dataesr/scientific-paragraphs-categorization huggingface dataset:
We also propose discriminative model extensions to map the output of the generative model to arbitrary labels with semantic and biological meaning, such as "tumor core" or "fluid-filled structure", but without a one-to-one correspondence to the hypo-or hyper-intense lesion areas identified by the generative model. We test the approach in two image sets: the publicly available BRATS set of glioma patient scans, and multimodal brain images of patients with acute and subacute ischemic stroke. We find the generative model that has been designed for tumor lesions to generalize well to stroke images, and the generative-discriminative model to be one of the top ranking methods in the BRATS evaluation. Personal use is permitted, but republication/redistribution requires IEEE permission. To support the further use and analysis of our generative segmentation algorithm, we make an implementation available in Python from http://ibbm.
I. Introduction Gliomas are the most frequent primary brain tumors. They originate from glial cells and grow by infiltrating the surrounding tissue. The more aggressive form of this disease is referred to as "high-grade" glioma. The tumor grows fast and patients often have survival times of two years or less, calling for immediate treatment after diagnosis. The slower growing "low-grade" disease comes with a life expectancy of five years or more, allowing the aggressive treatment to be delayed. Extensive neuroimaging protocols are used before and after treatment, mapping different tissue contrasts to evaluate the progression of the disease or the success of a chosen treatment strategy. As evaluations are often repeated every few months, large longitudinal datasets with multiple modalities are generated for these patients even in routine clinical practice.
The discriminative classifier uses the output of the generative model, which improves its robustness against intensity variation and changes in imaging sequences. This generative-discriminative model defines the state-of-the-art on the public BRATS benchmark data set  [1]. In the following we introduce the probabilistic model (Section II), derive the segmentation algorithm and additional biological constraints, and we describe the discriminative model extensions (Section III). We evaluate the properties and performance of the generative and the generative-discriminative methods on a public glioma dataset (Sections IV and V, respectively), including an experiment on the transfer of the generative model to images from stroke patients. We conclude with a discussion of the results and of future research directions (Section VI).
Similar to  [51] , we modify the probability that tumor is visible in channel c of voxel i by comparing the observed image intensity with the previously estimated prior to calculating updates for parameters θ (Eq. 16). We set the probability to zero if the intensity does not align with our expectations: 1 An implementation of the generative tumor segmentation algorithm in Python is available from http://ibbm. in. tum. de. ( ) 17 For hypo-intensity constraints we modify the posterior probability updates in the same way, using as a criterion. Spatial regularity of the tumor prior-Little spatial context is used in the basic model, as we assume the tissue class s i in each voxel to be independent from the class labels of other voxels. Atlas π k encourages spatially continuous classification for the healthy tissue classes by imposing similar priors in neighboring voxels.
To this end we identify all R isolated regions in the binary tumor map of the FLAIR image (containing voxels i with. We choose FLAIR since it is the most inclusive image modality. As artifacts may be connected to lesion areas, we over-segment larger structures using a watershed algorithm, subdividing regions with connections that are less than 5mm in diameter to reduce the number mixed regions containing both tumor pixels and artifact patterns. For each individual region r ∈ 1 … R we calculate a feature vector x r that includes volume, surface area, surface-to-volume ratio, as well as regional statistics that are minimum, maximum, mean and median of the normalized image intensities in the four channels. We scale the image intensities for each channels linearly to match the distribution of intensities in a reference data set.
A. Data and Evaluation Glioma data-We use the public BRATS 2012-2013 dataset that provides a total of 45 annotated multimodal glioma image volumes  [1]. Training datasets consist of 10/20 low/ high-grade cases with native T1, Gadolinium-enhanced T1 (T1c), T2 and FLAIR MR image volumes. The test dataset contains no labels, but can be evaluated by uploading image segmentations to a server; it includes 4/11 low-grade/high-grade cases. Experts have delineated tumor edema, Gadolinium-enhancing "active" core, non-enhancing solid core, cystic/necrotic core. We co-register the probabilistic MNI tissue atlas of SPM99 with the T1 image of each dataset using the FSL software, and sampled to 1mm 3 isotropic voxel resolution. We perform a bias field correction using a polynomial spline model (degree 3) together with a multivariate tissue segmentation using an EM segmenter that is robust against lesions 2   [51].
Image intensities of each channel in each volume are scaled linearly to match the histogram of a reference. Stroke data-Images are acquired in patients with acute and subacute ischemic stroke. About half of the 18 datasets comprise T1, T2, T1c and FLAIR images in patients in the sub-acute phase, acquired about one or two days after the event; another half comprises T1, T1c, T2 base diffusion and mean diffusivity (MD) images acquired in acute stroke patients within the first few hours after the onset of symptoms. For both groups the imaging sequences return tissue contrasts of normal tissues and lesion areas that are similar to hyperand hypo-intensities expected in glioma sequences; stroke lesions are characterised here by T1 hypo-, T1c hyper-, T2 hyper-and FLAIR /MD hyper-intense changes. For the quantitative evaluation of the algorithm, we delineate the lesion in every 10th axial, sagittal, and coronal slice, in each of the four modalities.
In addition, we annotate about 10% of the 2D slices twice to estimate variability. We register the probabilistic atlas and perform a model based bias field correction as for the glioma data. Image intensities are scaled to the same reference as for the glioma cases. Evaluation-To measure segmentation performance in the experiments with this dataset, we combine the set of four tumor labels (edema and the three tumor core subtypes) to one binary "complete lesion" label map. We compare this map with the hyper-intense lesion as segmented in T2 and FLAIR. Separately, we compare the "enhancing core" label map with the hyper-intense lesion as segmented in T1c MRI. Quantitatively, we calculate volume overlap between expert annotation A and predicted segmentation B using the Dice score. We compute Dice scores for whole brain when testing performances on the BRATS data set.
B. Model Properties and Evaluation on the BRATS data set Comparison of generative modelling approaches-We compare the proposed generative model against related generative tissue segmentations models and evaluate the relevance of individual components of our approach on the BRATS training data set. We calculate Dice scores in the area containing the lesion and the 3cm margin. Figure  3A  illustrates the benefit of the proposed multivariate tumor and tissue segmentation over a univariate segmentations that treat tumor voxels as intensity outliers similar to Van Leemput's EM segmentation approach for white matter lesion  [51]. On the given data this baseline approach leads to a high number of false positives, either requiring stronger spatial regularization or a more adaptive tuning of the outlier threshold.
C. Generalization Performance and Transfer to the Stroke Data Set We test the generalization performance of the generative model by using it for delineating ischemic stroke lesions that are similar in terms of lesion size and clinical image information. We apply the generative model as optimized for the BRATS dataset to the stroke images. As a single modification we allow T1c lesions to be both inside the FLAIR and T2 enhancing area and outside, as bleeding (which leads to the T1c hyper-intensities) may not coincide with the local edema. Stroke images contain cases with both active and chronic lesions with significantly different lesion patterns. Although datasets, imaging protocol, and even major acquisition parameters differ, we obtain results that are comparable to the tumor data. We calculate segmentations accuracies in the lesion area and observe good agreement between manual delineation and automatic segmentation in all four modalities (Fig.
5 ). We also observe false positives at the white matter-gray matter interfaces, similar to those we observed for the glioma tests data (Fig. 4 ). Most false positive regions are disconnected from the lesion and could be removed with little user interaction or postprocessing. Inter-rater differences and performance of the algorithm are comparable to those from the glioma test set, with Dice scores close to. 80 for segmenting the edema and around. 50-. 60 for T1c enhancing structures (Table  I ). Results on the stroke data underline the versatility of the generative lesion segmentation model and its good generalization performance not only across different imaging sequences, but also across applications. To the best of our knowledge this is one of the first attempts to automatically segment ischemic stroke lesion in multimodal images using a generative model.
Repeated for each feature of all trees in the decision forest, this measure helps to rank the features and to compare the relevance as shown in Figure  6. In our test we augment the dataset with a random feature (random samples from a Gaussian distribution with mean 0 and standard deviation of 1) to establish a lower baseline of the relevance score. For each feature we compare the distribution of changes in classification error against the changes of this random feature in a paired Cox-Wilcoxon test. We analyze feature relevance in a cross-validation on the BRATS training set. Results for the first discriminative model acting at the regional level are shown in Fig. 6. We find plausible features to be relevant: the relative location of the region with respect to the center of the brain (indicated as center_x, center_y, center_z in the figure), the surface-tovolume ratio (border2area), the total number of lesions visible for the given patient (num_lesions), the ratio of segmented voxels in T1c (tumor T 1cN), and some descriptors of image intensities, such as the minimum in FLAIR (int4_min), the maximum, median and average of the T2 intensities (int3_*), as well as the maximum in T1c (int1_max) and the minimum in T1 (int2_min).
VI. Summary and Conclusions In this paper, we extend the atlas-based EM segmenter by a latent atlas class that represents the probability of transition from any of the "healthy" tissues to a "lesion" class. In practice, the latent atlas serves as an adaptive prior that couples the probability of observing tumorinduced intensity changes across different imaging channels for the same voxel. Using the standard brain atlas for healthy tissues together with the highly specific multi-channel information provides us with segmentations of the healthy tissues surrounding the tumor, and enables us to automatically segment the images. The proposed generative algorithm produces outlines of the tumor-induced changes for each channel which makes it independent of the multimodal imaging protocol. We complement the basic probabilistic model with a discriminative model and test two different modeling strategies, both of them addressing shortcomings of the generative model, and find the resulting discriminativegenerative model to define the state of the art in tumor segmentation on the BRATS data set  [1].
Acknowledgments This research was supported by the  The National Alliance for Medical Image Analysis (NIH NIBIB   NAMIC U54-EB005149 ), The Neuroimaging Analysis Center ( NIH NIBIB NAC P41EB015902 ),  NIH NIBIB  ( R01EB013565 ), the  Lundbeck Foundation  ( R141-2013-13117 ), the  European Research Council  through the  ERC Advanced Grant MedYMA   2011-291080  (on Biophysical Modeling and Analysis of Dynamic Medical Images), the  Swiss NSF  project  Computer Aided and Image Guided Medical Interventions (NCCR CO-ME ), the  German Academy of Sciences Leopoldina  ( Fellowship Programme   LPDS 2009-10 ), the  Technische Universität München -Institute for Advanced Study  (funded by the  German Excellence Initiative  and the  European Union Seventh Framework Programme  under grant agreement n  291763 ), the  Marie Curie COFUND program  of the the  European Union  ( Rudolf Mössbauer Tenure-Track Professorship  to BHM).
We then used a simple cut-off to classify 28 clones as being capable of causing a strong Hipi phenotype (termed, "Hipi clones"; Additional file 3: Table  S3 ). We used sequencing and Clone Mapper  [28]  to verify the identity of the Hipi clones and determine their putative target genes (Additional file 3: Table  S3 ). These included bus-2 and bus-12, which respectively encode a galactosyltransferase and a sugar transporter required for the post-translational modification of surface-exposed proteins  [29, 30]. In a detailed analysis, we previously demonstrated that abrogating bus-2 or bus-12 function increases spore binding to the nematode cuticle  [31]. To address the question of whether the infectious burden of spores affected the strength of reporter gene expression, we exposed wild-type worms carrying nlp-29p::gfp to varying doses of D.
Global functional analyses Returning to a more global analysis, when we submitted the list of Nipi genes (except his and msp genes) to a WormNet analysis, 231 formed an intensely interconnected network with an average of 11. 1 edges/node (Fig. 4 ). To characterize this broader cellular signaling network, we ran the lists of candidate targets through an Expression Analysis Systematic Explorer (EASE) analysis  [45] , using our in-house database of functional annotations  [46]. A number of classes were significantly enriched (P < 10 -3 ; Additional file 7: Table  S6 ). Several were derived from early genome-wide ChIP-seq studies produced by the model organism encyclopedia of DNA elements (modENCODE) consortium. We did not exploit this data further since its reliability has recently been questioned by the consortium itself  [47].
As for all large-scale data mining, there are obvious omissions, due to incomplete coverage in databases. One example is nsy-1 that encodes a MAP3K  [122]   3 ). This list of 33 MAPK-related genes overlapped well (8/33) with the list of genes in class 7, reported to be required for multiple cytoprotective responses (i. e. , regulators of gst-4 (detoxification), hsp-4 (endoplasmic reticulum unfolded protein response (UPR)), hsp-6 (mitochondrial UPR,UPR mt ) and sod-3 (reactive oxygen species (ROS) response)  [49] ). There was an equivalent overlap (8/33) with the targets of clones able to suppress over-expression of gpdh-1p::gfp seen in osm-8 mutant worms (class 3). As discussed further below, this gives a further indication of the degree of imbrication of MAPK signaling with different cellular homeostatic processes.
Foundling and orphan genes A total of 33 genes were not found to be associated with any EASE class (Table  5 ; Additional file 7: Table  S6 ). A WormNet analysis failed to reveal any significant connection between the members of this group (area under the curve = 0. 5; P = 0. 3). Inspection of this list, however, revealed it to include two genes known to play specific and important roles in the regulation of antimicrobial peptide gene expression, namely dcar-1 and sta-2, which encode, respectively, a DAMP receptor  [20]  and a STAT-like transcription factor  [7]. Our EASE database  [46] , which currently contains more than 500 classes, has been built up by manual annotation and is necessarily biased to categories that we expect to be of interest in our studies. In an attempt to overcome this limitation, we assembled a far more complete and unbiased collection of functional classes, extracting data automatically from multiple sources including WormBase, FlyBase  [51] , KEGG  [52] , and the relevant RNAi databases  [53, 54] , and combined this with our EASE database to give a collection of more than 3700 classes of genes.
Even using this collection, we failed to find any significant enrichment for the group of 33 genes. Genes for which homologues are found only in a specific taxonomic group, irrespective of the level (e. g. , animals, nematodes, or Caenorhabditis), are called taxonomically-restricted genes (TRGs). TRGs that are restricted to a very narrow taxonomic group, generally a species, can be called orphan genes  [55]. Here, we have described a group of genes for which there is no pertinent functional data in a wide range of publically available databases. In a sense, it is as if these genes have been abandoned. By analogy with the term orphan gene, we apply here the term "foundling gene" to them. The failure to connect the foundling genes could be because they are not in reality linked to each other in any way, or, the explanation we favor, they collectively play specific roles in nematode epidermal defense against fungal infection, which has not hitherto been   3  for class labels; full data in Additional file 7: Table  S6 ; 15 genes from the 245 candidates, present only in one or more of the classes 1, 9, 14, and 31 are not shown).
High-throughput RNAi screen The RNAi screen was performed as previously described in detail  [19, 83]. Briefly, synchronized L1 larvae were deposited in 96-well plates containing nematode growth media agar, with a different RNAi clone in each well. After 30 hours at 25 °C, when worms had reached the L3-L4 stage, a fresh solution of D. coniospora spores was added to each well, and worms were harvested 18 hours later for analysis using the COPAS Biosort. All data was stored in a custommade database (Modul-Bio, Marseille, France) for subsequent analysis. Evaluation of the capacity of RNAi clones to block the increase in nlp-29p::gfp expression provoked by osmotic stress was performed as described  [9]. Briefly, following culture on RNAi clones for 48 h, young adult worms were transferred into 96well U-bottom plates containing 200 μL of 300 mM NaCl and gently agitated for 3 hours at 25 °C before Biosort analysis.
Bioinformatic analyses All analyses used the WS240 WormBase release, unless otherwise stated. Programs were written in Perl and the user interface was developed using HTML, PHP, JavaScript, and MySQL. We used WormNet v3  [42] , EASE 2. 0  [45]  with an in-house database of functional annotations  [46] , GOrilla  [48]  with the November 2015 data update, and KEGG  [52]  release 77. 1. For clustering, we used "One minus Pearson correlation" distance matrices within GENE-E (www. broadinstitute. org/cancer/software/ GENE-E/).
Data collection In addition to the previously collected datasets for C. elegans functional classes used in EASE analyses and manually assembled from the literature, including differential transcriptomic and proteomic data, miRNA targets, TF targets etc. , further classes were defined using data from a variety of resources. If more than one ortholog for a given fly gene was predicted, using an in-house perl script, we selected a best hit if the difference in the DIOPT score was greater than or equal to 2 (maximum score 10), but otherwise did not retain a worm ortholog. 4) Fly RNAi screen data was taken from http://www. flyrnai. org/RNAi_all_hits. txt (downloaded  14-11-14). RNAi clones for which no target gene was listed were excluded. Prediction of C. elegans orthologs was as above. 5) We downloaded all RNAi screen datasets for Drosophila from version 13 of GenomeRNAi.
org  [54]  and manually collated very similar classes to give 110 datasets. Prediction of C. elegans orthologs was as above. 6) We extracted the genes corresponding to 136 KEGG_pathways (July 2015) and then converted identifiers to WormBase GeneIDs. The resulting datasets are available on request. A separate collection of predicted C. elegans proteinprotein complexes was also assembled using experimentally determined protein complexes from other species  [61] [62] [63] [64]. Prediction of C. elegans orthologs was as above except that, if the difference in the DIOPT score was less than or equal to 2, the top two putative orthologs were retained.
Phylogenetic profiling To construct phylogenetic profiles, we followed an approach somewhat similar to that of Tabach et al. [57]. We collected data for a wide range of eukaryotic species. We downloaded the complete set of predicted proteins for 66 vertebrates from Ensembl (release 78) and for 55 invertebrates, 53 fungi, 32 plants, and 32 protists from Ensembl genomes (release 25). As many genes have multiple isoforms (e. g. , 30,939 for 20,493 protein-coding genes in C. elegans), we chose the longest transcript for each gene. We used BLASTP to compare the proteins predicted for the 33 C. elegans foundling genes against all 238 proteomes and we chose the best hit for each. From this, we generated a "BestHit" matrix  (33 × 238) , where each entry C ij is the best BLAST bit score of the top hit in species "j" for C.
We extracted all 1094 phenotypes available in WormBase WS246 from ftp://ftp. wormbase. org/ pub/wormbase/. 2) A total of 1221 expression cluster datasets (WS246) were downloaded from ftp://caltech. wormbase. org/ pub/wormbase/spell_download/. 3) We extracted the full list of Drosophila phenotypes from FlyBase release FB2014_06 via http://flybase. org/. bin/cvreport. html?cvterm=FBcv:0000347 +childdepth=2, and manually collated closely related classes to give a list of 145 to which we matched the corresponding FlyBase Gene IDs. Using the DRSC Integrative Ortholog Prediction Tool (DIOPT) (http://www. flyrnai. org/cgi-bin/DRSC_orthologs. pl), we identified the C. elegans orthologs for these genes.
Acknowledgments We thank  Ben Lehner ,  Mirko Francesconi ,  Nobert Perrimon ,  Thomas Richardson ,  Robin Evans ,  Jerome Reboul ,  Peter Sarkies ,  Yanhui Hu , and staff at the  Drosophila RNAi Screening Center ;  Todd Harris ,  Kimberly van Auken , and staff at WormBase, for discussion and advice;  Justine Melo  and  Emily Troemel  for the kind gift of strains;  Clement Ghigo  and  Sophie Cypowyj  for their contributions;  Pierre Golstein  and  Yishi Jin  for critical reading of the manuscript; and the bioinformatics platforms of the CIML and the  Laboratoire d'Informatique Fondamentale de Marseille  for providing computing resources, database, and web servers. We are grateful to system administrators  Manuel Bertrand  and  Kai Poutrain  for their support and input, and the anonymous reviewers for their very constructive criticisms.
Funding This work was funded by institutional grants from  AMU ,  INSERM  and  CNRS , program grants from the  PACA Regional Council , the  ANR  ( MIME-2007 ,  ANR-12-BSV3-0001-01 ,  ANR-11-LABX-0054  ( Investissements d'Avenir-Labex INFORM)  and  ANR-11-IDEX-0001-02  ( Investissements d'Avenir-A*MIDEX) ). BS was supported in part by the  Fondation ARC.
1 ) 1 We extracted all 1094 phenotypes available in WormBase WS246 from ftp://ftp. wormbase. org/ pub/wormbase/. 2) A total of 1221 expression cluster datasets (WS246) were downloaded from ftp://caltech. wormbase. org/ pub/wormbase/spell_download/. 3) We extracted the full list of Drosophila phenotypes from FlyBase release FB2014_06 via http://flybase. org/. bin/cvreport. html?cvterm=FBcv:0000347 +childdepth=2, and manually collated closely related classes to give a list of 145 to which we matched the corresponding FlyBase Gene IDs. Using the DRSC Integrative Ortholog Prediction Tool (DIOPT) (http://www. flyrnai. org/cgi-bin/DRSC_orthologs. pl), we identified the C. elegans orthologs for these genes.