dataesr/scientific-paragraphs-categorization huggingface dataset:
Classification For each of the 7 experiments and each of the 3 segmentations (BLAST-CT/4-classes manual/7-class manual), we trained a classifier to predict whether patients belong to the TILsum_Low or the TILsum_high group. Our classifier was designed using the PhotonAI toolbox  30  , which proposes machine learning pipelines, containing data preprocessing, data augmentation, feature selection, hyperparameter optimization and model evaluations. Due to the small number of patients in our dataset, we used a state-of-the-art method: a nested cross-validation  31, 32  , on the train and validation sub-datasets to assure statistical robustness in the tuning and evaluation of our classifiers. This procedure is detailed on Supplementary Figs. S1  and S2.
Ethics approval and consent to participate The study Radiomic-TBI involving human participants was reviewed and approved by the French institution Comité de protection des personnes (Radiomic-TBI cohort; NCT04058379, first posted: 15 august 2019). Informed consent was obtained from all subjects and/or their legal guardian(s). The study RadioxyTC was also allowed by the French Direction de la Recherche Clinique et de l'Innovation and registered on the Health Data Hub (Radioxy-TC cohort; Health Data Hub index F20220207212747, first posted: 7 February 2022). Patients were individually informed, but no written informed consent was required, although patients had the opportunity to decline their participation in the study. These studies were carried out in accordance with the french regulation.
Cohort characterization The cohort characterization can be found on Table  1 , for both groups TILsum_High and TILsum_Low used for the classification task. One can observe the unbalanced distribution of men and women and of antiaggregants in the two groups. As expected, the imaging scores (Marshall and Rotterdam) are lower in the TILsum_Low group than the TILsum_High one, whereas GCS are higher. The characterization of the second split of Dataset1 (train, validation and test sub-datasets) is provided in the Supplementary Table  S5.
On the overall lesion, CNN4 (average DICE score on overall lesions = 0. 64), showed statistically significantly better results than CNN3 (0. 55). On the EDH lesion, results are unexpected, as CNN3 is better than CNN4, but not significatively, probably due to the small sample size of images containing this lesion. External validation. Comparison of the DICE scores computed on Dataset2 (12 patients) between segmentations resulting from CNN1 to CNN4 and manual segmentations are shown on Fig. 4  and detailed on Supplementary Tables  S8  and S9.
Classification evaluation Prediction accuracy of the 2 best classification models ("4-class Classification model" and "7-class Classification model", see section "Classification model TILsum prediction") on the test sub-dataset of Dataset1 (Internal validation-6 patients) and on the Dataset2 (External validation-12 patients) for 6 segmentations (BLAST-CT, our 3 automatic segmentations, and the 2 manual segmentations) are shown on Table  5. Best accuracies were obtained for 7-class segmentations. CNN1 was only able to correctly classify 50% and 67% respectively internally and externally. The two transfer learning automatic segmentations were able to reach the same accuracy as manual segmentations internally, and with the 7-class segmentation, transfer learning automatic segmentation outperformed the external prediction made with the manual segmentation (10/12 vs 8/12).
Discussion In this study, we quantified the ability of volume, spatial location and type of brain lesions observed on admission CT-scans to predict the therapeutic intensity level of TBI patients within the first week in ICU. Volumes of 7 different lesions on 11 structural zones were able to predict this outcome with a mean AUC of 89% and a standard deviation of 17%. Although the small sample size limits the conclusions of this work, the most influential Table  4. AUC (Mean ± STD) on the outer folds of the models trained for 3 different segmentations (1 automatic and 2 manual) and 7 metrics sets. Results from the nested cross-validation procedure on data from the Train and Validation sub-datasets of Dataset1 (23 patients). Best result in bold. metrics are the volume of lesions located in brain lobes, and especially the volume of subdural hemorrhage.
This result is coherent with the medical experience about the large impact of SDH, often located in the frontal and parietal lobes, on medical care  34  , and with the study pre-published by Rosnati et al. in which 6-months mortality of TBI patients has been predicted from frontal EAH lesions  11. This recent study was conducted on more than 600 patients but only considered the 4-class BLAST-CT segmentation, when our study, conducted on way less patients, predicted a short-term outcome and exploited 7-class segmentations to highlight the influence of SDH. We also highlighted the low predictive power of BLAST-CT to automatically predict our TILsum-based outcome. This lack of prediction could be explained by the poor segmentation obtained using BLAST-CT on our brain CT-scans. Indeed, although BLAST-CT was developed on large multicentric datasets (n = 839) the DICE obtained using BLAST-CT on our patients were lower than those published by Monteiro et al.
14. In order to improve this automatic segmentation, we used 2 transfer learning approaches. First, we showed on the test sub-dataset of Dataset1 that the fine tuning of a deep learning algorithm on a small local dataset (n = 67 for training, consisting in the merge of train and validation sub-datasets) leads to a significantly increased segmentation accuracy. This result can change the classical paradigm in which the objective of segmentation studies is to train an algorithm on a large multicentric dataset to learn and overcome the intersite variability. It might then be possible to easily fine-tune with a few images an already trained algorithm in order to learn the specificities of a study. The second approach, aimed at automatically segmenting 7 lesions from the 4-class segmentation algorithm by transfer learning, showed good results on highly represented lesions but was less accurate on poorly represented ones (such as petechiaes or EDH), a classical behavior in machine learning.
Finally, in order to link our segmentation work to a clinically relevant issue, we validated our results by using the improved segmentations to predict our clinical outcome on the test sub-dataset of Dataset 1. We showed that our improved segmentations predict the TILsum based criteria as the manual ones do. Segmentation and classification were then validated on a new external dataset (Dataset2) leading, as obtained on the internal validation, to better results with transfer learning approaches, and a prediction accuracy of 83% with automatic segmentation (10/12), better than the one obtained with manual segmentation (8/12), which is counterintuivite. This former To our knowledge, we developed the first automatic tool to predict the intensity level of medical care from CT-scans of brain-injured patients, linking image processing to clinical care.
In order to share our CT-scan quantification tool described on Fig. 2  and named CT-TIQUA v1. 4, we encapsulated our best segmentation model, atlas registration, and volumes extraction in a docker container and integrated it on the computing platform VIP  36  , that enables anyone to execute any pipeline on dedicated computing resources from the web-interface: https:// vip. creat is. insa-lyon. fr/. This tool is the first to provide a 7-class segmentation of TBI injuries as well as registered atlas. Its universal utilization might allow to easily try it on another study or task. Of course, this study has some limitations. First, our datasets are small, leading to unstable classification performances and all these results must be validated on larger and multicentric cohorts before any further use in clinical practice.
28×0°. 28 and an angular resolution of 0 5 per pixel  (Gandorfer et al. 2018). PHI scans the Fe I 6173 Å spectral line to produce filtergrams at six different wavelength positions in all four Stokes parameters (i. e. , full polarimetry). Raw data can be compressed and downlinked directly or the inversion can be carried out on board. PHI provides the continuum intensity, line-of-sight (LOS) velocity, LOS magnetic field, and the vector magnetic field. PHI observations can be used to understand the evolution of the source region's magnetic field, and along with EUI, to determine whether interchange reconnection is taking place at the CH or AR boundary. Extrapolations of the photospheric field can also be constructed that provide the coronal magnetic field structure across the boundary of the source region.
Data taken during this time period include both partial frame images with AR 12957 in the field of view and full-Sun images. Data taken with the following filters are available: Thin Al poly, Thin Al mesh, Medium Al, Thin Be, Medium Be, and Thick Be. During RSW2, pointings are used for the partial frame images. First, the boundary of the southern polar CH was the target at various times between 2022 March 17 at 06:00 UT to March 18 at 19:40 UT. The coronal dimming that merges with the CH is particularly visible in the XRT Al poly filter  (Ngampoopun et al. 2023). Second, between 2022 March 18 19:40 UT until the end of March 21 the positive polarity of AR 12967 in the northern hemisphere was the pointing selection. Full-Sun images are also available during the RSW2. Data are collected using the Thin Al poly, Thin Al mesh, and Thin Be filters.
Summary and Discussion The Slow Solar Wind SOOP has been designed in order to understand the origins and formation mechanisms of the slow solar wind. The targets of the Slow Wind SOOP are openclosed magnetic field boundaries such as the edges of CHs and the peripheries of ARs where plasma can escape either directly along open magnetic field or indirectly, i. e. , due to interchange reconnection. Solar Orbiter's extensive suite of instruments and unique orbit provide the first opportunity to trace solar wind plasma measured in situ back to its source region on the Sun using data from a single space-based observatory. The SOOP operated for the first time during Solar Orbiter's first close perihelion passage in 2022 March. High-resolution observations were taken during two remote-sensing windows, which took place between 2022 March 3 06:00 UT and 2022 March 6 at 18:30 UT (RSW1), and 2022 March 17 06:00 UT until 2022 March 22 (RSW2).
Therefore, high-resolution remote-sensing observations were taken of the AR complex including ARs 12957, 12959, and 12961. Post observation, the results from the connectivity tool showed that the connectivity of Solar Orbiter evolved as follows. Solar Orbiter was connected to the positive polarity of AR 12954 in the northern hemisphere prior to RSW1, crossed the HCS to the negative polarity of AR 12955, and traced the equatorial CH boundary, before being connected to AR 12961 and then AR 12957 during the entirety of RSW1. The in situ data from Solar Orbiter support this analysis. First, inward-directed (negative) magnetic field was measured by MAG throughout and after RSW1. Second, a range of solar wind speeds between 670 and 260 km s -1 were measured by SWA/PAS. Initially, the arrival of moderately fast solar wind was observed, followed by a gradual decrease to speeds typical of slow solar wind.
The MAG and SWA/PAS data support these conclusions. The radial magnetic field measured by MAG was positive from midday on 2022 March 17 onward, apart from the arrival of an ICME on 2022 March 20. The solar wind speed measured is relatively slow throughout the window with initial speeds averaging around 400 km s -1 , gradually decreasing to a minimum of 210 km s -1. The decrease in speed could be due to the change in connectivity from the large positive polarity of AR 12965 to the small, decayed positive polarity of AR 12967 and the typical decreasing radial velcity profile in the ICME. The in situ data, connectivity, and upflows in Hinode/EIS data are investigated further in  Baker et al. (2023). Unfortunately, we do not have SWA/HIS data for this period, as HIS reached its thermal limit and was switched off on 2022 March 13.
This will make it more challenging to characterize the solar wind detected in situ with Solar Orbiter. Ngampoopun et al. (2023)  focus on the first target during RSW2, where a coronal dimming associated with a filament eruption merges with the southern polar CH boundary. The SO/EUI, Hinode/EIS, SDO/AIA, and HMI data suggest that there is component reconnection occurring between the boundary of the CH and the coronal dimming. In summary, the Slow Wind SOOP operated for 3. 5 and 5 days, respectively, during the first two remote-sensing windows of Solar Orbiter's close perihelion passage in 2022 March. The SOOP campaign was very successful, given that the targets for the RSWs must be decided in advance and the targets depend upon the predicted magnetic connectivity of the spacecraft during these time periods.
During the RSWs, Solar Orbiter observed both primary targets i. e. , the periphery of an AR and the boundary of a CH, outlined in the Slow Wind SOOP. Complementary data of these targets were also obtained by IRIS and Hinode through coordinated observation campaigns. During and after both remote-sensing windows, slow solar wind was measured by SWA/PAS. The radial magnetic field measured by MAG along with the connectivity tool suggest that the slow wind detected in situ by Solar Orbiter originated from two out of three of our selected targets, namely the boundaries of the negative polarity of AR 12961 during RSW1 and the periphery of the decayed positive polarity of AR 12967. The next steps are to validate these results by combining the analysis of a wider range of both remote-sensing and in situ data taken from Solar Orbiter during these time periods.
Acknowledgments S. L. Y. would like to thank  STFC  via the consolidated grant ( STFC ST/V000497/1 ),  NERC  via the  SWIMMR Aviation Risk Modeling   (SWARM)  project grant ( NE/V002899/1 ), and the  Donostia International Physics Center  for their hospitality. D. M. L. is grateful to the  Science Technology and Facilities Council  for the award of an  Ernest Rutherford Fellowship  ( ST/ R003246/1 ). D. B. is funded under  STFC  consolidated grant No. ST/S000240/1. The work of D. H. B. was performed under contract to the  Naval Research Laboratory  and was funded by the  NASA   Hinode program. L. M. G. would like to thank  NERC  via the  SWIMMR Aviation Modeling Risk (SWARM)  project (grant No. NE/V002899/1 ). V. P. acknowledges support from  NASA  contract  NNG09FA40C  (IRIS) and  NASA  grant No.
The ROB team thanks the  Belgian Federal Science Policy Office (BELSPO)  for the provision of financial support in the framework of the  PRODEX Programme  of the  European Space Agency (ESA)  under contract numbers  4000134088 ,  4000112292 ,  4000136424 , and  4000134474. Solar Orbiter  magnetometer operations are funded by the  UK Space Agency  (grant  ST/T001062/1 ). T. H. is supported by  STFC  grant  ST/ X002098/1. The German contribution to SO/PHI is funded by the  BMWi  through DLR and by  MPG  central funds. The Spanish contribution is funded by  FEDER/AEI/MCIU  ( RTI2018-096886-C5 ), a " Center of Excellence Severo Ochoa " award to  IAA-CSIC  ( SEV-2017-0709 ), and a  Ramón y Cajal fellowship  awarded to DOS. The French contribution is funded by  CNES. J. C. T. I. and D. O. S. are supported by the  Spanish Ministry of Economy and Competitiveness  through projects  ESP-2016-77548-C5-1-R , and by the  Spanish Science Ministry "Centro de Excelencia Severo Ochoa " Program  under grant  SEV-2017-0709  and project  RTI2018-096886-B-C51.
D. O. S. also acknowledges financial support from a  Ramón y Cajal fellowship. The development of the SPICE instrument was funded by  ESA  and  ESA  member states ( France ,  Germany ,  Norway ,  Switzerland ,  United Kingdom). The SPICE hardware consortium was led by  Science and Technology Facilities Council (STFC) RAL Space  and included  Institut d'Astrophysique Spatiale (IAS) ,  Max-Planck-Institut für Sonnensystemforschung (MPS) ,  Physikalisch-Meteorologisches Observatorium Davos  and  World Radiation Center (PMOD/ WRC) ,  Institute of Theoretical Astrophysics (University of Oslo) ,  NASA Goddard Space Flight Center (GSFC) , and  Southwest Research Institute (SwRI). The in-flight commissioning of SPICE was led by the instrument team at  UKRI/ STFC RAL Space. A. F. 's and A. G. 's research is funded by  UKRI STFC.
SO/SWA data are derived from scientific sensors that have been designed, created, and operated under funding provided via numerous contracts from the  UK Space Agency (UKSA) , the  UK Science and Technology Facilities Council (STFC) , the Agenzia Spaziale Italiana (ASI), the Centre National d'Etudes Spatiales (CNES, France), the Centre National de la Recherche Scientifique (CNRS, France), the Czech contribution to the ESA PRODEX program, and NASA. Solar Orbiter SWA work at UCL/MSSL was funded under STFC grants ST/T001356/1, ST/S000240/1, ST/X002152/1 and ST/W001004/1. SDO data are courtesy of NASA/SDO and the AIA, EVE, and HMI science teams. IRIS is a NASA small explorer mission developed and operated by LMSAL with mission operations executed at NASA Ames Research Center and major contributions to downlink communications funded by ESA and the Norwegian Space Centre.
Data Availability All data published in this article are publicly available online. Solar Orbiter data are available via the SOAR archive (https:// soar. esac. esa. int/soar/). Data from the Solar Dynamics Observatory can be downloaded from JSOC (http://jsoc. stanford. edu/). Finally, data taken by Hinode and IRIS can be retrieved from the OSLO database (http://sdc. uio. no/sdc/).
A B S T R A C T Domain Adaptation (DA) has recently raised strong interests in the medical imaging community. While a large variety of DA techniques has been proposed for image segmentation, most of these techniques have been validated either on private datasets or on small publicly available datasets. Moreover, these datasets mostly addressed singleclass problems. To tackle these limitations, the Cross-Modality Domain Adaptation (crossMoDA) challenge was organised in conjunction with the 24th International Conference on Medical Image Computing and Computer Assisted Intervention  (MICCAI 2021). Cross-MoDA is the first large and multi-class benchmark for unsupervised cross-modality Domain Adaptation. The goal of the challenge is to segment two key brain structures involved in the follow-up and treatment planning of vestibular schwannoma (VS): the VS and the cochleas.
Currently, the diagnosis and surveillance in patients with VS are commonly performed using contrast-enhanced T1 (ceT 1 ) MR imaging. However, there is growing interest in using non-contrast imaging sequences such as high-resolution T2 (hrT 2 ) imaging. For this reason, we established an unsupervised cross-modality segmentation benchmark. The training dataset provides annotated ceT 1 scans (N=105) and unpaired non-annotated hrT 2 scans (N=105). The aim was to automatically perform unilateral VS and bilateral cochlea segmentation on hrT 2 scans as provided in the.
Introduction Machine learning (ML) has recently reached outstanding performance in medical image analysis. These techniques typically assume that the training dataset (source domain) and test dataset (target domain) are drawn from the same data distribution. However, this assumption does not always stand in clinical practice. For example, the data may have been acquired at different medical centres, with different scanners, and under different image acquisition protocols. Recent studies have shown that ML algorithms, including deep learning ones, are particularly sensitive to data changes and experience performance drops due to domain shifts  (van Opbroek et al. , 2015; Donahue et al. , 2014). This domain shift problem strongly reduces the applicability of ML approaches to real world clinical settings.
To increase the robustness of ML techniques, a naive approach aims at training models on large-scale datasets that cover large data variability. Therefore, efforts have been made in the computer vision community to collect and annotate data. For example, the Open Images dataset  (Kuznetsova et al. , 2020)  contains 9 million varied images with rich annotations. While natural images can be easily collected from the Internet, access to medical data is often restricted to preserve medical privacy. Moreover, annotating medical images is time-consuming and expensive as it requires the expertise of physicians, radiologists, and surgeons. For these reasons, it is unlikely that large, annotated and open databases will become available for most medical problems. To address the lack of large amounts of labelled medical data, domain adaptation (DA) has raised strong interests in the medical imaging community.
DA is a subcategory of transfer learning that aims at bridging the domain distribution discrepancy between the source domain and the target domain. While the source and target data are assumed to be available at training time, target label availability is either limited (supervised and semi-supervised DA), incomplete (weakly-supervised DA) or missing (unsupervised DA). A complete review of DA for medical image analysis can be found in  Guan and Liu (2021). Unsupervised DA (UDA) has especially raised attention as it doesn't require any additional annotations. However, existing UDA techniques have been either tested on private, small or single class datasets. Consequently, there is a need for a public benchmark on a large and multi-class dataset. To benchmark new and existing unsupervised DA techniques for medical image segmentation, we organised the crossMoDA challenge in conjunction with the 24th International Conference on Medical Image Computing and Computer Assisted Intervention  (MICCAI 2021).
, 2018). For this reason, we proposed a cross-modality benchmark (from ceT 1 to hrT 2 ) that aims to automatically perform VS and cochleas segmentation on hrT 2 scans. This paper summarises the 2021 challenge and is structured as follows. First, a review of existing datasets used to assess existing domain adaptation techniques for image segmentation is proposed in Section 2. Then, the design of the crossMoDA challenge is given in Section 3. Section 4 presents the evaluation strategy of the challenge (metrics and ranking scheme). Participating methods are then described and compared in Section 5. Finally, Section 6 presents the results obtained by the participating team and Section 7 provides a discussion and concludes the paper.
Related work We performed a literature review to survey the benchmark datasets used to assess DA techniques for unsupervised medical image segmentation. On the methodological side, as detailed afterwards, a range of methods were used by the participating teams, illustrating a wide breadth of different DA approaches. Nonetheless, a thorough review of DA methodologies is out of the scope of this paper. We refer the interested reader to  Guan and Liu (2021)  for a recent review of these. Many domain adaptation techniques for medical image segmentation have been validated on private datasets, for example  Kamnitsas et al. (2017) ;  Yang et al. (2019). Given that these datasets used for the experiments are not publicly available, it is not possible to compare new methods with these techniques. Other authors have used public datasets to validate their methods.