dataesr/scientific-paragraphs-categorization huggingface dataset:
Interestingly, these datasets often come from previous medical segmentation challenges that weren't originally proposed for domain adaptation. For this reason, unsupervised problems are generated by artificially removing annotations on subsets of these challenge datasets. We present these open datasets and highlight their limitations for evaluating unsupervised domain adaptation: Another limitation of this dataset is that it only assesses single-class UDA solutions. Finally, the domain shift is limited as the source and target domains correspond to the same image modalities acquired with 3T MRI scanners. • SCGM: The Spinal Cord Gray Matter Challenge (SCGM) dataset is a collection of cervical MRI from four institutions  (Prados et al. , 2017). Each site provided unimodal images from 20 healthy subjects along with manual segmentation masks.
Various unsupervised domain adaptation techniques have been tested on this dataset  (Perone et al. , 2019; Liu et al. , 2021b; Shanis et al. , 2019). Again, the main limitation of this dataset is the small size of the test sets (N ≤ 20 scans). Moreover, the problem is singleclass and the domain shift is limited (intra-modality UDA). • IVDM3Seg: The Automatic Intervertebral Disc Localization and Segmentation from 3D Multi-modality MR Images (IVDM3Seg) is a collection of 16 manually annotated 3D multi-modal MR scans of the lower spine. Domain adaptation techniques were validated on this dataset  (Bateson et al. , 2019 (Bateson et al. , , 2020)). The test set is extremely small (N = 4) and it is a single-class segmentation task. • MM-WHS: The Multi-Modality Whole Heart Segmentation (MM-WHS) Challenge 2017 dataset  (Zhuang et al.
, 2019)  is a collection of MRI and CT volumes for cardiac segmentation. Specifically, the training data consist of 20 MRI and 20 unpaired CT volumes with ground truth masks. This dataset has been used to benchmark most multi-classes cross-modality domain adaptation techniques  (Dou et al. , 2018; Ouyang et al. , 2019; Cui et al. , 2021; Zou et al. , 2020). While the task is challenging, the very limited size of the test set (N = 4) strongly reduced the statistical power of comparisons. • CHAOS: The Combined (CT-MR) Healthy Abdominal Organ Segmentation (CHAOS) dataset  (Kavur et al. , 2021)  corresponds to 20 MR volumes and 30 unpaired CT volumes. Cross-modality domain adaptation techniques have been tested on this dataset  (Chen et al. , 2020a; Jiang and Veeraraghavan, 2020). 4 and 6 scans are respectively used as test sets for the MR and CT domains.
Consequently, the test sets are particularly small. • BraTS: The Brain Tumor Segmentation (BraTS) benchmark  (Menze et al. , 2014; Bakas et al. , 2017 Bakas et al. , , 2019 ) is a popular dataset for the segmentation of brain tumour subregions. While images were collected from a large number of medical institutions with different imaging parameters, the origin of the imaging data is not specified for each case. Instead, unsupervised pathology domain adaptation (high to low grades) has been tested on this dataset  (Shanis et al. , 2019) , which is a different problem than ours. Alternatively, BraTS has been used for cross-modality domain adaptation  (Zou et al. , 2020). However, the problem is artificially generated by removing image modalities and has a limited clinical relevance. In conclusion, test sets used to assess segmentation methods for unsupervised domain adaptation are either private, small or single-class.
Data overview The dataset for the crossMoDA challenge is an extension of the publicly available Vestibular-Schwannoma-SEG collection released on The Cancer Imaging Archive (TCIA)  (Shapey et al. , 2021b; Clark et al. , 2013). To ensure that no data in the test set was accessible to the participants, no publicly available scan was included in the test set. The open Vestibular-Schwannoma-SEG dataset was used for training and validation while an extension was kept private and used as the test set. The complete crossMoDA dataset (training, validation and testing) contained a set of MR images collected on 379 consecutive patients (Male:Female 166:214; median age: 56 yr, range: 24 yr to 84 yr) with a single sporadic VS treated with Gamma Knife stereotactic radiosurgery (GK SRS) between 2012 and 2021 at a single institution.
For each patient, contrast-enhanced T1-weighted (ceT 1 ) and high-resolution T2-weighted (hrT 2 ) scans were acquired in a single MRI session prior to and typically on the day of the radiosurgery. 75 patients had previously undergone surgery. Data were obtained from the Queen Square Radiosurgery Centre (Gamma Knife). All contributions to this study were based on approval by the NHS Health Research Authority and Research Ethics Committee (18/LO/0532) and were conducted in accordance with the 1964 Declaration of Helsinki. The scans acquired between October 2012 and December 2017 correspond to the publicly available Vestibular-Schwannoma-SEG dataset on TCIA (242 patients). The Vestibular-Schwannoma-SEG dataset was randomly split into three sets: the source training set (105 annotated ceT 1 scans), the target training set (105 non-annotated hrT 2 scans) and the target validation set (32 non-annotated hrT 2 scans).
Image acquisition All images were obtained on a 32-channel Siemens Avanto 1. 5T scanner using a Siemens single-channel head coil. Contrast-enhanced T1-weighted imaging was performed with an MP-RAGE sequence (in-plane resolution=0. 47×0. 47mm, matrix size=512×512, TR=1900ms, TE=2. 97ms) and slice thickness of 1. 0-1. 5mm. High-resolution T2-weighted imaging was performed with either a Constructive Interference Steady State (CISS) sequence (in-plane resolution=0. 47×0. 47mm, matrix size=448×448, TR=9. 4ms, TE=4. 23ms) or a Turbo Spin Echo (TSE) sequence (in-plane resolution=0. 55×0. 55mm, matrix size=384×384, TR=750ms, TE=121ms) and slice thickness of 1. 0-1. 5mm. The details of the dataset are given in Table 1 and sample cases from the source and target sets are illustrated in Fig  1.
Annotation protocol All imaging datasets were manually segmented following the same annotation protocol. The tumour volume (VS) was manually segmented by the treating neurosurgeon and physicist using both the ceT 1 and hrT 2 images. All VS segmentations were performed using the Leksell GammaPlan software that employs an in-plane semiautomated segmentation method. Using this software, delineation was performed on sequential 2D axial slices to produce 3D models for each structure. The adjacent cochlea (hearing organ) is the main organ at risk during VS radiosurgery. In the crossMoDA dataset, patients have a single sporadic VS. Consequently, only one cochlea per patient -the closest one to the tumour -was initially segmented by the treating neurosurgeon and physicist. Preliminary results using a fully-supervised approach  (Isensee et al.
, 2021)  showed that considering the remaining cochlea as part of the background leads to poor performance for cochlea segmentation. Given that tackling this challenging issue is beyond the scope of the challenge, both cochleas were manually segmented by radiology fellows with over 3 years of clinical experience in general radiology using the ITK-SNAP software  (Yushkevich et al. , 2019). hrT 2 images were used as reference for cochlea segmentation. The basal turn with osseous spiral lamina was included in the annotation of every cochlea to keep manual labels consistent. In addition, modiolus, a small low-intensity area (on hrT 2 ) within the centre of the cochlea, was included in the segmentation as well.
Data curation The data was fully de-identified by removing all health information identifiers and defaced  (Milchenko and Marcus, 2013). Details can be found in  Shapey et al. (2021b). Since the data was acquired consistently (similar voxel spacing, same scanner), no further image pre-processing was employed. Planar contour lines (DICOM RT-objects) of the VS were converted into label maps using SlicerRT  (Pinter et al. , 2012). Images and segmentation masks were distributed as compressed NIfTI files (. nii. gz). The training and validation data was made available on zenodo 1. As we expect this dataset to be used for other purposes in addition to cross-modality domain adaptation, the data was released under a permissive copyrightlicense (CC-BY-4. 0), allowing for data to be shared, distributed and improved upon.
Challenge setup The validation phase was hosted on Grand Challenge 2  , a well-established challenge platform, allowing for an automated validation leaderboard management. Participant submissions are automatically evaluated using the evalutils 3  and MedPy 4  Python packages. To mitigate the risk that participants select their model hyper-parameters in a supervised manner, i. e. by computing the prediction accuracy, only one submission per day was allowed on the validation leaderboard. The validation phase was held between the 5th of May 2021 and the 15th of August 2021. Following the best practice guidelines for challenge organisation  (Maier-Hein et al. , 2020) , the test set remained private to reduce the risk of cheating. Participants had to containerise their methods with Docker following guidelines 5  and submit their Docker container for evaluation on the test set.
Participating methods A total of 341 teams registered to the challenge, allowing them to download the data. 55 teams from 16 different countries submitted predictions to the validation leaderboard. Among them, 16 teams from 9 different countries submitted their containerised algorithm for the evaluation phase. In this section, we provide a summary of the methods used by these 16 teams. Each method is assigned a unique colour code used in the tables and figures. Brief comparisons of the proposed techniques in terms of methodology and implementation details (training strategy, pre-, post-processing, data augmentation) are presented in Table  2. To bridge the domain gap between the source and target images, proposed techniques can be categorised into three groups that use: 1. Image-to-image translation approaches such as CycleGAN and its extensions to transform ceT 1 scans into pseudo-hrT 2 scans ( ) or hrT 2 scans into pseudo-ceT 1 scans ( ).
Moreover, 3D connected components analysis was utilised to ensure that only two cochleas and one tumour are remained. PremiLab (5th place, Yao et al. ). The proposed framework consists of a content-style disentangled GAN for style transfer and a modified 3D version ResU-Net along with two types of attention modules for segmentation (DAR-U-Net). Specifically, content is extracted from both modalities using the same encoder, while style is extracted using modality-specific encoders. A discriminative approach is adopted to align the content representations of the source and target domain. Once the GAN is trained, ceT 1 -to-hrT 2 images are generated with diverse styles to later train the segmentation network, which can imitate the diversity of hrT 2 domain. Different from the original 2D ResU-Net  (Diakogiannis et al.
). This framework proposed by  Li et al. (2021a)  consists of two components: Synthesis and segmentation. For the synthesis component, the Cycle-GAN pipeline is used for unpaired image-to-image translation between ceT 1 and hrT 2 MRIs. For the segmentation component, the generated hrT 2 MRIs are fed into two 2. 5D U-Net models  (Wang et al. , 2019)  and two 3D U-Net models  Li et al. (2021b). The 2. 5D models contain both 2D and 3D convolutions, as well as an attention module. Residual blocks and deep supervision are used in the 3D CNN models. Furthermore, various data augmentation schemes are applied during training to cope with MRIs from different scanners, including spatial, image appearance, and image quality augmentations. Different parameter settings are used for the two 2. 5D CNN models.
The difference between the two 3D CNN models is that only one of them had an attention module. Finally, the models are ensembled to obtain the final segmentation result. DBMI pitt (8th place, Zu et al. ). The proposed framework use 3D image-to-image translation to generate pseudodata used to train a segmentation network. To perform imageto-image translation, authors extended the 2D CUT model  (Park et al. , 2020)  to 3D translation. The translation model consists of a generator G, a discriminator D and a feature extractor F. The generator G is built upon the 2. 5D attention U-Net proposed in  Wang et al. (2019)  where two down-sampling layers are removed. For the discriminator D, PatchGAN discriminator is selected  Isola et al. (2017). The model structure of D is the 6 layers of Resnet, and when feeding images to the D, the images are divided into 16 equal-size patches, which is faster for model feed-forward without sacrificing any performance.
The feature extractor F is a simple multi-layer full-connected network as  Park et al. (2020). The segmentation network is a 2. 5D  (Wang et al. , 2019). To perform image segmentation, the attention 2. 5 U-Net  (Wang et al. , 2019)  is used as backbone architecture. A detection module is built upon it. Finally, post-processing is performed using standard morphological operations (hole filling) and the largest component selection for VS. Hi-Lib (9th place, Wu et al. ). The proposed approach is based on the 2. 5D attention U-Net  (Wang et al. , 2019) , where a GAN-based data augmentation strategy is employed to eliminate the instability of unpaired image-to-image translation. Specifically, a source-domain image is sent to the trained Cy-cleGAN  (Zhu et al. , 2017)  and CUT  (Park et al. , 2020)  to obtain two different pseudo-target images, and then they are converted back to source domain-like images, so that each source domain image shares the same label with its augmented versions.
, 2021)  framework. Firstly, the annotated source training set is used to train a 3D nnU-net framework. Secondly, another 3D nnU-net framework is trained with domain adaptation. Specifically, DA is performed by minimising the weighted crossentropy on the source domain and the weighted entropy on the target domain. At inference stage, VS segmentation is performed using the adapted nnU-Net framework. In contrast, the two models are ensembled for the cochleas task. skjp (15th place, Kondo). The proposed approach uses Gradient Reversal Layer (GRL)  (Ganin et al. , 2016)  to perform domain adaption. First, a 3D version of ENet  (Paszke et al. , 2016)  is trained with the annotated source domain dataset. Then, domain adaptation is performed. Specifically, feature maps extracted in the encoder part of the network are fed to GRL.
Limitations and future directions for the challenge The lack of robustness to unseen situations is a key problem for deep learning algorithms in clinical practice. We created this challenge to benchmark new and existing domain adaptation techniques on a large and multi-class dataset. In this challenge, the domain gap between the source and target images is large, as it corresponds to different imaging modalities. However, the lack of robustness can also occur when the same image modalities are acquired with different settings (e. g. , hospital, scanner). This problem is not addressed in this challenge. Indeed, images within a domain (target or source) have been acquired at a unique medical centre with the same scanner. Moreover, 96% of the test set has been acquired with the same sequence parameters.
WMH: The MICCAI White Matter Hyperintensities (WMH) Challenge dataset (Kuijf et al. , 2019) consists of brain MR images with manual annotations of WMH from three different institutions. Each institution provided 20 multi-modal images for the training set. Domain adaptation techniques have been validated on each set of scans acquired at the same institution (Orbes-Arteaga et al. , 2019; Palladino et al. , 2020; Sundaresan et al. , 2021). Each institution set (N = 20) is not only used to assess the methods but also to perform domain adaptation during training. Consequently, the test sets are extremely small (N ≤ 10 scans), leading to comparisons with low statistical powers.
L. H. and M. H. are supported by the German Research Foundation (DFG) under grant number 320997906 [HE 7364/2-1]. S. J. and S. E. are supported by the Spanish project PID2019-105093GB-I00 and by ICREA under the ICREA Academia programme B. L. and V. K. are supported by the French Government, through the National Research Agency (ANR) 3IA Côte d'Azur [ANR-19-P3IA-0002], IHU Liryc [ANR-10-IAHU-04]. H. Li and I. O are supported by the National Institute of Health (NIH) [R01-NS094456]. L. Z. and H. D. are supported by the Natural Science Foundation of China (NSFC) under Grants 81801778, 12090022, 11831002. Y. X. and K. B. are supported by NIH Award Number 1R01HL141813-01, NSF 1839332 Tri-pod+X, SAP SE, and Pennsylvania's Department of Health and are grateful for the computational resources provided by Pittsburgh Super Computing grant number TG-ASC170024.
Acknowledgements We would like to thank all the other team members that helped during the challenge:  Sewon Kim ,  Yohan Jun ,  Taejoon Eo ,  Dosik Hwang  (Samoyed);  Fei Yu ,  Jie Zhao ,  Bin Dong  ( PKU BIALAB );  Can Cui ,  Dingjie Su ,  Andrew Mcneil  (MIP);  Xi Yang ,  Kaizhu Huang ,  Jie Sun  (PremiLab);  Yingyu Yang ,  Aurelien Maillot ,  Marta Nunez-Garcia ,  Maxime Sermesant  ( Epione-Liryc );  Dewei Hu ,  Qibang Zhu ,  Kathleen E Larson ,  Huahong Zhang  (MedICL);  Mingming Gong  (DBMI pitt);  Ran Gu ,  Shuwei Zhai ,  Wenhui Lei  ( Hi-Lib );  Richard Osuala ,  Carlos Martın-Isla ,  Victor M. Campello ,  Carla Sendra-Balcells ,  Karim Lekadir  ( smriti161096 );  Mikhail Belyaev  (IRA). This work was supported by the  Engineering and Physical Sciences Research Council (EPSRC)  [ NS/A000049/1 ,  NS/A000050/1 ],  MRC  ( MC/PC/180520 ) and  Wellcome Trust  [ 203145Z/16/Z ,  203148/Z/16/Z ,  WT106882 ].
TV is supported by a  Medtronic / Royal Academy of Engineering  Research Chair [RCSRF1819\7\34]. Z. S and K. Y. are supported by the  National Natural Science Foundation of China  [No. 61876155 ], the  Jiangsu Science and Technology Programme (Natural Science Foundation of Jiangsu Province)  [No. BE2020006-4 ] and the  Key Program Special Fund in Xi'an Jiaotong-Liverpool University (XJTLU)  [ KSF-E-37 ]. C. K. and M. H. are supported by the  Federal Ministry of Education and Research  [No. 031L0202B ]. H. S. and H. G. K. are supported by  Basic Science Research Program  through the  National Research Foundation of Korea (NRF)  funded by the  Ministry of Science and ICT  [ 2019R1A2B5B01070488 ,  2021R1A4A1031437 ],  Brain Research Program  through the  NRF  funded by the  Ministry of Science, ICT & Future Planning  [ 2018M3C7A1024734 ],  Y-BASE   R&E Institute a Brain Korea 21 ,  Yonsei University , and the  Artificial Intelligence Graduate School Program ,  Yonsei University  [No.
• WMH: The MICCAI White Matter Hyperintensities (WMH) Challenge dataset (Kuijf et al. , 2019) consists of brain MR images with manual annotations of WMH from three different institutions. Each institution provided 20 multi-modal images for the training set. Domain adaptation techniques have been validated on each set of scans acquired at the same institution (Orbes-Arteaga et al. , 2019; Palladino et al. , 2020; Sundaresan et al. , 2021). Each institution set (N = 20) is not only used to assess the methods but also to perform domain adaptation during training. Consequently, the test sets are extremely small (N ≤ 10 scans), leading to comparisons with low statistical powers.
Introduction Human society is ushering into an intelligent society from an information society, in which computing has become a key element in formulating and promoting the development of society. In the new era of digital civilization with the internet of all things, traditional computing on data is far from being able to meet the growing endeavor for a higher level of intelligence by humans. The growing interest in intelligent computing, coupled with the development of computing science, the intelligent perception of the physical world, and the understanding of the cognitive mechanism of human consciousness, has collectively elevated the intelligence level of computing and accelerated the discovery and creation of knowledge. Recent years have witnessed the rapid development of computing and information technology, from which artificial intelligence (AI) has been established as the frontier of human exploration of machine intelligence thanks to the unprecedented popularity and success of deep learning.
Based on this, a series of breakthrough research results have been produced, including the convolutional neural network (CNN) proposed by Yann LeCun and contributions by Yoshua Bengio in the area of causal inference in deep learning  [1, 2]. Geoffrey Hinton, one of the pioneers of AI, proposed the deep belief network model and the backward propagation optimization algorithm in 2006  [3]. Jürgen Schmidhuber, another significant AI researcher, proposed the most widely-used recurrent neural network (RNN), long short-term memory (LSTM)  [4]. It has been successfully applied in many fields to process entire sequences of data, such as speech, video, and time-series data. In March 2016, AlphaGo, an AI Go program launched by DeepMind, battled with Lee Sedol, the world's top human Go master, and has attracted unprecedented worldwide attention.
This epochmaking man-machine battle ended with a crushing victory of AI and has become a catalyst to push the wave of AI to a whole new level. Another significant promoter of AI is the emergence of large pre-training models that have started to be widely used in natural language and image processing to deal with a wide variety of applications with the assistance of transfer learning. For example, GPT-3 has demonstrated that a big model, with a high level of structural complexity and a huge number of parameters, can improve the performance of deep learning. Inspired by GPT-3, a host of large-scale deep learning models have emerged  [5] [6] [7]. Computational capacity is one of the important elements underpinning intelligent computing. Given the astronomical data sources, heterogeneous hardware configurations, and changing computing requirements in our information society, intelligent computing mainly meets the computational capacity requirements of intelligent tasks through vertical and horizontal architectures.
Data Intelligence Improving the universality of computing is critical for intelligent computing. Problems in realworld scenarios, such as analog, graph, etc. , need various computations. Another critical point of intelligent computing is how to improve the intellectual level of computing. Empirically, we all need to learn from intelligent creatures in nature with no exception for computation, such as the three classical intelligence methods: artificial neural network, fuzzy system, and evolutionary computing. The theory of intelligent computing includes but is not limited to the above types of computing to achieve a high level of ubiquity and intelligence.
With the development of database technology, graph databases have risen strongly with their comprehensive application scenarios and flexible model expression. They have become one of the four core members of the emerging NoSQL data family. The expansion of the graph model is reflected in tasks such as storing and managing graph data. On the other hand, the connotation expansion of the model brings an increment in algorithm complexity for the algorithm evolution of various graph processing. Furthermore, it makes the results of computing problems more suitable for application in natural scenes. In recent years, with the development of deep learning technology, graph data has been used as the input of the neural network models, and various types of graph neural network models and calculation methods have been derived.