dataesr/scientific-paragraphs-categorization huggingface dataset:
Predict cell type: Once the activity matrix generated, a k-Nearest Neighbors matrix with k=20 is computed, then an adjacency matrix using Jaccard distance and finally transformed as a weighted graph using igraph function graph. adjacency. Clustering is then performed using leiden_find_partition from leidenbase package with ModularityVertexPartition as partition type and a maximum number of iterations of 2. The average activity score is computed by cell type and by cluster. Each cluster is attributed the cell type for which the activity score is the highest, if it passes a threshold of default value 0, otherwise it is labeled as unassigned. This value can be modified by the user, depending on the level of confidence needed for annotation.
We build an object detection model to automate chromosomal aberration detection using recent advances in deep convolutional neural networks and statistical learning. We formulated the problem of rare aberration detection as a heatmap regression problem requiring the minimization of a sparsitypromoting loss to reduce the false alarm rate. Our Unet-based approach is analoguous to a one-stage object detector, and keeps the number of hyperparameters to a minimum. Finally, we demonstrate large performance improvements using an ensemble of checkpoints collected during a single run of training. A PCA-based strategy is used to provide cues for interpretation of our deep neural networkbased model. The methodology is demonstrated on real, large, and challenging datasets depicting rare chromosomal aberrations and is favorably compared to a reference dosimetry technique.
Organization The remainder of the paper is organized as follows. In Section 2, we first describe the related works for automated biological dosimetry. Second, we briefly summarize the main features of ConvNets and review the ensemble approaches in deep learning. In Section 3, we present our training framework based on sparse representations and model ensemble. In this section, we describe a novel visualization approach to explore the model building during training. Section 4 is devoted to data descriptions and evaluation metrics definition. We use two datasets: a training dataset where aberrations are overrepresented, and a validation dataset for calibration curve estimation. In Section 5, we evaluate the performance the single model and model ensemble and demonstrate the robustness of the method regarding the presence of debris in metaphase images.
Cytogenetic biological dosimetry As chromosomes are a very common object of interest in cytogenetic biological dosimetry, characterizing the shape of chromosomes with computer vision tools has been a longstanding area of research. Typically, one approach consists in detecting chromosome centromeres  [39] , as they are a reliable indicator to separate dicentrics from monocentrics. Centromeres may be retrieved by locating minimas of the chromosome width along the centerline, as shown in  [40, 37]. Those classifiers have been used in pipelines tackling automated dose estimation in several different commercial solutions. In Europe, Metafer (provided by MetaSystems) has the largest marketshare, and its performance have been evaluated several years ago  [41, 10]. In  [24] , Liu et al. showcased a software stack called ADCI which estimated a dose from metaphase images, using conventional computer vision techniques and prior chromosome morphology.
Key-point regression in deep learning In key-point regression, Gaussian spots are predicted over specific landmarks of the image, like the eyes on a human face or the joints of a skeleton. It is a subtask of a large number of computer vision tasks like facial recognition or pose estimation  [4, 5]. It can be solved with common image segmentation models by minimizing the L 2 distance between a predicted heatmap and the corresponding ground truth over a training dataset. While heatmap regression and object detection are different tasks at first glance, and heatmap regression does not predict bounding box extent, several authors have proposed a unified method that consists in predicting a center point and bounding box dimensions  [44] , or the corners of a bounding box as key-points  [21].
Keypoint regression with heatmap regression models In Heatmap Regression Models (HRMs), objects of interest are represented as Gaussian spots. The model is trained to predict spot positions in the image domain, with a labelled dataset D = {(x 1 , y 1 ), • • • , (x n , y n )} comprised of n realizations of a pair of random variables (X, Y ). For each image x i , we have x i (u, v) ∈ [0, 1] at each location (u, v) ∈ Ω, where Ω denotes the image grid of size |Ω| = H × W.
This regularizer is defined as  [32] : where ρ is a parameter that balances the sparsity and the smoothness terms in the predicted heatmap. The components of the gradient vector are computed with respect to the image coordinate axes as follows: R(ϕ θ (x i )) = (u,v)∈Ω ρ 2 ∥∇ u,v ϕ θ (x i )∥ 2 2 + (1 -ρ) 2 ϕ θ (x i ) 2 (u, v), (3) ∇ u,v ϕ θ (x i ) = ϕ θ (x i )(u, v) -ϕ θ (x i )(u + 1, v) ϕ θ (x i )(u, v) -ϕ θ (x i )(u, v + 1). ( 4 ) As this is a simple linear transformation of the image, computational overhead is minimal. The criterion (2) is highly non-convex because of non-linearities in ϕ θ. Therefore, finding a global minimum is hopeless. Nevertheless, a good local minima may be found using iterative first order methods, usually some variant of SGD. The exact gradient of the training criterion with respect to θ is estimated on a random subset J of the complete dataset D, because of memory constraints: ∇ θ n i=1 L(ϕ θ (x i ), y i ) + R(ϕ θ (x i )) ≃ ∇ θ |J | j=1 L(ϕ θ (x j ), y j ) + R(ϕ θ (x j )).
Setting of model parameters We trained Unet for N e = 100 epochs epochs with Adam  [8] , with a constant learning rate of 3 × 10 -4 , a weight decay parameter of 0. 1 and a batch size of 12 on a single Tesla V100. The learning rate was unchanged during training to ensure parameter space exploration, using an analoguous reasoning to the one provided in  [15]. We did not use data augmentation for two reasons. First, we found that the wide variety of chromosome morphology and orientations in our dataset was enough for our model to learn this invariance. We did not observe detection failures based on object orientation. Second, more agressive data augmentation like noise or blurring quickly made monocentrics and dicentrics indistinguishable. Training stability was very sensitive to the variance of the blurring kernel or the Gaussian noise, because accurate chromosome classification relies on very small details.
The rest of this section gives a formal overview of our visualization technique. Formally, for an input image x i of size H × W , the ℓ-th layer of our Unet produces a feature volume f ℓ i of size H ′ × W ′ × N ℓ (see Section 5. 3 and Figure  5 (a) ). Here, we choose the second-to-last layer of the decoder and we drop the superscript ℓ to improve readability, so that f ℓ i = f i. For the last layer, N ℓ = 128 (see illustration in Figure  5 ). We retrieve the set of feature vectors that correspond to the spatial locations of aberrations (dicentrics and fragments) in image x i. For a set of N i a aberrations located at X i a = {(u 1 , v 1 ), • • • , (u N i a , v N i a )} in image x i , we build a set of feature vectors f i a = {f i (u 1 , v 1 ), • • • , f i (u N i a , v N i a )} ∈ R N i a ×N ℓ.
Data description Our training dataset is composed of 5430 labelled images of size H = 888, W = 1008, padded to H = 896, W = 1008 to ensure that downscaling has an integer height and width. Labels are binary images with size H ′ = 202, W ′ = 252, taking value 0 everywhere except at the center of chromosomal aberrations (roughly between the two centromeres for a dicentric chromosome), where it takes value 1. There is one binary image per aberration classes for each image, so that aberration classification is possible. Chromosomal aberrations are the only labelled objects, neither debris nor monocentric chromosomes are labelled. We chose this labelling scheme instead of semantic segmentation or bounding boxes as it lead to the lowest labelling time, which in turn meant a greater number of images could be labelled for the same labelling budget.
For the same reason, we did not label debris or monocentric chromosomes. This also prevented the discovery of trivial models where chromosomes would be detected but always labelled as monocentric, as they outnumber dicentric ones by an extremely large margin. As explained in Section 3. 1, this binary image is blurred with a Gaussian kernel, to reduce the underrepresentation of the labels against the background. Images have been selected so that each images contains only the chromosomes corresponding to a single cell, i. e. , no image contains more than 46 chromosomes. The metaphases in the dataset do not have any missing chromosome, or an excessive chromosome count. Our dataset contains 5021 dicentrics and 7540 fragments, Figure  6  shows the repartition of images of images into aberration counts bins.
Images with a high aberration count are much rarer than images with a low aberration count. On average, there is more than one aberration per image, which corresponds to a very high ionizing radiation dose. As a normal metaphase contains 23 chromosome pairs, this means that even in this case, the overwhelming majority of chromosomes are healthy (i. e monocentric) ones. In our evaluation setting, the training set consists of 80% of those image. 10% of the images are retained for a validation set, used for hyperparameter selection. Finally, 10% of the data is held as a test set for a fair performance evaluation. As this training dataset is not representative of a real-world exposition, we use another dataset to estimate the calibration curve of our model. This dataset was built by collecting metaphases from samples irradiated at specific, known doses.
The aberrations in this dataset are not labelled. It contains 21215 metaphases taken from samples irradiated at 0 Gy, 0. 1 Gy, 0. 2 Gy, 0. 3 Gy, 0. 5 Gy, 0. 7 Gy, 0. 9 Gy, 1 Gy, 1. 5 Gy, 2 Gy, 3 Gy and 4 Gy.
For example, in the segmentation task, the Recall of Metafer lies between 35% and 75%. For the classification task, The Recall is around 35%, and the Precision around 40%. Note that the performance figures given for Metafer in Table  2  are not computed on the dataset mentioned in Section 4. 1, but taken from  [41]  instead. Although the performance of Metafer is uncertain, and we did not compute it on our dataset, we feel confident that our model brings a very significant improvement in chromosomal aberration, as the uncertainty around the ensemble performance is low enough that even in the worst case scenario, it achieves very significant performance improvements over Metafer. Finally, training with a loss that promotes sparsity improves Precision and Recall for fragments and dicentrics, as seen in Figure  8.
Robustness to non-chromosome objects in metaphase images An automated dosimetry system should always distinguish between chromosome and non-chromosome (nuclei, debris) objects. Current automated methods reject non-chromosome objects using explicit shape analysis. For example, a nuclei can be rejected using the fact that it is broadly circular and has a uniform texture. However, the shape of debris is usually more complex, which makes metaphase selection more difficult in most dosimetry systems. In our approach, we do not detect debris and nucleis explicitely, as they are not annotated in our dataset. Instead, Unet learns to reject debris from the training data, without the need for specific annotations or handcrafted object detection algorithms. In the rest of this section, we propose a simple visualization of this fact, using PCA.
Transfer between training and calibration curve datasets As explained in Section 1, most images routinely analyzed by biologists do not contain any aberration, even for high doses. Our annotated training dataset is a subset of a much larger archive of patient data. This annotated dataset contains 5,430 images depicting at least one aberration over ∼80k images. This reduces training time considerably, and prevents the discovery of trivial models where no object is ever predicted. However, this also means that our training dataset is not an accurate representation of real-world metaphase images as metaphases containing aberrations are considerably over-represented. In the previous section, we saw that the model described in this paper performs very well in terms of object detection metrics. However, we still need to investigate wether a model trained on this unbalanced dataset can accurately estimate aberration counts on a realistic dataset.
Initial calibration curve estimates were unsatisfying: our ensemble would overestimate low doses, and underestimate high doses. Two additional details were needed to improve performance. First, we investigated the Cumulative Distribution Function (CDF) of the maximum probabilities predicted by the members of the ensemble for the dicentric  To improve readability, we show the 4 curves closest to the manual calibration curve displayed in black. Metafer curve is displayed in red. and fragment class. Those CDFs were estimated using the 4 Gy subset of the calibration curve dataset, and are shown in Figure  13. Instead of setting a single threshold for all model, we picked a quantile, and retrieved the corresponding CDF value for each model. Furthermore, we used domain knowledge to reject spurious dicentric detection.
We considered dicentric detections if and only if at least one fragment was present in the same image. As usual in biological dosimetry, we fitted a linear-quadratic model to the point cloud of average dicentric count retrieved on the calibration curve dataset. This led to a large improvement in our calibration curve estimation, as shown in Figure  14. Furthermore, the Metafer calibration curve is semi-automated: dicentric chromosomes undergo a manual review, because of the very high FPR of the algorithm, as described in Section 5. 1. On the contrary, our calibration curve is obtained in a fully automated setting.
Furthermore, a simple regularization term modeling the intrinsic heatmap sparsity helps performance. We pushed this performance further by ensembling several checkpoints collected during training. We proposed a visualization of the latent features of Unet during training to explore the relationship between the dynamics of training and this performance improvement. Furthermore, we showed that the variation in performance between different (randomly sampled) ensembles is lower than between single checkpoints of a training run. This is especially relevant in the context of the deployment of a deep learning model in an automated fashion in a medical setting. Those improvements can be achieved without the need for any architectural modifications or extensive hyperparameter calibration. Our database is very imbalanced: the average number of aberration per cell is over 1, which corresponds to an extremely high dose of ionizing radiation.
It is very likely that some of the spurious detections can be attributed to this training set imbalance. We evaluated our ensemble of Unet in a realistic setting, on a calibration curve dataset. Using model-adaptive thresholding and the domain knowledge of co-occurence of dicentrics and fragments, we reach a very competitive calibration curve, widely outperforming the Metafer baseline. Furthermore, our ensemble can be used in a fully automated fashion, while the Metafer solution requires manual review to reduce the number of false positives. It is therefore possible to build a competitive aberration detection system even with a large distribution gap between training and inference images.
Materials and methods An online survey was distributed by the Glomerulonephritis WG to all ESPN members by email. The survey was web based and developed in the English language. It assessed three domains: center policy for administration of RTX (dose, number of courses, co-administrated drugs, CD19/20 monitoring), screening policy for HGG defined as serum levels below age-standardized reference ranges (prior, during and after 9 months after RTX infusion), its management with IgG replacement therapy (IGRT), and RTX-associated morbidity and mortality (center observations on the number of patients with severe infections requiring hospitalization, data on individual etiology and outcome). The survey consisted of 15 main questions and 25 subquestions of which 29 were close-ended and 11 open-ended.
Respondents were able to review and change their answers. Question response types included 20 dichotomous, 8 single choice, 1 multiple choice, and 11 free text responses. The survey was developed by means of Google Documents Application. Participants were informed on the number of questions and time for survey completion. The survey was non-obligatory and no incentives were offered. Answers were stored automatically in a database. Replies were obtained from 84 centers representing 14 EU countries (Belgium, Czech, Denmark, France, Germany, Greece, Italy, Malta, the Netherlands, Poland, Portugal, Spain, Sweden, and the UK), 2 non-EU countries (Russia and Turkey), and 3 non-European locations (Canada, Israel, and Iran). The response rate was 100% for 28 queries and between 91 and 98% for the remaining 13.
Though infrequent, serious infections in nephrotic subjects are an important issue and children with this complication require monitoring and rapid management as 3 deaths were recorded among the 33 children reported with severe infections. In conclusion, we advocate for the routine screening for HGG prior to and post RTX treatment in children with FRNS/ SDNS. The survey has demonstrated that if screened for, HGG is a widely recognized complication observed by nearly half of European pediatric nephrology centers using this off-label therapy. The main limitation of the performed survey is the lack of data on the exact incidence of HGG in children treated for INS and the influence of RTX and other IMS drugs on its incidence and duration. Furthermore, the design of the study did not enable a risk analysis for the development of HGG nor the risk of acquiring severe infections in its presence.
L0-norm was another important prior constraint in medical imaging and it can provide a more sparse solution than TV  [13]. For example, to overcome the weakness of TV in over-smoothing low-contrast structures, Sun et al. attempted to introduce L0-norm optimization into the IR method and achieved obvious promotion  [14]. Then, Wang et al. paid attention to punishing the wavelet coefficients based on the L0-norm and outperformed classical CT reconstruction in metal artifact suppression  [15]. To better improve the performance in feature restoration, Xu et al. combined the L0-norm and dictionary learning (L0DL) for limited-angle CT. Both simulated and preclinical datasets claimed the proposed L0DL generated high-quality images with more clear structures  [16]. In addition, non-local similarity  [17] , low-rank  [18] , and sparse representation  [19, 20]  were also commonly used as prior knowledge in IR algorithms for CT reconstruction and achieved competitive results.
Cross-domain Residual-Optimization based Structure Strengthening (CROSS) framework for limited-angle CT reconstruction. The proposed CROSS method consists of three steps, which alternatively perform the CNN model on the image domain and projection domain. Specifically, in the first step, the structure strengthening network (SS-Net) is adopted to remove the artifacts and preserve the textures. Next, the missing projection data can be obtained from the results in the first step according to  [32]. For the purpose of data consistency improvement, a residual network (ResNet) is applied to boost restored projection data. Different from the last step in  [33] , the proposed CROSS utilizes the SS-Net on the residual error map rather than the CT images, which could improve the restoration accuracy in the areas that have the larger attenuation coefficients.
Compared to deep iterative methods  [44, 47] , the CROSS method can be applied to cone-beam CT reconstruction with less computational cost and provides competitive results simultaneously. Experiments based on the simulated and real datasets validate that the proposed CROSS method performs well in edge preservation, detail restoration, and artifact removal. The rest of this paper is organized as follows. Section II introduces the workflow of the proposed CROSS framework and the details of the neural network used in this work. Section III gives the experimental results and analysis of the different components in CROSS. In section IV, we will discuss some related issues and make a plan for the next work.
B. Structure Strengthening Network To better reconstruct images for limited-angle CT, the SS-Net is employed in the image domain, whose architecture is shown in Fig. 2. SS-Net has three branches, which are the image restoration sub-network, structure-enhanced sub-network  [49] [50] [51] , and feature fusion sub-network, respectively. The image restoration sub-network (IRS-Net) (as shown in Fig. 2(a) ) adopts the FED-INet as the backbone  [47] , which employs the asymmetric convolutional module (ACM) to improve the feature extraction and has been effectively validated for tissue restoration. IRS-Net is a classical image-domain-based method that takes the FDK reconstruction images as input and generates high-quality images 𝑩𝑩 𝟏𝟏 with most structures and less artifacts. So, IRS-Net holds the low-frequency information of CT images.
Dissimilarly, the structure enhancement sub-network (SES-Net) (as illustrated in Fig. 2 (b) ) attempts to preserve the texture information contained in the CT images. SES-Net also uses the FED-INet as the backbone and inputs the FDK results but outputs the structure details 𝑩𝑩 𝟐𝟐. Consequently, the high-frequency information of the CT images is stored in the feature maps of the SES-Net. Since IRS-Net and SES-Net separately possess the low-and high-frequency components of the CT images, they can be further improved by fusing their feature maps. As depicted in Fig. 2(c ), the feature fusion module (FFM) concatenates the feature maps together, which are extracted from the IRS-Net and SES-Net respectively, and brings improvements over 𝑺𝑺 𝟏𝟏 in edge preservation.
III. EXPERIMENTS In this work, all the experiments were conducted on a PC with CPU Inter(R) Xeon E5-2683 and GPU NIVIDA GTX TITAN. The hyper-parameters 𝜆𝜆 1 and 𝜆𝜆 2 in Eqs. (  5 ) and  (7)  were set to 0. 001 and 0. 001, respectively. All the parameters of the three networks Φ 1 , Φ 2 and Φ 3 were updated using the Adam algorithm  [58]. Noting that these three networks were dependent and needed to be trained one by one. The learning rate was initially set to 10 -3 and linearly decreased to 1e -5 within 50 epochs. To evaluate the proposed CROSS framework, five reconstruction methods were treated as comparisons, including the FDK algorithm (Ramp-filter), TV  [59] , DDNet  [48] , MSWDNet  [28] , and DCAR  [32]. Specifically, DDNet, MSWDNet, and DCAR were implemented in the TensorFlow framework with Python language.
A. Simulated Data Results The images of simulated experiments were downloaded from The Cancer Imaging Archive (TCIA). In this work, we adopted cone-beam geometry to evaluate different reconstruction methods for limited-angle CT. The geometry parameters were as follows. The source-to-detector distance was 100 cm and the source-to-axial distance was 50 cm, respectively. The detector size was 900×400 and each element covered an area of 1. 5×1. 5 mm 2. For one circle, there were 960 views were collected. The volume was reconstructed with 512×512×200 and each voxel was 0. 9×0. 9×0. 9 mm 3. It was worth noting that FDK will lead to cone-beam artifacts when the reconstructed slice is far away from the central slice. To overcome this issue, we only selected the middle 120 slices in the entire volume.