dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements This paper is dedicated to  Prof Dr Raphael Mechoulam , the founder of cannabinoid research. M. S. , L. H. H. and M. v. d. S. were supported by an  ECHO-STIP  grant from the  Dutch Research Council-Chemical Sciences. A. M. was supported by the  RPF program. Partial support from  Ministero dell'Istruzione, dell'Universitae della Ricerca  ( PRIN 2010-2011  grant to M. M. ) is gratefully acknowledged. P. P. was supported by the  Intramural Research Program  of  NIH/NIAAA. Mark Rau  (laboratory J. G. ) is acknowledged for measuring AEA uptake inhibition. Pharmacokinetics in vivo laboratories, Drug Disposition and Safety laboratories. Pharmaceutical Sciences pRED and F. Hoffmann-La Roche Ltd, Basel, Switzerland  are acknowledged for performing the in vitro DMPK and in vivo studies.
Colorectal adenocarcinoma originating in intestinal glandular structures is the most common form of colon cancer. In clinical practice, the morphology of intestinal glands, including architectural appearance and glandular formation, is used by pathologists to inform prognosis and plan the treatment of individual patients. However, achieving good inter-observer as well as intra-observer reproducibility of cancer grading is still a major challenge in modern pathology. An automated approach which quantifies the morphology of glands is a solution to the problem. This paper provides an overview to the Gland Segmentation in Colon Histology Images Challenge Contest (GlaS) held at MICCAI'2015. Details of the challenge, including organization, dataset and evaluation criteria, are presented, along with the method descriptions and evaluation results from the top performing methods.
Good quality gland segmentation will pave the way for computer-assisted grading of CRC and increase the reproducibility of cancer grading. However, consistent good quality gland segmentation for all the differentiation grades of cancer has remained a challenge. This was a main reason for organizing this challenge contest. The Gland Segmentation in Colon Histology Images (GlaS) challenge 1  brought together computer vision and medical image computing researchers to solve the problem of gland segmentation in digitized images of Hematoxylin and Eosin (H&E) stained tissue slides. Participants developed gland segmentation algorithms, which were applied to benign tissue and to colonic carcinomas. A training dataset was provided, together with ground truth annotations by an expert pathologist. The participants developed and optimized their algorithms on this dataset.
In contrast to the above method, which mainly uses pixel-level information, Gunduz-Demir et al. [2010]  represented each tissue component as a disk. Each disk is represented by a vertex of a graph, with nearby disks joined by an edge between the corresponding vertices. They proposed an algorithm, using graph connectivity to identify initial seeds for region growing. To avoid an excessive expansion beyond the glandular region, caused, for example, by large gaps in the surrounding epithelial boundary, edges between nuclear objects are used as a barrier to halt region growing. Those regions that do not show glandular characteristics are eliminated at the last step. The validation of this method was limited only to the dataset with healthy and benign cases. Fu et al. [2014]  introduced a segmentation algorithm based on polar coordinates.
This method is slow but effective. Most of the works for intestinal gland segmentation have used different datasets and/or criteria to assess their algorithms, making it difficult to objectively compare their performance. There have been many previous initiatives that provided common datasets and evaluation measures to validate algorithms on various medical imaging modalities  [Murphy et al. , 2011 , Gurcan et al. , 2010 , Roux et al. , 2013 , Veta et al. , 2015]. This not only allows a meaningful comparison of different algorithms but also allows the algorithms to be implemented and configured thoroughly to obtain optimal performance  [Murphy et al. , 2011]. Following these successful initiatives, we therefore organized the Gland Segmentation in Colon Histology Images (GlaS) challenge. This challenge was a first attempt to address the issues of reproducibility and comparability of the results of intestinal gland segmentation algorithms.
Materials The dataset used in this challenge consists of 165 images derived from 16 H&E stained histological sections of stage T3 or T4 2  colorectal adenocarcinoma. Each section belongs to a different patient, and sections were processed in the laboratory on different occasions. Thus, the dataset exhibits high inter-subject variability in both stain distribution and tissue architecture. The digitization of these histological sections into whole-slide images (WSIs) was accomplished using a Zeiss MIRAX MIDI Slide Scanner with a pixel resolution of 0. 465µm. The WSIs were subsequently rescaled to a pixel resolution of 0. 620µm (equivalent to 20× objective magnification). A total of 52 visual fields from both malignant and benign areas across the entire set of the WSIs were selected in order to cover as wide a variety of tissue architectures as possible.
An expert pathologist (DRJS) then graded each visual field as either 'benign' or 'malignant', according to the overall glandular architecture. The pathologist also delineated the boundary of each individual glandular object on that visual field. We used this manual annotation as ground truth for automatic segmentation. Note that different glandular objects in an image may be part of the same gland. This is because a gland is a 3-dimensional structure that can appear as separated objects on a single tissue section. The visual fields were further separated into smaller, non-overlapping images, whose histologic grades (i. e. benign or malignant) were assigned the same value as the larger visual field. Representative example images of the two grades can be seen in Figure  1. This dataset was also previously used in the gland segmentation study by  Sirinukunwattana et al.
[2015]. In the challenge, the dataset was separated into Training Part, Test Part A, and Test Part B. Note that the data were stratified according to the histologic grade and the visual field before splitting. This was done to ensure that none of the images from the same visual field appears in different parts of the dataset (i. e. Training, Test Part A, or Test Part B). However, since the data were not stratified based on patient, different visual fields from the same slide can appear in different parts of the dataset. A breakdown of the details of the dataset is shown in Table  1. The ground truth as well as the histologic grade which reflects morphology of glandular structures were provided for every image in the Training Part at the time of release. We used Test Part A and Test Part B as off-site and on-site test datasets respectively.
Each team could submit up to 3 different methods. The intention of this requirement was for the organizers to identify teams who were serious about participating in the challenge. The organizers based their reviews on two criteria: clarity of the method description and soundness of the validation strategy. Segmentation performance was not considered in this review. The submission of this document was due by July 17 th , 2015. Stage 3: Release of the Test Data Part A and Submission of Segmentation Results The first part of the test data (Test Part A, see Section 3 for details) was released on August 14 th , 2015 to those teams selected from the previous stage which also agreed to participate in the GlaS contest. The teams were given a month to further adjust and optimize their segmentation algorithms, and carry out segmentation on Part A of the test data.
Each team could hand-in up to 3 sets of results per method submitted in Stage 2. The submission of the segmentation results was due by September 14 th , 2015. Evaluation of the submitted results was not disclosed to the teams until after the challenge event. Stage 4: GlaS'2015 Challenge Event The event was held in conjunction with MICCAI'2015 on October 5 th , 2015. All teams were asked to produce segmentation results on the second part of the test data (Test Part B, see Section 3) within 45 minutes. The teams could either bring their own machines or conduct an experiment remotely. There was no restriction on the number of machines that the teams could use to produce results. Those teams that could not be present at the event provided implementations of their algorithms with which the organizers carried out the segmentation on their behalf.
Challenge Statistics By the end of Stage 1, a total of 110 teams from different academic and industrial institutes had registered. A total of 21 teams submitted the 2-page document for review in Stage 2, and 20 teams were invited to participate in the GlaS competition event. In Stage 3, only 13 teams submitted results on Part A of the test data in time. Late entries were neither evaluated nor considered in the next stage of the competition. On the day of the challenge event, 11 of the 13 teams that submitted the results on time in Stage 3 attended the on-site competition and presented their work. The organizers carried out the segmentation on behalf of the other two teams that could not be present.
Detection Accuracy The F1 score is employed to measure the detection accuracy of individual glandular objects. A segmented glandular object that intersects with at least 50% of its ground truth object is counted as true positive, otherwise it is counted as false positive. The number of false negatives is calculated as the difference between the number of ground truth objects and the number of true positives. Given these definitions, the F1 score is defined by F1score = 2 • Precision • Recall Precision + Recall , (1) where Precision = TP TP + FP , Recall = TP TP + FN , (2) and TP, FP, and FN denote respectively the number of true positives, false positives, and false negatives from all images in the dataset.
In order to separate touching glands, the feature maps from hierarchical layers were up-sampled with two different branches to output the segmented object and contour masks respectively. The parameters of the down-sampling path were shared and updated for these two kinds of masks. This could be viewed as a multi-task learning framework with feature representations, simultaneously encoding the information of segmented objects and contours. To alleviate the problem of insufficient training data  [Chen et al. , 2015] , an off-the-shelf model from DeepLab  [Chen et al. , 2014] , trained on the 2012 PASCAL VOC dataset 5  , was used to initialize the weights for layers in the down-sampling path. The parameters of the network were obtained by minimizing the loss function with standard back-propagation 6.
ExB 8 This method first preprocessed the data by performing per channel zero mean and unit variance normalization, where the mean and variance were computed from the training data. The method then exploited the local invariance properties of the task by applying a set of transformations to the data. At training time, the dataset was augmented by applying affine transformations, Gaussian blur and warping. During testing, both image mirroring and rotation were applied. The main segmentation algorithm consisted of a multi-path convolutional neural network. Each path was equipped with a different set of convolutional layers and configured to capture features from different views in a local-global fashion. All the different paths were connected to a set of two fully connected layers. A leaky rectified linear unit was used as a default activation function between layers, and a softmax layer was used after the last fully connected layer.
The applied transformations were random elastic deformations, rotations, shifts, flips, and blurs. The color transformations were random multiplications applied in the HSV color space. To avoid accidentally joining touching objects, a high pixel-wise loss weight was introduced for pixels in thin gaps between objects in the training dataset (see  Ronneberger et al. [2015] ). The exact same u-net layout with the same hyperparameters as in  Ronneberger et al. [2015]  was used for the challenge. The only difference were more training iterations and a slower decay of the learning rate. The team submitted two entries. The first entry Freiburg1 was a connected component labelling applied to the raw network output. The second entry Freiburg2 post-processed the segmentation maps with morphological hole-filling and deletion of segments smaller than 1000 pixels.
Additional Experiments In the challenge, the split of the test data into two parts -Part A (60 images) for off-site test and Part B (20 images) for on-site test -to some extent introduces bias into the performance evaluation of the segmentation algorithms due to equal weight given to performance on the two test parts. The algorithms that perform particularly well on Test Part B would therefore get a better evaluation score even though they may not have performed as well on Test Part A, where the majority of the test dataset is to be found. In addition, the imbalance between the benign and malignant classes in Test Part B, only 4 benign (20%) and 16 malignant (80%) images, would also favor algorithms that perform well on the malignant class. In order to alleviate these issues, we merged the two test parts and re-evaluated the performance of all the entries.
e. , F1score + ARI + H obj ). For both sets of rank sums, the new ranking orders are largely similar to those reported in Section 8. 2, with a few swaps in the order, while the top three entries remaining the same, namely CUMedVision2, ExB1, ExB3. The main factors that negatively affect the performance of the methods are a number of challenges presented by the dataset. Firstly, large white empty areas corresponding to the lumen of the gastrointestinal tract which are not in the interior of intestinal glands can easily confuse the segmentation algorithms (Figure  4a ). Secondly, characteristics of non-glandular tissue can sometimes resemble that of the glandular tissue. For instance, connective tissue in muscularis mucosa or sub-mucosa layers of the colon is stained white and pinkish and has less dense nuclei, thus resembling the inner part of glands (Figure  4b ).
, 2007 , Altunbay et al. , 2010 , Basavanhally et al. , 2010 , Ozdemir and Gunduz-Demir, 2013 , Gultekin et al. , 2015]. However, because these extracted features are often physically less interpretable in the eyes of practitioners, it is difficult to adopt such an approach in clinical settings. On the other hand, the appearance of glands such as size and shape obtained through segmentation is easy to interpret. Segmentation also helps to localize other type of information (e. g. , texture, spatial arrangement of cells) that is specific to the glandular areas. Even though the dataset used in the challenge included images of different histologic grades taken from several patients, it lacked other aspects. First of all, inter-observer variability was not taken into account as the ground truth was generated by a single expert.
This is because the intricate and arduous nature of the problem makes it difficult to find several volunteer experts to perform manual segmentation. Considerable experience is required in order to delineate boundaries of malignant glands, which are not so well-defined as those of the benign ones. Moreover, a single image can contain a large number of glands to be segmented, making the task very laborious. Secondly, digitization variability was also not considered in this dataset. It is, in fact, very important to evaluate the robustness of algorithms when the data are scanned by different instruments. As whole-slide scanners are becoming increasingly available, this type of real-world problem should be expected. The choice of evaluation measures would also affect the comparative results. In this challenge, we emphasized object segmentation and accordingly defined the object-level Dice index and the object-level Hausdorff distance to measure segmentation accuracy at the object level rather than at the pixel level.
Although not directly clinically relevant, F1 score is important in assessing the accuracy of the identified glands. Since the morphological assessment is done on the basis of tissue slide including several thousands of glands, an algorithm with high value of F1 score is more preferable as it can detect a larger number of glands. Gland segmentation algorithms presented here are not ready for deployment into clinic in their present form. Although some of the top algorithms produce good segmentation results for the contest dataset and will probably fare well in the real world, there needs to be a large-scale validation involving data from multiple centers annotated by multiple pathologists before any of these algorithms can be deployed in a diagnostic application. The challenge is now completed, but the dataset will remain available for research purposes so as to continually attract newcomers to the problem and to encourage development of state-of-the-art methods.
Extension of the dataset to address inter-observer and inter-scanner variability seems to be the most achievable aim in the near future. Beyond the scope of segmentation, there lie various extremely interesting future research directions. Previous studies have shown the strong association between the survival of colorectal cancer patients and tumor-related characteristics, including lymphocytic infiltration  [Galon et al. , 2006 , Fridman et al. , 2012] , desmoplasia  [Tommelein et al. , 2015] , tumor budding  [Mitrovic et al. , 2012], and necrosis [Richards et al. , 2012]. A systematic analysis of these characteristics with the help of gland segmentation as part of automatic image analysis framework could lead to a better understanding of the relevant cancer biology as well as bring precision and accuracy into assessment and prediction of the outcome of the cancer.
Conclusions This paper presented a summary of the Gland Segmentation in Colon Histology Images (GlaS) Challenge Contest which was held in conjunction with the 18th International Conference on Medical Image Computing and Computer Assisted Interventions  (MICCAI'2015). The goal of the challenge was to bring together researchers interested in the gland segmentation problem, to validate the performance of their existing or newly invented algorithms on the same standard dataset. In the final round, the total number of submitted entries for evaluation was 19, and we presented here in this paper 10 of the leading entries. The dataset used in the challenge has been made publicly available and can be accessed at the challenge website (http: //www. warwick. ac. uk/bialab/GlasContest/). Those who are interested in developing or improving their own approaches are encouraged to use this dataset for quantitative evaluation.
This paper was made possible by NPRP grant number NPRP5-1345-1-228 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. Korsuk Sirinukunwattana acknowledges the partial financial support provided by the Department of Computer Science, University of Warwick, UK. CUMedVision team acknowledges Hong  Kong RGC General Research Fund (Project No. CUHK 412513). The work of Olaf Ronneberger was supported by the Excellence Initiative of the German Federal and State Governments (EXC 294). Philipp Kainz was supported by the Excellence Grant 2014 of the Federation of Austrian Industries (IV), and Martin Urschler acknowledges funding by the Austrian Science Fund (FWF): P 28078-N33. The authors thank Professor David Epstein for his extensive help with the wording and mathematical notation in this paper, and Nicholas Trahearn for his help with the wording.
X-ray micro-computed tomography and digital volume correlation The μ-CT scans for the DVC analyses were acquired using a custom Nikon/Metris scanner consisting of a 450 kVp power source coupled with a cesium-iodide detector (1621 Perkinelmer). The specimens were positioned 116. 5 mm from the source, resulting in a pixel resolution of 28. 3. Each tomogram comprised 1601 angular projections collected at an angular step of over a rotation of the sample. The projection data was were reconstructed using Nikon's CTPro and CTAgent reconstruction software, which uses a filtered back projection algorithm. Similar to digital image correlation, DVC involves dividing the 3D scan volume into smaller sub-volumes, which can then be tracked between load steps using the internal microstructure of the material  (Bay et al.
, 1999). The new position of the sub-volume centroid is taken to be at a location where the correlation coefficient between the original and displaced sub-volume is best. Here we performed the DVC analysis using DaVis software (LaVision, DaVis v. 8. 2. 3 Software, Goettingen, Germany) by comparing the three strained states back to the reference scan at. The strain fields are then determined from the spatial gradients of displacement vector fields of the sub-volumes over a gauge length equal to 50% of the sub-volume size, as described in  Gillard et al. (2014). A noise study was performed using sub-volume sizes between 24 to 192 voxels (based on the resolution of the μ-CT scan each voxel represents a cube of side length )  (Gillard et al. , 2014). A 150 voxel subvolume size with 50% overlap gave the best compromise between noise and an adequate spatial resolution for strain in these specimens.
8% to 14%). All calculations were performed using the commercial finite element (FE) package Abaqus FEA and the unit cell discretised via 10-noded tetrahedral element (C3D10 in the Abaqus FEA notation) with a minimum element size , where is the diameter of the spherical particles. The mesh is chosen to be denser in the narrow inter-particle channels, and coarser inside the elastic particles. All calculations are carried out in a finite strain setting. We emphasise here that loading is specified in terms of the Cauchy stresses and the corresponding triaxiality , consistent with stress measures employed in the porous matrix constitutive model. However, we present the stress predictions in terms of the 2 nd Piola-Kirchhoff stress , where is the macroscopic deformation gradient (i. e. volume average of the deformation gradient over the unit cell) and , in order to be consistent with the measurements discussed in Section 3.
Acknowledgements The authors are grateful to the  Office of Naval Research (ONR)  for their financial support through grant number  N000141210637  on Nickel bonded TiC composites for dynamic blast mitigation applications (program manager  Dr. David Shifler ). We also acknowledge the μ-VIS centre at the  University of Southampton  for provision of tomographic imaging facilities, supported by  EPSRC  grant  EP-H01506X.
Objectives We aimed to test whether a brief, opportunistic intervention in general practice was a feasible and acceptable way to engage with bowel screening nonresponders. Design This was a feasibility study testing an intervention which comprised a brief conversation during routine consultation, provision of a patient leaflet and instructions to request a replacement faecal occult blood test kit. A mixed-methods approach to evaluation was adopted. Data were collected from proformas completed after each intervention, from the Bowel Screening Centre database and from questionnaires. Semi-structured interviews were carried out. We used descriptive statistics, content and framework analysis to determine intervention feasibility and acceptability. Participants Bowel screening non-responders (as defined by the Scottish Bowel Screening Centre) and primary care professionals working in five general practices in Lothian, Scotland.