dataesr/scientific-paragraphs-categorization huggingface dataset:
J. R-M. , H. D. , M. S. , and W. V. ;  Marie Skłodowska-Curie Innovative Training Network (MSCA-ITN ) RNAct supported by  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  813239. (https://ec. europa. eu/info/research-and-innovation/funding/funding-opportunities/fundingprogrammes-and-open-calls/horizon-2020_en). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
The three main areas of development are: (i) network architectures to extract relevant features from WSI for classification or segmentation purposes; (ii) generative adversarial networks (GANs) to address some of the issues present in the preparation and acquisition of WSIs; and, (iii) unsupervised learning to create labeling tools for precise annotations. Regarding data, many top-tier conference competitions have been organized and released annotated datasets to the community; however, very few of them contain brain tissue samples. Those which do, are from brain tumor regions obtained during a biopsy, making it harder to study other brain disorder categories which frequently require post-mortem data. In  [1] , the authors also mention seven key challenges in diagnostic AI in pathology, listed as follows: • Access to large well-annotated data sets.
Most articles on brain disorders use private datasets due to hospital privacy constraints. • Context switching between workflows refers to a seamless integration of AI into the pathology workflow. • Algorithms are slow to run as image sizes are in gigapixels' order and require considerable computational memory. • Algorithms require configuration, and fully automated approaches with high accuracy are difficult to develop. • Properly defined protocols are needed for training and evaluation. • Algorithms are not properly validated due to a lack of open datasets. However, research in data augmentation might help in this regard. • Introduction of Intelligence Augmentation to describe computational pathology improvements in diagnostic pathology. AI algorithms work best on well-defined domains rather than in the context of multiple clinico-pathological manifestations among a broad range of diseases; however, they provide relevant quantitative insights needed for standardization and diagnosis.
Depending on the application, lossless or lossy compression algorithms can be applied. Lossless compression typically yields a 3X-5X reduction in size; meanwhile, lossy compression techniques such as JPEG and JPEG2000 can achieve from 15X-20X up to 30X-50X reduction respectively  [23]. Due to no standardization of WSI file formats, scan manufacturers may also develop their proprietary compression algorithms based on JPEG and JPEG2000 standards. Commercial WSI formats have a mean default compression value ranging from 13X to 27X. Although the size of WSI files is considerably reduced, efficient data storage was not the main issue when designing WSI formats for more than 10 years. In  [25] , Helin et al. addressed this issue and proposed an optimization to the JPEG2000 format, which yields up to 176X compression.
Methods in Brain Computational Pathology This section is dedicated to different machine learning and deep learning methodologies to analyze brain tissue samples. We describe the technology by focusing on how this is applied (i. e. , at the WSI or the patch Machine Learning for Brain Disorders, Chapter 18 level), the medical task associated with it, the dataset used, the core structure/architecture of the algorithms, and the significant results. We begin by describing the general challenges in WSI analysis. Then we move on to deep learning methods concerning only WSI analysis, and we finalize with machine learning and deep learning applications for brain disorders focusing on the disease rather than the processing of the WSI. In addition, as in the primary biomedical areas, data annotation is a vital issue in computational pathology, generating accurate and robust results.
This last approach needs, however, further validation as results are not conclusive and have not been accepted by the scientific community yet. Relevant features for disease analysis, diagnosis, or patient stratification can be extracted from individual patches by looking into cell characteristics or morphology; however, higher structural information, such as the shape or extent of a tumor, can only be captured in more extensive regions. Some approaches to processing multiple magnification levels of a WSI are reported in  [47, 48, 49, 50, 51]. They involve leveraging the pyramidal structure of WSI to access features from different resolutions and model spatial correlations between patches. All the studies cited so far have no specific domain of application. Most of them were trained and tested using synthetic or public datasets containing tissue pathologies from different body areas.
DL algorithms for Brain WSI analysis In recent times, deep-learning-based methods have shown promising results in digital pathology  [52]. Unfortunately, only a few public datasets contain WSI of brain tissue, and most of them only contain brain tumors. In addition, most of them are annotated at the slide level, making the semantic segmentation of structures more challenging. Independently of the task (i. e. , detection/classification or segmentation) and the application in brain disorders, we will explore the main ideas behind the methodologies proposed in the literature. For the analysis of benign or cancerous pathologies in brain tissue, tumor cell nuclei are of significant interest. The usual framework for analyzing such pathologies was reported in  [53]  and used the WSI of diffuse glioma.
These patches are then fed to a CNN to output probability maps and generate initial contours for the nuclei using a region merging algorithm. The correct nucleus segmentation is obtained by alternating dictionary-based shape deformation and inference. This method outperformed classical image processing algorithms with promising results (mean Dice similarity coefficient of 0. 85 and detection F 1 score of 0. 77 computed using gold-standard regions within 15 pixels for every nucleus center) using CNN-based features over classical ones. Following a similar approach, Xu Y. et al. [55]  reported the use of deep convolutional activation features for brain tumor classification and segmentation. The authors used a pre-trained AlexNet CNN  [56]  on the ImageNet dataset to extract patch features from the last hidden layer of the architecture.
The 3 models included an FCNN, U-Net  [62] , and Segnet  [63] , with Segnet achieving the highest accuracy in terms of the intersection-over-union index. In  [64] , an FCNN was used on a dataset of 22 WSIs for semantic segmentation of tangle objects from post-mortem brain WSIs. Their model is able to segment tangles of varying morphologies with high accuracy under diverse staining intensities. An FCNN model is also used in  [65]  to classify morphologies of tau protein aggregates in the gray and white matter regions from 37 WSIs representing multiple degenerative diseases. In  [14] , tau aggregate analysis is processed on a dataset of 6 post-mortem brain WSIs with a combined classification-segmentation framework which achieved an F 1 score of 81. 3% and 75. 8% on detection and segmentation tasks respectively.
In  [16] , neuritic plaques have been processed from 8 human brain WSI from the frontal lobe, stained with AT8 antibody (majorly used in clinics, helping to highlight most of the relevant structures). The impact of the staining (ALZ50  [14]  vs. AT8  [16] ), the normalization method, the slide scanner, the context, and the DL traceability/explainability have been studied, and a comparison with commercial software has been made. A baseline of 0. 72 for the Dice score has been reported for plaque segmentation, reaching 0. 75 using an attention U-Net. Several domains in DL-based histopathological analysis of AD tauopathy remain unexplored. First, even if, as discussed, a first work concerning neuritic plaques has been recently published by our team in  [16] , most of the existing works have used DL for segmentation of tangles rather than plaques, as the latter are harder to identify against the background gray matter due to their diffuse/sparse appearance.
In brain tumor research, an early survival study for brain glioma is presented in  [44]. The approach has been previously described above. In brief, it is a 4-stage methodology based on randomly sampled patches from different patients' slides. They perform dimensionality reduction using PCA and then K-means clustering to group patches according to their phenotype. Then, the patches are sent to a deep convolutional network (DeepConvSurv) to determine which were relevant for the aggregation and final survival score. The deep survival model is trained on a small dataset leveraging the architecture the authors proposed. Also, the method is annotation free, and it can learn information about one patient, regardless of the number or size of the WSIs. However, it has a high computational memory footprint as it needs hundreds of patches from a single patient's WSI.
In  [69] , the authors presented three classification methods to categorize adult diffuse glioma cases into oligodendroglioma and astrocytoma classes using radiographic and histologic image data. Thirty-two cases were gathered from the TCGA project 4  containing a set of MRI data (T1, T1C, FLAIR, and T2 images) and its corresponding WSI, taken from the same patient at the same time point. The methods described were proposed in the context of the Computational Precision Medicine (CPM) satellite event at MICCAI 2018, one of the first combining radiology and histology imaging analyses. The first one develops two independent pipelines giving two probability scores for the prediction of each case. The MRI pipeline preprocesses all images to remove the skull, co-register, and re-sample the data to leverage a fully convolutional neural network (CNN) trained on another MRI dataset (i.
e. , BraTS-2018) to segment tumoral regions. Several radiomic features are computed from such regions, and after reducing its dimensionality with PCA, a logistic regression classifier outputs the first probability score. WSIs also need a preprocessing stage as tissue samples may contain large areas of glass background. After a color space transformation to HSV (hue saturation value), lower and upper thresholds are applied to get a binary mask with the region of interest, which is then refined using mathematical morphology. Color-normalized patches of 224 × 224 pixels are extracted from the region-of-interest (ROI) and filtered to exclude outliers. The remaining patches are used to refine a CNN (i. e. , DenseNet-161) pre-trained on the ImageNet dataset. In the prediction phase, the probability score of the WSI is computed using a voting system of the classes predicted for individual patches.
The scores from both pipelines are finally processed in a confidence-based voting system to determine the final class of each case. This proposal achieved an accuracy score of 0. 9 for classification. The second and third approaches also processed data in two different pipelines. There are slight variations in the WSI preprocessing step in the second method, including Otsu thresholding for glass background removal and histogram equalization for color normalization of patches of 448 × 448 pixels. Furthermore, the authors used a 3D CNN to generate the output predictions for the MRI data and a DenseNet pre-trained architecture for WSI patch classification. The last feature layer from each classification model is finally used as input to an SVM model for a unified prediction. In addition, regularization using dropout is performed in the test phase to avoid overfitting the models.
The accuracy obtained with this methodology was 0. 8. The third approach uses larger patches from WSI and an active learning algorithm proposed in  [70]  to extract regions of interest instead of randomly sampling the tissue samples. Features from the WSI patches are extracted using a VGG16 CNN architecture. The probability score is combined with the output probability of a U-Net + 2D DenseNet architecture used to process the MRI data. The method achieved an accuracy of 0. 75 for unified classification. Although results are promising and provide a valid approach to combining imaging modalities, data quality and quantity are still challenging. The use of pre-trained CNN architectures for transfer learning using a completely different type of imaging modality might impact the performance of the whole pipeline.
As seen in previous sections, WSI presents specific characteristics depending on the preparation and acquisition procedures not represented in the ImageNet dataset. An extension to the previous study is presented in  [71]. The authors proposed a two-stage model to classify gliomas into three subtypes. WSIs were divided into tiles and filtered to exclude patches containing glass backgrounds. An ensemble learning framework based on three CNN architectures (EfficientNet-B2, EfficientNet-B3, and SEResNeXt101) is used to extract features which are then combined with meta-data (i. e. , age of the patient) to predict the class of glioma. MRI data is preprocessed in the same way as described before and input to a 3D CNN network with a 3D ResNet architecture as a backbone. The release of new challenges and datasets, such as the Computational Precision Medicine: Radiology-Pathology Challenge on brain tumor classification (CPM-RadPath), has also allowed studies using weakly supervised deep learning methods for glioma subtype classification.
The network's predictions (in new WSIs) are then reviewed by an expert who can refine the predictions by modifying the segmentation outline or validating new structures found in the WSI. Additionally, an attention-based architecture is used to create a visual explanation and refine the hyperparameters of the initial architecture in charge of the prediction proposal. We tested the attention-based architecture with a dataset of 8 WSI divided into patches following an ROI-guided sampling. Results show qualitatively in Figure  10  that through this visual explanation, the expert in the loop could define the border of the neuritic plaque (object of interest) more accurately so the network can update its weights accordingly. Additionally, quantitative results (dice score of approximately 0. 7) show great promise for this attention U-Net architecture.
The activation maps are used for final pixel-level segmentation. The method outperforms other weakly supervised methods applied to these types of images and generalizes to other datasets easily. A medical use-case of AI versus human interpretation of histopathology data using a liver biopsy dataset is described in  [78] , which also stresses the need to develop methods for causability or measurement of the quality of AI explanations. In  [67] , AI models like deep auto-encoders were used to generate features from whole-mount prostate cancer pathology images that pathologists could understand. This work showed that a combination of human and AI-generated features produced higher accuracy in predicting prostate cancer recurrence. Finally, in  [16] , the authors show that, besides providing valuable visual explanation insights, the use of attention U-Net is even helping to increase the results of neuritic plaques segmentation by pulling up the Dice score to 0.
75 from 0. 72 (with the original U-Net). Based on the fusion of MRI and histopathology imaging datasets, a deep learning 3D U-Net model with explanations is used in  [79]  for prostate tumor segmentation. Grad-CAM  [80]  heat maps were estimated for the last convolutional layer of the U-Net for interpreting the recognition and localization capability of the U-Net. In  [81] , a framework named NeuroXAI is proposed to render explainability to existing deep learning models in brain imaging research without any architecture modification or reduction in performance. This framework implements seven state-of-the-art explanation methods -including Vanilla gradient  [82] , Guided back-propagation, Integrated gradients  [83] , SmoothGrad  [84]  and Grad-CAM. These methods can be used to generate visual explainability maps for deep learning models like 2D and 3D CNN, VGG  [85] , and Resnet-50  [86]  (for classification) and 2D/3D U-Net (for segmentation).
In  [87] , the high-level features of three deep convolutional neural networks (DenseNet-121, GoogLeNet, MobileNet) are analysed using the Grad-CAM explainability technique. The Grad-CAM outputs helped distinguish these three models' brain tumor lesion localization capabilities. An explainability framework using SHAP  [88]  and LIME  [89]  to predict patient age using the morphological features from a brain MRI dataset is developed in  [90]. The SHAP explainability model is robust for this imaging modality to explain morphological feature contributions in predicting age, which would ultimately help develop personalized agerelated bio-markers from MRI. Attempts to explain the functional organization of deep segmentation models like DenseUnet, ResUnet, and SimUnet and understand how these networks achieve high accuracy brain tumor segmentation are presented in  [91].
The biomedical tissue is excited with a dual-wavelength fiber laser set up at the so-called pump and Stokes frequencies to enhance the weak vibrational effect  [94]. This technique is known as coherent anti-Stokes Raman scattering (CARS) or stimulated Raman scattering histology (SRH). Sarri et al. [95]  proposed the first one-to-one comparison between SRH and H&E as the latter technique remains the standard in histopathology analyses. The evaluation was conducted using the same cryogenic tissue sample. SRH data was first collected as it did not need staining. SRH and SHG (second harmonic generation, another label-free nonlinear optical technique) were combined to generate a virtual H&E slide for comparison. The results evidenced the almost perfect similarity between SRH and standard H&E slides.
Acknowledgments The human samples used in the preparation of the images throughout the chapter were obtained from the Neuro-CEB brain bank 5 (BRIF Number  0033-00011 ), partly funded by the patients' associations  ARSEP ,  ARSLA , " Connaître les Syndromes Cérébelleux" ,  France-DFT ,  France Parkinson  and by  Vaincre Alzheimer Fondation , to which we express our gratitude. We are also grateful to the patients and their families. Knowledge and annotations have been provided with the support of  Benoît Delatour  and  Lev Stimmer  from the  Alzheimer's Disease team -Paris Brain Institute (ICM), Paris, France. Some of the research cited here (Section 2, and 4) was supported by  Mr Jean-Paul Baudecroux , The  Big Brain Theory Program -Paris Brain Institute (ICM)  and the  French government  under management of  Agence Nationale de la Recherche  as part of the " Investissements d'avenir "  ANR-10-IAIHU-06  ( Agence Nationale de la Recherche -10-IA Institut Hospitalo-Universitaire-6 ).
Building Blocks of TFHE In this section, we first briefly outline flavors of LWE, we outline a technical notion, referred to as the concentrated distribution, and we provide a list of symbols and notation. Then, we introduce in detail a generalized variant of LWE, denoted GLWE, and we comment on its additive homomorphism, security and other properties. Finally, we define the decomposition operation that we use to build up a compound scheme called GGSW, which enables multiplicative homomorphism. The torus is the underlying additive group of LWE that is used in TFHE, denoted T and defined as T := R/Z with the addition operation. The torus can be represented by the interval [0, 1), with each addition followed by reduction mod 1, e. g. , 0. 3+0. 8 = 0. 1. Since T is an abelian group, we may perceive T as an algebraic Z-module, i.
e. , we further have scalar multiplication Z×T → T, defined as repeated addition. The ring variant of LWE, introduced by Lyubashevsky et al. [13] , extends the module's ring to a ring of polynomials with a bounded degree. In TFHE, we will work with the ring Z[X]/(X N + 1), denoted Z (N ) [X], with N a power of two. Then, the underlying Z (N ) [X]-module comprises torus polynomials modulo X N + 1, denoted T (N ) [X].
Discretized Torus For the sake of simplicity of the noise growth analysis, TFHE is defined over the continuous torus, whereas in implementation, a discretized finite representation must be used instead. To cover the unit interval uniformly, TFHE implementations use an integral type-usually 32-or 64-bit (u)int-to represent a torus element, where we denote the bit-precision by τ. E. g. , for τ = 32-bit uint32 type, t ∈ uint32 represents t /2 32 ∈ T ∼ [0, 1), where the denominator is usually denoted by q = 2 τ (in this case q = 2 32 ). Using such a representation, we effectively restrict the torus T to its submodule T q := q -1 Z/Z ⊂ T.
Decomposition To enable homomorphic multiplication and at the same time to reduce its noise growth, torus elements get decomposed into a series of integers. The operation is parameterized by (i) the decomposition base (denoted B; we only consider B = 2 γ a power of two), and (ii) by the decomposition depth (denoted d). We further denote g := ( 1 /B, 1 /B 2 ,. , 1 /B d ), (6) referred to as the gadget vector. We define gad- get decomposition of µ ∈ T ∼ [-1 /2, 1 /2) ⊂ R, denoted g -1 (µ), as the base-B representation of μ = ⌊B d • µ⌉ ∈ Z (multiplied in R) in the alphabet [-B /2, B /2) ∩ Z. Note that such decomposition is unique. For the decomposition error, it holds that µ -⟨g, g -1 (µ)⟩ ≤ 1 /2B d. (7) We denote ε 2 := 1 12B 2d and (8) 1 https://github. com/fakub/LWE-Estimates V B := B 2 + 2 12 (9) the variance of the decomposition error and the mean of squares of the alphabet [-B /2, B /2) ∩ Z (n.
Existing TFHE Implementations The original (experimental) TFHE library  [29]  is not developed anymore, instead, there are other, more or less active implementations. Here we list selected implementations of TFHE: TFHE-rs  [30] : written in Rust, TFHE-rs implements latest findings by Bergerat et al. [25]  (recently separated from the Concrete Library  [31]  that implements higher-level operations and interfaces); FPT  [32] : an experimental FPGA accelerator for TFHE bootstrapping (a benchmark of state-of-the-art implementations in software/GPU/FPGA/ASIC can also be found in  [32] ); nuFHE  [33] : a GPU implementation of TFHE. TFHE is also implemented as a part of more generic libraries like OpenFHE  [34] , which is a successor of PALISADE  [35]  and which also attempts to incorporate the capabilities of HElib  [16]  and HEAAN  [19].
[23,24]urity of LWE as estimated by lattice-estimator by Albrecht et al. [23,24](commit ID f9dc7c), using underlying group size q = 2 64. Interpolated between grid points. Raw data can be found at https://github. com/fakub/ LWE-Estimates. 40 576. bit-security of LWE 512 32 448 -log(α) 24 256 320 384 16 192 128 8 64 0 256 512 768 1024 1280 1536 1792 2048 LWE dimension n Fig. 3.
Acknowledgments The authors thank the  Korea Institute for Advanced Study  for providing computing resources ( KIAS Center for Advanced Computation  Linux Cluster System) for this work. A special thank should be given to  Profs. Ho Seong Hwang  and  Jihoon Kim  who commented on the photometry of extragalactic images. J. K. was supported by a  KIAS Individual Grant  ( KG039603 ) via the  Center for Advanced Computation at Korea Institute for Advanced Study. J. L. is supported by the  National Research Foundation of Korea  ( NRF-2021R1C1C2011626 ). This work benefited from the outstanding support provided by the  KISTI National Supercomputing Center  and its  Nurion Supercomputer  through the  Grand Challenge Program  ( KSC-2018-CHA-0003 ). A large data transfer was supported by  KREONET , which is managed and operated by  KISTI.