-dious and slow process, requiring a lot of resources. There is still an important gap in the conversion pipeline from paper documents to useful information, especially when documents are handwritten and are degraded due to the ageing impact.. Handwriting recognition (HWR) is one of the most significant topics within the field old Document Image Analysis and Recognition (DIAR). It is, after decades, a key research activity with still many challenges towards the creation of digital libraries of historical manuscripts whose contents can be retrieved and crosslinked. Over the last years, relevant research achievements have been attained and some systems have been reported in the literature that are capable of transcribing handwritten documents up to a certain precision. Generally speaking a HWR system can be divided in two coupled components, namely the morphological and the language models. The morphological component formulates the recognition of a handwritten word as a pattern recognition problem. The language model allows to take into account the prior probabilities of the lexical units of a given lexicon. Handwritten text can be seen as a sequence of observations (e.g. characters, graphemes, or columns features) so following an analytical approach the classification of an unknown sequence X is formulated in terms of the likelihood that a feature vector sequence X is generated by the model of word W , and the prior probabilities P (X) of (sub)words in the lexicon. In this framework, a large amount of data is required to train both the morphological and the language models. However, in historical documents such training data may not exist, or may require a tedious manual creation. Moreover, the information contained in a collection of manuscripts may not be useful to train recognizers for another collection because of different periods of time with different script styles, or different disciplines, with non-intersecting and restricted lexicons. On the other hand, some use cases do not require full transcription, or the existing technologies can hardly transcribe documents with poor quality. Rather than attempting to transcribe text to its full extend, only checking the existence of special keywords (names, dates, cities, etc.) may be enough. Handwriting recognition approaches usually combine morphological (i.e. shape) and language models. When dealing with historical documents, for different reasons, not always the language model is neither available nor useful. In word spotting applications, when the purpose is to retrieve a page containing a queried word from a large database, the representation of the shape of the word takes a high relevance. In this paper we have compared different descriptors to assess the strengths and weaknesses of statistical and structural models when recognizing handwritten words in a word spotting application. We have compared four approaches, namely sequence alignment using DTW as a classical reference, a bag of visual words approach as statistical model, a pseudo-structural model based on a Loci features representation, and a structural approach where words are represented by graphs.. The four approaches have been tested with two collections of historical data.. The overall conclusion we can draw according to the results described in Section 3 is that the better performance is achieved with a statistical model based in the image photometry. In our work, it has been implemented with a local descriptor based on a bag of visual words representation constructed from dense SIFT features extracted from the word image. The main drawback of this approach is its high memory requirements to store such a large descriptor. This makes it unable for a retrieval framework unless that dimensionality reduction and hashing strategies are implemented, which would harm its performance. The main drawback of DTW is that it is very time consuming, so it would difficult to integrate it in a real time retrieval application. However, DTW is robust to the elastic deformations typically found in handwritten words, so it appears to be more robust in multiple writer scenarios. As we could expect, the methods based on structural features underperform the statistical ones. The ones we have implemented rely on the geometric and topological information of the image contours or skeletons. This information varies a lot among different instances of the same word class. The good points of the approaches we have presented is its ability to be integrated in a retrieval framework. Of course, it is not an intrinsic feature of structural methods.. There are many parameters to take into account if one wants to implement a word spotting application. Depending on the different characteristics that define the system, the selection of the most appropriate method can be decided. We identify the following factors as relevant when designing a word spotting system. First, the availability of a lexicon that allows to infer a language model. A language model requires enough training data statistically representative. Intra class variability and inter class separability is also a key issue. This might depend on whether we have a single or multiple writer scenario, if the number of classes is known a priori, or if the collection covers a large period of time with strong variation in script styles. When the designed application requires a real time retrieval it is important to choose a compact descriptor. In that case, the loose of performance can be compensated with a relevance feedback paradigm. The later factor is related to the ability of compiling off line the whole set of words of the collection to build an indexation structure for the retrieval process. A last consideration is the querying mode, namely query-byexample or query-by-string. The former requires learning the model from individual characters, while the latter can be performed "on the fly" asking the user to crop an example word as a query.. As a final conclusion regarding feature descriptors, there is no universal representation able to cope will all the above criteria. Although in our study a statistical descriptor outperforms the other ones, we can not neglect the use of structural information. It is also very important the indexability of the descriptor. A representation which is very accurate and has a good performance in small sets, can dramatically reduce its performance when it is used for retrieving large collections of document images. We present in Figures 7 and 8 an example of the qualitative results for a given query for the Barcelona Cathedral and the George Washington collection respectively. We can see that all the methods present some false positives in the first ten responses. However, it is interesting to notice that this false positive words are in most of the cases similar to the query in terms of shape. In Figure 7 , when asking for the word "Farrer ", we obtain similar results such as "Farer ", "Ferrer ", "Carner ", "Fuster " or "Serra". In the case of the George Washington collection, the behavior is similar. When asking for the word "Company" we obtain as false alarms similar words as "Conway", "Commissary", "Companies", "Country", "complete", etc.. In Figure 9 we present the precision and recall plots for both collections and in Tables 2 and 3 we present the evaluation measures for the George Washington and the Barcelona Cathedral collection respectively.. We can see that in both scenarios, the BoVW method outperforms the other three. It is however interesting to notice that the George Washington collection seems to be easier for the BoVW method whereas the Barcelona Cathedral is the one where we obtain better results for the other three methods. The BoVW method extracts the word representation directly from the gray-level word images, whereas the DTW-based, the pseudo-structural and the structural methods need a preliminary step of binarization. In the George Washington collection there are many degraded word images that might be difficult to binarize hindering the performance of the word representations that need a preprocessing step including binarization.. We can see an example of the performance of the Otsu binarization process for the two collections in Figure 11 . As we can appreciate, the binarization process in the Barcelona Cathedral dataset is quite robust and stable, whereas the George Washington collection is more noisy and the binarization performs quite different for words from the same class. Even if the BoVW method performs better in both cases than the rest, it also presents an important drawback. Its feature vector is huge (140.000 dimensions) respect the other three methods. This is an issue when facing large collections since the feature vectors must fit in memory for real-time retrieval. Binarization techniques such as LSH 10 or product quantization 14 should be taken into account. However, even if those indexing methods will reduce the size of the descriptor so it can be used in real scenarios, they will also harm its discriminative power decreasing its performance.. Notice that the pseudo-structural method performs very close to the DTW a) b) reference method for the Barcelona Cathedral collection. However, DTW is very time consuming. Computing distances among words with the DTW method is very time consuming since the algorithm has a O(n 2 ) complexity. On the other hand, the pseudo-structural word representation is a compact signature that can be easily indexed, and thus scales much better than a DTW-based method.. Finally, we can see that the structural method performs poorly in both collections. This word representation needs a preliminary step of transforming the raster image to a vectorial image in order to build a graph representing the words. When working with historical documents, slight degradations affect too much to the vectorization process yielding to an important performance loss of this word representation. However, it seems from the performance that the vectorization step does not have much effect on the performance but in time complexity for computing the paths. This can happen due to the appearance of noise in the image for which the vectorization method can create spurious critical points which increases the path computation time. Since the appearance of spurious points does not change the overall structure of a path, the performance will not change drastically.. In Figure 10 we can see the results for a scalability test. For each given query, having R relevant words in the collection we performed the retrieval experiments by iteratively increasing the negative samples by a factor of R in the considered dataset. In the first step, d = 0, the collection has just the R relevant items, so all the methods yield a mean average precision of 1. In the next iteration, d = 1 we add a random selection of R non-relevant items. At d = 2 the collection consist of the R relevant words and 2 × R non-relevant words, and so on. We can see that again the BoVW method outperforms the rest in both collections. The pseudo-structural and structural methods for the George Washington collection present a sudden drop in performance at the early steps, that is that adding just a few negative samples to the dataset already hinders a lot the performance of these methods. On the other hand, for the Barcelona Cathedral collection, increasing the dataset do not entail such a . Pseudo-structural. performance loss and both the pseudo-structural and the structural method have the same behavior in the early stages. We can also note that even if the methods present a sudden performance drop at the early stages, the performance tends to remain stable in the last stages. In order to summarize the analysis of the results of this study, we present in Table 4 a summary of the pros and cons of each of the word representations. For each of the methods we highlight their strengths and the weaknesses. Concerning the complexity of the methods, both the BoVW and the DTW-based methods present some defficiencies. The feature vectors from the BoVW method occupy a lot of memory whereas the time complexity for the distance computation in the DTW method is also high. On the other hand, both the structural and pseudostructural methods are efficient in terms of space and time, which makes them a good candidates for being indexed. As we shown above, the need of preprocessing steps might severely hinder the performance of the methods. In that sense, the BoVW method, which can be directly computed from the gray-level images, is a better choice than the methods that need a binarization step, and much better that the structural method that needs a vectorization step in order to build the word graphs. This is also reflected by the observed performance of these methods. Finally, notice that none of the word representations under study scale well, either by its high-dimensionality, by the complexity of the distance computation or by their performance when increasing the dataset size. Due to its effectiveness, word spotting has been largely used for historical document indexing and retrieval, not only for old printed documents 28 , but also for old handwritten ones 7, 17, 22, 23, 30, 33 . It is important to remark that, although the use of a language model is very common in HWR, it may be useless when dealing with historical documents. It may be due to different reasons: the lack of enough training data to compute lexicon frequencies, constrained vocabularies (names, cities) or non stable over the time, "on the fly" querying (the user selects a collection and wants to search an arbitrary word in it), etc. In this scenario, a good description of the word images is a key issue, so the recognition relies only in the morphological model. In this paper we study the influence of different feature representations when recognizing handwriting. Different representations can be found in the literature for the description of word images. Similarly to shape descriptors, they can be classified into statistical and structural. The former represent the image as a n-dimensional feature vector, whereas the latter usually represent the image as a set of geometric and topological primitives and relationships among them.. Statistical descriptors are the most frequent and they can defined from global and local features. Global features are computed from the image as a whole, for example the width, height, aspect ratio, number of pixels, are global features. In contrast, local features are those which refer independently to different regions of the image or primitives extracted from it. For example, position/number of holes, valleys, dots or crosses at keypoints or regions are local features. Rath and Manmatha proposed a Dynamic Time Warping-based approach which computed profile features 31, 33 : the upper and lower profiles, the number of foreground pixels, and the number of transitions black/white. This set of features showed better performance than the raw pixels as input features used in their previous work 27 . In a similar way, other approaches also use information about profiles: the QBS approach proposed by Kesidis et al. 17 for machine-printed documents compute 90 features based on zoning plus 60 features based on upper and lower profiles. Frinken et al. 7, 8 proposed a word spotting approach based on neural networks, which use the set of features proposed by Marti and Bunke 29 . For each window of 1-column width, the following 9 geometric features are computed: The 0th, 1st and 2nd moment of the black pixels distribution within the window; the position of the top-most and bottom-most black pixels (upper/lower profiles); the inclination of the top and bottom contour of the word at the actual window position; the number of vertical black/white transitions; and the average gray scale value between the top-most and bottom-most black pixel.. Gradient features are also very common: In the HMM-based word spotting approach of Rodríguez and Perronnin 34 , local gradient histogram features were computed. Similarly, in the multi-lingual QBS proposed by Leydier et al. 22, 23 , gradient features from ZOI (zones of interest) are extracted. Moghaddam and Cheriet

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O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


A p-Median approach for predicting drug response in tumour cells Background: The complexity of biological data related to the genetic origins of tumour cells, originates significant challenges to glean valuable knowledge that can be used to predict therapeutic responses. In order to discover a link between gene expression profiles and drug responses, a computational framework based on Consensus p-Median clustering is proposed. The main goal is to simultaneously predict (in silico) anticancer responses by extracting common patterns among tumour cell lines, selecting genes that could potentially explain the therapy outcome and finally learning a probabilistic model able to predict the therapeutic responses. Results: The experimental investigation performed on the NCI60 dataset highlights three main findings: (1) Consensus p-Median is able to create groups of cell lines that are highly correlated both in terms of gene expression and drug response; (2) from a biological point of view, the proposed approach enables the selection of genes that are strongly involved in several cancer processes; (3) the final prediction of drug responses, built upon Consensus p-Median and the selected genes, represents a promising step for predicting potential useful drugs. Conclusion: The proposed learning framework represents a promising approach predicting drug response in tumour cells. In this paper the problem of identifying a suitable profile of cancer patients by linking gene expressions, drug responses and types of cancer has been addressed. A learning framework based on three building blocks has been proposed. The experimental results highlight three main findings: (1) the proposed Consensus p-Median is able to create groups of cell lines that are highly correlated both in terms of gene expression and drug response;. (2) from a biological point of view, the gene selection performed on these clusters allows the identification of genes that are strongly involved in several cancer processes; (3) the prediction of drug responses, by using the patient profile obtained through clustering and gene selection, represents a promising step for predicting potentially useful drugs. Concerning the ongoing research, several issues are still to be investigated. Among them the next future work will be focused to the identification of a suitable number of clusters and the use of more "selective" discretization policies. As far is concerned with the methodological approach, an interesting comparison relates with those approaches, belonging to the multiple tasks learning, able to simultaneously predict the drug responses given a (subset) of gene expressions. Instances of future investigations are Marginal Regression [70] and Support Vector Machine [71] For Multitask Learning. A further development of the proposed investigation relates to the exploitation of additional data sources, such as proteomic expression profiles, to better predict the drug response in tumour cells. Cancer is a disease treated with various strategies depending on the type of cancer and the stage of the disease. Generally, therapeutic agents are selected according to the specific cancer type and patient population, based on the effectiveness in large-population studies [1, 2] . Now, with the advances of the genomic era, a massive amount of high-throughput data has been made available for understanding the cancer system biology. The public available datasets composed of genomic data and drug responses offer the opportunity to reveal valuable knowledge about the hidden relationships between gene expression and drug activity of tumor cells, pointing out the conditions that bring a patient to be more responsive than others to a given therapeutic agent. Although data collection provides the baseline to enable a better understanding Mathematical and statistical models of complex biological systems play a fundamental role in system biology, and in particular in cancer related issues. They can be exploited for exploratory purposes, to validate hypothesis and make predictions about quantities that are difficult or impossible to be measured in vivo.. In the last decade, several studies have been conducted to develop platforms on which cancer high-throughput computational analysis can be performed. Much of these computational approaches are targeted at predicting the drug sensitivity/resistance by means of statistical inference and regression methods able to take into account genomic information of hundreds of genes for determining a specific drug response [3] [4] [5] . However, the massive availability of chemical compounds as potential cancer therapies has opened to the investigation of in silico therapy response prediction which requires more sophisticated computational models and methods to optimize the experimental design of cell-drug screenings. http://www.biomedcentral.com/1471-2105/ 15/353 A first attempt of gene-drug integrative analysis was presented in [6] where, thanks to a hierarchical clustering algorithm, several investigations have been performed: (1) cell-to-cell correlation on the basis of gene expression and drug activity profiles, (2) relationships between drug activity patterns and mechanisms of action, (3) genedrug correlation on the basis of gene expression and drug activity profiles. In subsequent investigations [7, 8] , the triangle gene expression profiles, drug responses and cancer types has been explored by integrating unsupervised and supervised machine learning algorithms. The clustering approach based on Soft Topographic Vector Quantization (STVQ) [9] has shown that gene expression profiles are more related to the cancer type than to the drug activity patterns, while thanks to the structure learning of Bayesian Networks (BN) some biologically meaningful relationships among gene expression levels, drug activities and cancer types have been confirmed and in some cases revealed. More recent works [10] [11] [12] [13] are targeted at integrating explorative approaches with predictive paradigm towards a computational gene-drug screening. While [10, 11] and [12] are based on non-deterministic clustering approaches (k-Means, STVQ and Genetic Programming) for identifying relevant genes involved in cancer mechanisms and predictive of drug response, [13] introduces a framework based global optimization to cancel the randomness, and therefore the variance, of stochastic clustering results when predicting a therapy outcome.. The results of the above quoted papers and of a wide set of related approaches highlight several interesting considerations:. • The explorative analysis performed through clustering approaches reveals that the tissue of origin is more related to the gene expression profile than the drug activity patterns. This suggests that the genomic information of a cell line plays a fundamental role, independently of the organ of origin, to understand anticancer therapy responses. This idea has been supported by the fact that several cell lines with a relatively high expression level of those genes regulating multi-drug resistance have been clustered in the same group. This indicates that chemoresponse mechanisms are distributed across different tissues in the panel and that it should be possible to link drug responses to gene expression profiles. • In order to cancel the variability of results of stochastic clustering and to guarantee the convergence to a global minimum, we need to address the clustering problem by exact approaches able to find globally optimal solutions. • Computational approaches based on Bayesian Networks reveal interesting relationships among subsets of genes and drugs. The potential of Bayesian Networks encourages us to exploit this probabilistic model not only for deductive purposes, but also for prediction issues.. In order to achieve the final goal of simultaneously predicting the drug response of several compounds given a patient genomic profile, we propose a computational framework based on the following assumption: groups of cell lines homogeneous in terms of both gene expression profile and drug activity should be characterized by a subset of genes that explains the drug responses. To this purpose a three-folds analysis has been investigated: p-Median problem formulations to create clusters of homogeneous cell lines, Feature Selection Policies to select relevant genes and finally Bayesian Networks to predict drug responses of tumour cell lines. Computational results show that the proposed Consensus p-Median, combined with gene selection and BN inference engine, yields homogeneous clusters while guaranteeing good predictive power for inferring drug responses for a new cell line. This is also confirmed by the biological evaluation performed on the selected genes: according to the existing literature the set of genes used to train the BNs, which has been selected by using the groups of cell lines obtained by the proposed Consensus p-Median, has shown to be biologically relevant from an oncological point of view.

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