dataesr/scientific-paragraphs-categorization huggingface dataset:
The tumor is segmented on CT-scan (an ROI or Region Of Interest is defined) and the signal intensity in each voxel is calculated. This corresponds to the attenuation of X-ray beam photons, which in turn is represented by the density in Hounsfield Units of the tissue (difference of density in comparison to water). Different spatial scale filters (SSFs) will ultimately modify all of these outputs (Fig. 1 ). Finally, the software computes Shannon's entropy and other low order textural features. Beyond TexRAD, the radiomics community has developed a wide range of solutions allowing for the extraction of those metrics and an even larger dataset of imaging features through simple pipelines. The extraction is based on open-source and free solutions with inter-operable locked docker containers (e.
Methods Patients. We screened 525 consecutive patients prospectively enrolled in the MOSCATO-01 precision medicine trial (Gustave Roussy, Villejuif, France, NCT01566019). Patients were included after informed consent. The experimental protocol of the MOSCATO trial was carried out in accordance with guidelines and regulations and was approved by our institutional review board (Gustave Roussy, Villejuif, France). In this trial, each patient had a targeted tumor biopsy (synchronous metastases SM#1) to determine its molecular profile and was subsequently prescribed a molecular targeted therapy adapted to the molecular profile. The sample size was derived from power calculation, which is provided as a supplementary material.
Finally, we computed the prognostic value of entropy for overall survival analysis. We compared the overall survival in two groups: high entropy when entropy is above the median, and low entropy otherwise. Overall survival medians within these groups were estimated with the Kaplan Meier method. We computed the concordance index for validating the predictive ability of a model based on the increase in entropy for the prediction of overall survival. For this we used the package survcomp from Bioconductor  30. Statistical analyses were performed using R version 3. 3. 0 and SPSS 24. 0.
Purpose: Survival of acute leukemia (AL) patients following umbilical cord blood transplantation (UCBT) is dependent on an array of individual features. Integrative models for risk assessment are lacking. We sought to develop a scoring system for prediction of overall survival (OS) and leukemia-free survival (LFS) at 2 years following UCBT in AL patients. Experimental Design: The study cohort included 3,140 pediatric and adult AL UCBT patients from the European Society of Blood and Marrow Transplantation and Eurocord registries. Patients received single or double cord blood units. The dataset was geographically split into a derivation (n ¼ 2,362, 65%) and validation set (n ¼ 778, 35%). Top predictors of OS were identified using the Random Survival Forest algorithm and introduced into a Cox regression model, which served for the construction of the UCBT risk score.
Source of data and participants This was a retrospective prognostic modeling study. The data source was based on information reported to Eurocord and the Acute Leukemia Working Party, Paediatric Disease Working Party, and Cord Blood Committee of the European Society for Blood and Marrow Transplantation (EBMT). The EBMT registry is a voluntary working group of more than 500 transplant centers, whose participants routinely report data on patients undergoing hematopoietic stem cell transplantation (HSCT). Eurocord collects data on UCBT performed in >50 countries, mainly in EBMT centers. The population selection criteria included children and adults undergoing an UCBT (single/double unit) between the years 2004 and 2014, as a treatment for de novo or secondary AL in all disease statuses. Patients receiving myeloablative (MAC) or reduced intensity conditioning regimen (RIC) were included.
MAC was defined as a regimen containing either total body irradiation (TBI) with a dose greater than 6 Gy, a total dose of oral Busulfan greater than 8 mg/kg, or a total dose of intravenous Busulfan greater than 6. 4 mg/kg  (11). Patients who had a previous allogeneic HSCT were excluded. All patients or legal guardians provided informed consent for UCBT according to the Declaration of Helsinki. The review board of Eurocord/EBMT approved this study. To maximize the power and generalizability of the results, all available patients on the registry were included, provided they meet inclusion criteria and had no missing data on the measured outcome. Geographical splitting (i. e. , random selection of the center's country; Supplementary Table  S1 ) was applied to generate the derivation (n ¼ 2,362, 65%) and validation datasets (n ¼ 778, 35%) from the original cohort (i.
Statistical analysis pipeline Briefly, the analysis pipeline comprised of sequential stages: (i) preprocessing-data quality assurance and multiple imputations of missing values; (ii) estimation of predictor importance using the random survival forest (RSF) for feature selection; (iii) interaction analysis using RSF and Cox modelization; (iv) construction of the risk score. Principles for prognostic model development were adapted from the Transparent Reporting of a prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement  (12). Analyses were performed with SPSS version 21 (SPSS Inc. /IBM) and R version 3. 2. 1 (R Development Core Team( including the mi, randomForestSRC, ggRandomForests, and survival packages.
Missing data We assumed missing data occurred at random depending on the clinical variables and UCBT outcomes, and performed multiple imputations using chained equations  (15). Multiple imputation allows to introduce the variability of imputed data to find a range of possible responses from which to work from. It has been shown to be an effective way of handling missing data and minimizes bias and loss in precision that may often result from excluding such patients. In addition, multiple imputation remains valid even if the proportion of missing data is large  (12). Missing values were predicted for the entire cohort on the basis of all predictors listed in Table  1 , demographical information on the transplant center, and patients overall survival  (16). A total of 20 imputations were performed on the entire cohort, producing 20 distinct datasets.
e. , at the root node) where they partition large samples of the populations. Predictiveness of a variable is inversely related to the value of minimal depth. Smaller minimal depth value of a variable, the greater is its predictive impact  (20). RSF and minimal depth are further explained in Appendix 1. For all 20 versions of the imputed derivation datasets, we constructed comprehensive RSF models, using all available variables, for prediction of 2 years OS. The importance of variables in each model was determined using the minimal depth method. A list of variables, ordered by average importance across models is presented in Supplementary Fig. S1. A threshold for variable importance was determined through a nested RSF approach  (21) , estimating a cut-off for predictive contribution by serially introducing variables into RSF models according to their importance, while measuring improvement in the C-index.
Risk score construction The top predictors and grouped interactions were introduced into a Cox model. Hazard ratio estimates were pooled over 20 imputed derivation datasets using Rubin's rules  (22). The weight for each predictor was determined according the hazard ratio (HR ¼ 1-1. 49 ¼ 1; HR ¼ 1. 5-2. 49 ¼ 2; HR ¼ 2. 5-3. 49 ¼ 3), providing P < 0. 1. The score was categorized, and intervals representing outliers were grouped. The associated risk for 2 years OS, LFS, NRM, and relapse incidence was calculated and plotted on a randomly selected imputed derivation dataset and its corresponding validation set (i. e. , the derivation and validation sets were produced in the same iteration of the multiple imputation process). Calibration and discrimination, assessed with the time-dependent AUC  (12, 23) , were used to evaluate the score's quality.
Predictor identification and ranking Minimal depth, a dimensionless order statistic measuring the predictiveness of a variable in a survival tree, was used to estimate variable importance across the derivation datasets (Appendix 1). Disease status was consistently the most influential factor, followed by age and cell dose (TNCs cryopreserved; Supplementary Fig. S1 ). To determine a cut off for the variables' cumulative predictive contribution (i. e. , the minimum number of variables required maximal predictive performance), a nested RSF approach was applied  (21). RSF prediction models were constructed on serially introduced variables, according to their minimal depth ranking. Predictive performance on the derivation datasets, as measured by the C-index, incrementally improved until reaching a plateau after introducing the top 10 ranked variables (disease status, TNCs cryopreserved, age, center experience, interval from diagnosis to UCBT, year of UCBT, CMV serostatus, degree of HLA mismatch, previous autograft, and ATG administration; Fig.
The UCBT risk score For the score construction, we introduced the top predictors of 2 years overall survival into a Cox multivariate model. Age, cell dose (TNCs cryopreserved) and center experience were categorized into two groups according to acceptable thresholds  (7, 24). Although not considered among top predictors, we included diagnosis into the model, as it carries important clinical information. The score was developed on all 20 versions of the imputed derivation datasets. In a preliminary phase, a randomly selected version of one of the imputed derivation datasets was used to analyze interactions; coplots derived from a RSF model showed that increasing age was associated with inferior predicted survival in the groups receiving ATG and that the effect of disease status was diagnosis dependent (Supplementary Fig.
S2 ). These interactions were validated in a Cox model (Tables  S2  and S3 ). The UCBT risk score was derived from pooled estimates of Cox models over multiple imputed datasets (Table  2 ; ref. 22). The models included independent predictors and variables grouping interactions (ATG administration conditioned on age group and disease status conditioned on diagnosis). The variable year of UCBT was kept in the models for covariate adjustment, but excluded from the score. Duration from diagnosis to UCBT was not included in the Cox model as it is highly dependent on disease and disease status, resulting in complex three-way interactions with 16 possible combinations. Furthermore, its addition to the Cox models had minimal impact on the model's performance (i. e. , the difference in the Akaike's Information Criterion between the two models was 0.
04%). On a randomly selected derivation dataset and its corresponding validation set (patients' characteristics are listed in Table  S4 ), the UCBT score ranged from 0 to 8 (Table  3 ). Calibration was excellent (Supplementary Fig. S3 ). An increasing score corresponded with increasing hazard and decreasing probability for 2 years OS and LFS (log-rank P < 0. 001 for both, Table  4  and Supplementary Table  S5 ). Over the validation cohort, probabilities of 2 years OS and LFS ranged from 70. 21% (95% CI, 68. 89-70. 71) and 64. 76% (95% CI, 64. 33-65. 86), respectively, for patients having a score of 0 to 1, to 14. 78% (95% CI, 10. 91-17. 41) and 18. 11% (95% CI, 14. 40-22. 30) for a score of 6 to 8 (Fig. 2 ). Increasing scores were also associated with a greater probability for 2 years NRM, ranging from 14.
10% to 56. 45%. However, considerable overlap was noted between patients assigned scores of 3 to 5 (Table  4 ). Similar results were obtained when analyzing all versions of the imputed validation datasets (Supplementary Table  S6 ). Patients assigned with a score of 0 to 1 were more likely be in first CR (78. 1%), receive 3 Â 10 7 /kg or more TNCs (99. 4%), have donors with 0 to 1 HLA mismatches (84. 3%), and be transplanted in centers performing a high number of UCBT/year (89. 6%). Very high-risk patients, with scores of 6 to 8 were transplanted mainly in late CRs or advanced disease 87. 3%), be adults receiving ATG (72. 0%), have a donor with over one HLA mismatch (79. 9%), and transplanted in center performing 20 or less UCBT/year (60. 8%; Table  S7 ). The score's median discrimination (AUC) for 2 years OS overall imputation of the.
g. , when haploidentical or 9/10 HLA unrelated donors are not available), whereas high-volume centers may choose to pursue UCBT as a valid option early on. Other discriminating factors were CMV seropositivity (88. 4%) and two or more than one HLA mismatch (79. 9%) in the group with the highest risk. Patients with scores over 5 should be carefully evaluated taking into account the associated increase in risk. The study has several limitations. First, the UCBT score was not validated in an external cohort. Nonetheless, geographical validation (i. e. , the transplantation centers' countries in the derivation and validation dataset differed) was used to ensure generalizability of the results  (12). Also, the UCBT score weights were assigned by pooling Cox estimations over multiple versions of imputed derivation datasets  (22).
The UCBT risk score and validation datasets was 68. 76 (range ¼ 68. 19-69. 04) and 68. 12 (range ¼ 67. 43-68. 69), respectively. Similarly, for 2 years LFS the median AUC was 66. 68 (range ¼ 66. 05-66. 98) and 65. 78 (range ¼ 65. 20-66. 28), respectively.
Acknowledgments This study was supported by  The Varda and Boaz Dotan Research Center in Hemato-Oncology  affiliated with the  CBRC of Tel Aviv University and The Shalvi Foundation for the Support of Medical Research. The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U. S. C. Section 1734 solely to indicate this fact.
Experimental protocol Each component x, y, z of the coordinates of the calibration and control markers -hereafter referred to as 'metric measurements' -were measured using a measuring tape relatively to a predetermined origin marker (0, 0, 0); the precision is 0. 5 cm. Pixel coordinates of the calibration cage and control tool were then manually determined using a motion analysis freeware (Kinovea, https://www. kinovea. org). Using MatLab, we ran a calibration and reconstruction algorithms based on DLT equations. The entire calibration process takes less than 20 minutes of time. The sample standard deviation of the differences between metric and DLT values was calculated using Root Mean Squared Error (RMSE) method. This calculation as well as the statistical analysis described below was applied to all components (x, y, z) confounded, reflecting the overall accuracy  (Kwon and Fiaud 2002).
Acknowledgements The authors declare no potential conflicts of interests. The authors wish to thank the management of  Ecole Supérieure d'Ostéopathie-Paris especially Roger, Christophe  and  Olivier Caporossi  for providing all the necessary equipment for this study;  Serge Pin ,  Laurent Stubbe  and  Jeanine Sanchez  for all their support;  Laurent Gaillard ,  Denis Boulanger  and  Pascal Masson  for their technical support.
1 Introduction 20 years ago getting access to large visual data sets for research was a problem and open data collections that could be used to compare algorithms of researchers were rare. Now it is getting easier to access data collections but it is still hard to obtain annotated data with a clear evaluation scenario and strong baselines to compare to. Motivated by this, ImageCLEF has for 15 years been an initiative that aims at evaluating multilingual or language independent annotation and retrieval of images  [15, 18, 5, 24]. The main goal of ImageCLEF is to support the advancement of the field of visual media analysis, classification, annotation, indexing and retrieval. It proposes novel challenges and develops the necessary infrastructure for the evaluation of visual systems operating in different contexts and providing reusable resources for benchmarking, which is also linked to initiatives such as Evaluation as a Service (EaaS)  [11].
Many research groups have participated over the years in these evaluation campaigns and even more have acquired its datasets for experimentation. The impact of ImageCLEF can also be seen by its significant scholarly impact indicated by the substantial numbers of its publications and their received citations  [22]. There are other evaluation initiatives that have had a close relation with ImageCLEF. LifeCLEF  [14]  was formerly an ImageCLEF task. However, due to the need to assess technologies for automated identification and understanding of living organisms using data not only restricted to images, but also videos and sound, it was decided to be organised independently from ImageCLEF. Other CLEF labs linked to ImageCLEF, in particular the medical task, are: CLEFeHealth  [10]  that deals with processing methods and resources to enrich difficult-to-understand eHealth text and the BioASQ  [3]  tasks from the Question Answering lab that targets biomedical semantic indexing and question answering but is now not a lab anymore.
Due to their medical topic, the organisation is coordinated in close collaboration with the medical tasks in ImageCLEF. This paper presents a general overview of the ImageCLEF 2017 evaluation campaign  1  , which as usual was an event organised as part of the CLEF labs  2. Section 2 presents a general description of the 2017 edition of ImageCLEF, commenting about the overall organisation and participation in the lab. Followed by this are sections dedicated to the four tasks that were organised this year. Section 3 explains all details on the life logging task; Section 4 details the caption prediction task; Section 5 describes the two subtasks for the tuberculosis challenge and the pilot task on remote sensing data is described in Section 6. For the full details and complete results, the readers should refer to the corresponding task overview papers  [6, 9, 7, 2].
Overview of Tasks and Participation ImageCLEF 2017 consisted of three main tasks and a pilot task that covered challenges in diverse fields and usage scenarios. In 2016  [25]  the tasks were completely different with a handwritten retrieval task, an image annotation task and a medical task with several subtasks. In 2017 the tasks completely changed and only the caption prediction was a subtask already attempted in 2016 but for which no participant submitted results in 2016. The 2017 tasks are the following: -ImageCLEFlifelog: aims at developing systems for lifelogging data retrieval and summarization, so for persons automatically logging their life. -ImageCLEFcaption: addresses the problem of bio-medical image caption prediction from large amounts of training data. Captions can either be created as free text or concepts of the image captions could be detected.
-ImageCLEFtuberculosis: targets the challenge of determining the tuberculosis (TB) subtypes and drug resistances automatically from the volumetric image information (mainly related to texture) and based on clinical information that is available such as age, gender, etc. -ImageCLEFremote (pilot task): targets the estimation of the population of a geographical area based on low definition but free earth observation images as provided by Copernicus program. In order to participate in the evaluation campaign, the groups first had to register either on the CLEF website or from the ImageCLEF website. To actually get access to the datasets, the participants were required to submit a signed End User Agreement (EUA). Table  1  summarizes the participation in Image-CLEF 2017, including the number of registrations and number of signed EUAs, indicated both per task and for the overall lab.
The table also shows the number of groups that submitted results (a. k. a. runs) and the ones that submitted a working notes paper describing the techniques used. The number of registrations could be interpreted as the initial interest that the community has for the evaluation. However, it is a bit misleading because several people from the same institution might register, even though in the end they count as a single group participation. The EUA explicitly requires all groups that get access to the data to participate, even though this is not enforced. Unfortunately, the percentage of groups that submit results is often relatively small. Nevertheless, as observed in studies of scholarly impact  [22, 23] , in subsequent years the datasets and challenges provided by ImageCLEF do get used quite often, which in part is due to the researchers that for some reason were unable to participate in the original event.
After a decrease in participation in 2016, the participation increased well in 2017 and this despite the fact that all four tasks did not have a participating community as all tasks were new and had to create the community from scratch. Still, of the 167 groups that registered and 60 that submitted a valid copyright agreement, only 27 submitted results in the end. The percentage is in line with past years with 20% of the registered groups submitting results and about 50% of those that signed the agreement. The following four sections are dedicated to each of the tasks. Only a short overview is reported, including general objectives, description of the tasks and datasets and a short summary of the results. 3 The Lifelog Task.
Data Sets Used The Lifelog dataset consists of data from three lifeloggers for a period of about one month each. The data contains a large collection of wearable camera images (approximately two images per minute), an XML description of the semantic locations (e. g. Starbucks cafe, McDonalds restaurant, home, work) and the physical activities (e. g. walking, transport, cycling), of the lifeloggers at a granularity of one minute. A summary of the data collection is shown in Table  2. Given the fact that lifelog data is typically visual in nature and in order to reduce the barriers-to-participation, the output of the Caffe CNN-based visual concept detector was included in the test collection as additional meta data. Topics Aside from the data, the test collection included a set of topics (queries) that were representative of the real-world information needs of lifeloggers.
Lessons Learned and Next Steps What we learned from the lifelogging task is that multi-modal analysis seems still to be a problem that not many address. Often only one type of data is analysed. For the future it would be important to encourage participants to try out all modalities. This could be achieved by providing pre-extracted features with the data. Apart from that there was a large gap between signed-up teams and submitted runs. We think that this is based on the complexity of the task and the large amount of data that need to be analysed. Supporting participants with pre-extracted features could also help in this case because feature extraction can take much time. Finally, and most importantly, we could show how interesting and challenging lifelog data is and that it holds much research potential, not only in multimedia analysis but also from a system point of view for the performance.
Task Setup The ImageCLEF 2017 caption task  [9]  casts the problem of image understanding as a cross-modality matching scenario in which visual content and textual descriptors need to be aligned and concise textual interpretations of medical images are generated. The task works on the basis of a large-scale collection of figures from open access biomedical journal articles from PubMed Central (PMC)  6. Each image is accompanied by its original caption and a set of extracted UMLS R (Unified Medical Language System R )  7  Concept Unique Identifiers (CUIs), constituting a natural testbed for this image captioning task. In 2016, ImageCLEFmed  [12]  proposed a caption prediction subtask. This edition of the biomedical image captioning task at ImageCLEF comprises two subtasks: (1) Concept Detection and (2) Image Caption Prediction.