dataesr/scientific-paragraphs-categorization huggingface dataset:
NIR observations of GRB 221009A were carried out with the TNG telescope using the NICS instrument in imaging mode. Here we use observations carried out on October 22, approximately 13. 3 days after the burst, and nearly simultaneous with the JWST observation. The image reduction was carried out using the JITTER task of the ESO-ECLIPSE package. 45 Astrometry was performed using the Two Micron All Sky Survey (2MASS) 46  catalog. Aperture and point-spread function (PSF)-matched photometry were performed using the DAOPHOT package  (Stetson 1987). To minimize any systematic effect, we performed differential photometry with respect to a selection of local isolated and nonsaturated reference stars from the 2MASS and the UKIDSS 47  surveys. The resulting magnitudes in the AB-system are J = 19. 41 ± 0.
06, H = 18. 97 ± 0. 06, and K = 18. 49 ± 0. 08 at 13. 25 days post-burst. A set of millimeter data taken with NOEMA between 78 and 150 GHz (program S22BF, PI de Ugarte Postigo) were interpolated to the epoch of the JWST observation. The observations were performed in the medium-extended C configuration. The data reduction and analysis was done with CLIC and MAPPING from the GILDAS software package, flux calibration was relative to the reference sources MWC349 and LKHA101. The fluxes were determined with UV point-source fits for a consistent error propagation. A full analysis of the NOEMA data set will be published in a forthcoming paper. In addition to data taken nearly simultaneously with JWST, we also utilized the observations with VLT/X-SHOOTER obtained at 0. 5 days post-burst  (Malesani et al.
Hubble Space Telescope We obtained three epochs of imaging with the HST on 2022 November 8, 2022 November 19, and 2022 December 4 (Program 17264, PI: Levan), corresponding to ∼30, 41, and 56 days post-burst. At the first epoch, observations were obtained in F625W, F775W, F098M, F125W, and F160W. A guide star failure in the IR observations during the second epoch meant that observations were only obtained in the optical. At the final observations on December 4, we obtained F625W, F098M, F125W, and F160W. The data were aligned and reduced via astrodrizzle, while the native pixel scale was retained due to the relative paucity of dithers in most cases (0 04 pix -1 for WFC3/UVIS images and 0 13 pix -1 for WFC3/IR images). The images clearly show the afterglow in all bands superimposed on an underlying host galaxy.
SNe peak at around the rest-frame V-band, but the combination of redshift and, in particular, the heavy extinction pushes this peak into the near-IR for GRB 221009A (see, e. g. ,  Fulton et al. 2023). In Figure  2 , we plot the best-fit power law to the data after correction for the extinction. The JWST spectrum is in excess of the model at ∼1 μm and appears to rise at the blue end. However, we caution that this region is where the afterglow is faintest and where the extinction correction is largest. However, the simultaneous GTC spectrum also shows rising flux blueward of the JWST. This may suggest that the blue upturn in JWST is accurate and has a blue SN contribution. This SN would be substantially bluer than SN 1998bw at the same epoch. Still, there is some variation in the apparent colors of SNe associated with GRBs.
g. , to the blue). The challenges of high foreground extinction and a bright afterglow will continue to make studying the SN associated with GRB 221009A challenging. Still, accurate host subtraction, combined with the collation of the extensive, coherent data obtained for GRB 221009A, should enable much better constraints. It is unlikely (although possible in the absence of clear-cut evidence for an SN) that GRB 221009A is created through a compact object merger. The burst's environment (i. e. , its host galaxy) appears very broadly typical of the long-GRB population. There is no evidence of an unusual galaxy or location within the host. This, in turn, implies that the environment in which GRB 221009A formed is comparable to those of other long GRBs at low redshift. For example, it does not match where we may expect to locate very low-metallicity gas, or especially massive stars.
A. J. L. , D. B. M. , and N. R. T. are supported by the  European Research Council (ERC)  under the European Unionʼs  Horizon 2020 research and innovation program  (grant agreement No. 725246 ). G. P. L. is supported by a  Royal Society Dorothy Hodgkin Fellowship  (grant Nos. DHF-R1-221175  and  DHF-ERE-221005 ). J. H. and L. I. were supported by a  VILLUM FONDEN   Investigator grant  to  JH  (project No. 16599 ). B. D. M. acknowledges support from the  National Science Foundation  (grant No. AST-2002577 ). J. P. U. F. acknowledges support from the  Carlsberg Foundation. The  Cosmic Dawn Center (DAWN)  is funded by the  Danish National Research Foundation  under grant No. 140. D. A. K. acknowledges the support by the  State of Hessen  within the  Research Cluster ELEMENTS (Project ID   500/10.
This study aims to develop a robust pipeline for classifying invasive ductal carcinomas and benign tumors in histopathological images, addressing variability within and between centers. We specifically tackle the challenge of detecting atypical data and variability between common clusters within the same database. Our feature engineering-based pipeline comprises a feature extraction step, followed by multiple harmonization techniques to rectify intra-and inter-center batch effects resulting from image acquisition variability and diverse patient clinical characteristics. These harmonization steps facilitate the construction of more robust and efficient models. We assess the proposed pipeline's performance on two public breast cancer databases, BreaKHIS and IDCDB, utilizing recall, precision, and accuracy metrics.
Our pipeline outperforms recent models, achieving 90-95% accuracy in classifying benign and malignant tumors. We demonstrate the advantage of harmonization for classifying patches from different databases. Our top model scored 94. 7% for IDCDB and 95. 2% for BreaKHis, surpassing existing feature engineering-based models (92. 1% for IDCDB and 87. 7% for BreaKHIS) and attaining comparable performance to deep learning models. The proposed featureengineering-based pipeline effectively classifies malignant and benign tumors while addressing variability within and between centers through the incorporation of various harmonization techniques. Our findings reveal that harmonizing variabilities between patches from different batches directly impacts the learning and testing performance of classification models.
One of the major challenges in biomedical research lies in the necessity to have substantial volumes of patient data to train classification models effectively. However, in the vast majority of biomedical applications, such extensive datasets are not readily available. Consequently, we often resort to pooling patient data from multiple acquisition centers. This practice introduces what is commonly known as the "batch effect, " an artifact attributed to differences in acquisition hardware or protocols. Such batch effects hinder the generalizability of our models. Data harmonization is becoming an increasingly crucial issue in biomedical research. This is done by estimating the batch effect between different centers and minimizing its impact, thereby enhancing the generalizability of the models.
This technique has been extensively applied in medical imaging, in FDG PET/CT imaging, as seen in works like  1  and 2 and in MRI imaging  3. However, harmonization of features in histopathological slices is less widespread. Harmonization constitutes a critical, yet intricate, facet of histopathological image classification. Specifically, two principal types of variability serve as obstacles to the robust performance of machine learning algorithms: intra-database and inter-database. Intra-database variability arises from inconsistencies present within a single data collection center, often taking the form of variations in staining or fluctuations in quality across patches within an individual histopathological slide. Figure  5  illustrates examples of patches from a histopathological slide following unsupervised clustering.
One cluster comprises images of border regions, while the other encapsulates images of central regions, thereby underscoring the need for intra-slide harmonization to mitigate such variabilities. Additionally, this intra-slide variability is manifest not only between different cluster patches but also within the same cluster patches across various classes, as illustrated in Fig. 6. Inter-database variability exists across multiple centers and originates from heterogeneous imaging technologies or diverse acquisition protocols. These variabilities compromise the fidelity of machine learning models, rendering them less reliable and poorly generalizable. The significance of this research lies in its twofold contribution to histopathological image classification. Firstly, by addressing both intra-database and inter-database variabilities, our approach improves the generalizability and robustness of machine learning models across diverse imaging protocols.
This directly contributes to increase diagnostic accuracy. Secondly, the proposed harmonization techniques enhance model reliability, particularly in multi-center clinical settings, thereby impacting early cancer diagnosis and treatment. To surmount these challenges, our research proposes a harmonization-centric pipeline operational on dual fronts: intra-database and inter-database. Breast cancer continues to pose a critical public health challenge globally. Early detection remains crucial for favorable patient outcomes but is frequently impeded by the excessive workload and the potential for human error in conventional diagnostic procedures  4. Artificial Intelligence (AI) and machine/deep learning (ML/DL) have emerged as potent adjuncts to human expertise in clinical diagnosis, and in certain scenarios, surpass it  5.
The focal point of our research is to bridge the existing research gap by concentrating on both intra-database and inter-database harmonization methods. Within the domain of intra-database harmonization, we introduce methodologies to standardize patches within each histopathological slide, thereby alleviating intra-slide variability and augmenting classification performance. For inter-database harmonization, we implement techniques to synchronize data across disparate databases, thereby yielding a consolidated and robust training set. Several preceding studies have ventured into data harmonization in the context of medical imaging. For instance, the ComBat technique, developed by Johnson et al. , aimed to ameliorate non-biological variations often found in microarray data  6. Subsequent adaptations of this method extended its application to harmonize data in PET/CT/MRI imaging  7, 8, 9, 10, 11.
However, these works have largely focused on inter-database harmonization, neglecting the challenges associated with intra-database variability. To address this research gap, our study employs a pipeline that melds feature engineering with harmonization techniques. Specifically, we propose a novel strategy for harmonizing patches categorized as atypical, as well as clusters produced through unsupervised classification techniques. In doing so, we aspire to enhance the reliability and accuracy of histopathology classification models. The pipeline undergoes evaluation in the setting of classifying histopathological slides as either cancerous or non-cancerous, using data from two publicly accessible databases. Our objective entails assessing multiple harmonization methods to navigate both intra-and inter-database variabilities, thereby facilitating the selection of the most suitable model for accurate classification.
Dataset We used two publicly available datasets of histopathological images of breast tumors for our study: the Invasive Ductal Carcinoma (IDC) dataset  12  and the Breast Cancer Histopathology Image Classification (BreakHis) dataset  13. Both datasets contain digitized images of histopathological slides, and have been used extensively in previous research on breast tumor classification using machine learning techniques. The IDC dataset includes images of invasive ductal carcinoma from 162 patients, scanned at 40x magnification with a whole slide scanner. A total of 277,524 patches of size 50x50 pixels were extracted from these slides, of which 78,786 were positive for IDC and 198,738 were negative. The dataset was annotated using Aperio's ImageScope visualization software. This dataset was chosen for its large size and well-defined target variable (IDC vs.
non-IDC). The BreakHis dataset, on the other hand, contains images of both benign and malignant breast tumors of different histological types. It includes 9,109 microscopic images of breast tumor tissue collected from 82 patients at different magnification factors. This dataset was built in collaboration with the P &D Laboratory -Pathological Anatomy and Cytopathology, Parana, Brazil. It currently contains four distinct histological types of benign breast tumors and four malignant tumors. This dataset was chosen for its diversity of histological types, which can have different implications for prognosis and treatment. Both datasets present potential intra and inter variabilities due to differences in acquisition parameters, scanner type, and staining protocols, among others (Table  1 ). These variabilities can affect the performance of machine learning models trained on these datasets, and thus highlight the need for harmonization techniques to reduce their impact.
Methods We delineate the technical specifics of the pipeline we engineered for the task of histopathological image classification, with a particular emphasis on its modular architecture. We have incorporated two harmonization modules to tackle the complexities arising from intra-database and inter-database variabilities. The intra-database harmonization module (depicted in Fig. 1 ) incorporates a series of pre-processing blocks designed to minimize within-database disparities. These blocks are optimized to handle anomalies that cannot be classified as outliers but still demonstrate distinct characteristics, thus potentially biasing model training (see Fig. 3 ). Indeed, in the proposed methodology, we have designed a pipeline that extracts radiomic features. Following normalization, the data are partitioned into training and test sets (adhering to the literature percentages for both training and testing sets).
Subsequent processing is applied exclusively to the training set; however, these procedures are extrapolated to the test data without including them in the training process. For the objective of multi-center adaptability, our pipeline features an additional harmonization module. This system, illustrated in Fig. 2 , serves to improve the model's resilience when exposed to data from different centers. The pipeline was implemented using Python 3. 7 with the TensorFlow library and Keras in their versions 2. 3. 0. We evaluated the different models using balanced accuracy, recall, and precision as metrics. In the proposed methodology, we designed a pipeline that extracts radiomic features. Following normalization, the data is partitioned into training and test sets. Subsequent treatments are applied to the training set, while the derived insights are extrapolated to the test set without incorporating them into the learning process.
Preprocessing and normalization As we calculated different groups of features, including histograms, GLCM, and entropies, we considered two methods to normalize the data: StandardScaling and RobustScaling. In both methods, the Not a Numbers (NaNs) were treated as null values. However, we opted for the RobustScaling method because it is robust to outliers, unlike the classical StandardScaling method, which we later coupled with the ComBat harmonization. the scaling follows the formula (1): The RobustScaling method removes the median and scales the data to the interquartile range (IQR), which is the difference between the 1st and 3rd quartiles. The centering and scaling of this method are not influenced by the presence of a number of marginal outliers based on percentiles. We implemented this method using the Python library "sklearn".
Intra-database harmonization module This section aims to identify patches within the training sets of both IDCDB and BreaKHIS that are considered outliers or atypical. The objective is to bring these samples into closer proximity, thereby reducing the variance between patches containing similar information. For data visualization, we applied Principal Component Analysis (PCA)  17  to the standardized data using StandardScaling. We then projected the data onto the first two factorial axes to visualize the results obtained (see Fig. 3 ). We assumed that the distant points (atypical data) in the scattered plot are patches from a different source than the main point cloud. The PCA plot revealed the presence of many outliers. In our study, we utilized a dataset comprising pre-segmented histopathological patches.
This methodology is illustrated in Fig. 4. Upon identifying the most efficient outlier detection algorithm, we applied it to the entire dataset. The dataset was then divided into two subsets: one containing atypical patches (border patches) and another consisting of non-atypical patches (primarily central patches from the histopathological slides). The results of this classification are presented in Fig. 5. To harmonize the features extracted from the atypical and non-atypical patches, we used the ComBat method  6. This method is designed to eliminate non-biological variability or "batch effects" that commonly occur in multiple batches of microarray experiments. The ComBat method estimates the parameters that represent batch effects using Empirical Bayesian (EB) techniques  6. The ComBat method has been widely used in the biomedical fields to harmonize multicenter data  7, 8, 10, 21.
The model equation follows formula (2): where Y ijg represents the expression value for the patch g for sample j from batch i. X is the design matrix for sample conditions, multiplied by the β g that contains the regression coefficients corresponding to the design (2) Y ijg = α g + Xβ g + γ ig + δ ig ε ijg , Figure 3. The projection of the samples onto the principal factorial plane, both before and after harmonization, elucidates the impact of our methodology on the projected scatterplot. As illustrated, patches with either IDC or non-IDC subtypes can exist as outliers within the entire dataset and need to be aligned closer to the reference scatterplot, which comprises the majority of samples. matrix. γ ig and δ ig are considered as the site parameters (additive and multiplicative batch effect).
Thus, they are estimated using EB and removed in the final adjustments. The harmonization of the data was done on the main point cloud and on the atypical data using a batch vector containing logic values (01 for a patch belonging to the point cloud and 10 for a patch belonging to the atypical patch cluster). The harmonization process involved four steps. Firstly, we created a design matrix containing the covariates of the patches and the batch values. Secondly, we standardized the data across features to prepare it for fitting. Thirdly, we fit a linear model to the data to compute the γ * and β * parameters that represent the batch effect. Finally, we adjusted the data by removing the computed batch effect, resulting in harmonized features across the entire dataset. As depicted in Fig. 3b , the intra-base harmonization approach successfully reduced the intra-class distance and brought the outliers closer to the main point cloud, resulting in a significant reduction in the patch-level variability.
Classification The datasets used in our study are highly imbalanced, with non-cancerous patches being much more prevalent than cancerous ones. Specifically, the IDC dataset contains approximately 60% non-cancerous patches and 40% cancerous patches, while the Breakhis dataset contains 864 non-cancerous patches and 625 cancerous patches. To address this imbalance, we implemented a synthetic minority oversampling technique (SMOTE)  22  , which generates synthetic samples of the minority class to balance the dataset for training the models. We used the SMOTENN variant  23  , which combines undersampling using the Edited Nearest Neighbors method and oversampling using the SMOTE method and the RandomUnderSampler technique, for quick and easy balancing of the minority class by randomly selecting a subset of the data for the targeted class.
Best models for IDC and BreaKHis In order to better detail the performance of these best models that perform better than current state-of-the-art, we provided the recall, precision and F-score values (Tables  10  and 9 ), along with the different options of each model that led to the results of each. In Tables  9  and 10 , we present the detailed results of the best models obtained for both IDC and BreaKHis databases. Note that the F-score is the harmonic mean of the precision and the recall values.
Multicenter models In Table  13 , we present the comparative results among three models that used the multicenter harmonization. The best performing model is MultiC 1, which achieved an accuracy of 95. 67% and 95. 04% for training and validation, respectively, and 80% and 81% for testing on IDC and BreaKHis databases, respectively. These results demonstrate the effectiveness of multicenter harmonization in improving the robustness of the models against external data.
Discussion Initially, we trained models without any data pre-processing except for normalization to establish a baseline for comparison. These base models achieved a testing balanced accuracy of approximately 67% for the IDC database and almost 90% for the BreaKHis database (Table  5 ). The disparity in performance can be partially attributed to the larger size and greater diversity of the IDC database (277,524 images) compared to BreaKHis (1,490 images). Drawing on the baseline models, we compared our proposed approaches for outlier harmonization within each database. We observed very similar performances (Table  6 ), with a slight advantage for the ComBat outliers harmonization after StandardScaling normalization approach ( 95. 3%, 87. 48%) over the RobustScaling only approach ( 94. 4%, 87.
48%) on the BreaKHis and IDC databases, respectively. The results in Table  6  compare the outcomes obtained from our three different configurations: Configuration 1 refers to RobustScaling, Configuration 2 to ComBat harmonization without outlier detection, and Configuration 3 to ComBat harmonization with outlier detection. With the best pipeline options determined (i. e. , ComBat outliers harmonization and StandardScaling normalization), we evaluated the techniques implemented to alleviate the balancing problem between malignant and benign patches. The DownSampling method achieved classification scores of 81. 39%, 80. 34%, and 74. 88% on training, validation, and test sets for the IDC database. Conversely, the SMOTENN method outperformed DownSampling, yielding improvements of 10% on the training and validation sets and 5% on the test set for the same database (Table  7 ).
After balancing the number of patches, we assessed our pipeline's components designed for per-patch harmonization to reduce variability between patches containing the same information. We selected the best models resulting from the previous stages. The best outcomes obtained from this stage correspond to the models where we applied StandardScaling normalization, outlier harmonization with ComBat, balancing with the SMOTENN method, and Harmonization between cluster patches (Table  8 ). These results help identify the best options to implement in our proposed pipeline, achieving high performance in differentiating malignant and benign patches across the two distinct databases. The highest performance was observed in Configuration 8 for the BreaKHis dataset, with 100% accuracy on the training set, 94.