dataesr/scientific-paragraphs-categorization huggingface dataset:
This study tests the generalisability of three Brain Tumor Segmentation (BraTS) challenge models using a multi-center dataset of varying image quality and incomplete MRI datasets. In this retrospective study, DeepMedic, no-new-Unet (nn-Unet), and NVIDIA-net (nv-Net) were trained and tested using manual segmentations from preoperative MRI of glioblastoma (GBM) and low-grade gliomas (LGG) from the BraTS 2021 dataset (1251 in total), in addition to 275 GBM and 205 LGG acquired clinically across 12 hospitals worldwide. Data was split into 80% training, 5% validation, and 15% internal test data. An additional external test-set of 158 GBM and 69 LGG was used to assess generalisability to other hospitals' data. All models' median Dice similarity coefficient (DSC) for both test sets were within, or higher than, previously reported human inter-rater agreement (range of 0.
However, these algorithms are trained and assessed on a highly curated dataset optimised for quality: each subject has a complete dataset of high-quality pre-and post-contrast T1-weighted (T1w and T1c, respectively), T2-weighted (T2w), and T2-weighted fluid-attenuated inversion recovery (FLAIR) images, which does not accurately reflect the realities of clinically-acquired MRI data. For example, a recent study using a model (DeepMedic) trained exclusively on BraTS data, achieved a median Dice similarity coefficient (DSC) of 0. 81 on BraTS test data but only 0. 49 on external clinical data  23. The aim of the current study was to determine the performance and generalisability of three of the highestperforming models at recent BraTS challenges  [24] [25] [26]  on real-world clinical data. Models have been trained with both BraTS data and another multi-centre dataset obtained from 12 different hospitals worldwide: the PICTURE project (www.
Materials and methods All patients provided informed consent and data were obtained and anonymized according to the General Data Protection Regulation and Health Insurance Portability and Accountability Act. Local Institutional Review Board approval was obtained for all primary studies. For the the VU medical center Amsterdam the institutional review board approved of the experiments in this study under case nr. 2014. 336. Of the patients involved in the current study, 40 were previously studied in an inter-rater agreement study by Visser et al. 29. The 275 Glioblastoma patients from the PICTURE dataset were previously used in a study focused on robust tumor core segmentation in glioblastoma patients Eijgelaar et al. 23. The study was carried out in concordance with the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) guidelines  32.
Missing scans Both datasets contain pre-operative T1w, T1c, T2w, and FLAIR images. However, in the PICTURE dataset some patients had missing sequences, see Table  1  for a breakdown and Fig. 1  for examples of subjects with a missing FLAIR or T2. Only patients with at least T1c and either T2w or FLAIR were included to be able to manually segment all tissue classes. Out of 1731 total cases, there were 204 missing pre-contrast T1w, 186 missing T2w, and 19 missing FLAIR, see "Model training and testing" for details of the sparsified training used to account for missing sequences.
Pre-processing T1w, T2w, and FLAIR images were rigidly registered to the T1c image. Subsequently, the T1c was registered to the SRI24 atlas (https:// www. nitrc. org/ proje cts/ sri24/)  33  using an affine transformation. The same transform was applied to the other MR sequences (T2w, FLAIR, T1w). All modalities were resampled to 1mm isotropic voxels in the SRI24 atlas space, the rigid and affine registrations were applied using a single interpolation step. All registrations and resampling were conducted using the Advanced Normalization Tools (ANTs)  34. N4 bias field correction  35  was used and skull stripping was performed with the "HD-bet" algorithm (https:// github. com/ MIC-DKFZ/ HD-BET)  36.
Deep learning segmentation models Three algorithms were selected for this study based on high performance in recent BraTS challenges  18, 22, 37  , availability of a user-friendly and reproducible implementation online, and the uniqueness of the algorithm, see Table  2. 23. This study showed that performance drops substantially if not all sequences are available. This could be solved by inserting empty (zero-filled) scans in place of missing sequences, see the first column of Fig. 1. During training, the T1w, T2w, and FLAIR were additionally set to zero with independent probabilities of 20%, in line with the estimated frequency of missing sequences in the clinical setting  23. We used the validation data to confirm convergence, the hyperparameters of all models were kept at the default values, as reported in the associated papers, or as used in the published code repositories (Table  2 ).
Experiments and statistical analyses Four separate experiments were performed. In experiment 1, DSC and HD from the internal and external test sets were analyzed separately for each model/tumor class using a paired two-tailed t-test to assess differences between each model. DSC and HD were also compared in the following experiments using independent samples (Welch's) t-tests: experiment 2-GBMs vs LGGs on the internal and external test set to assess differences in performance on the differing tumor grades; experiment 3-GBMs and LGGs from the internal vs external test sets to assess the change in performance when segmenting external hospital data not previously seen by the models; and experiment 4-GBM and LGG patients with incomplete vs complete imaging datasets in the external test set to assess the change in performance when segmenting patients with incomplete imaging datasets from external hospital data not yet seen by the models.
Discussion In this study, we compared the performance of three of the top performing BraTS challenge deep learning models for automated brain tumor segmentation in an external multi-centre hospital dataset (https:// www. pictu repro ject. nl). We extended the valuable work of the BraTS challenge by increasing the number of training cases and using a less strictly curated, and therefore more clinically-relevant, dataset  [27] [28] [29] [30] [31]. Subsequently, we tested the generalisability of the three models on an external test set comprised of data from hospitals not used in model training. Akin to the realities of clinical assessment, we further show the utility of these models when segmenting incomplete MRI datasets, due to acquisition protocols or patient-specific circumstances, sparsified training was applied to account for missing pulse-sequences  23.
Our results demonstrate that nn-Unet, when supplemented sparsified training, produces high DSC and low HD for glioma segmentations in real-world hospital data. www. nature. com/scientificreports/ nn-Unet with sparsified training may be an optimal combination for pre-surgical applications, with acceptable results in 97. 47% of patients, based on the outlier rate of 2. 53% for nn-Unet. nn-Unet yielded the fewest outliers in all categories across all models. Furthermore, it showed the smallest reductions in segmentation performance on the external test set. There were also no statistically significant changes in segmentation quality when comparing complete versus incomplete imaging datasets. In line with other recent work, this suggests not all of the MRI sequences are necessary when models are augmented using sparsified training, or similar methods  23, 46, 47.
However, the lower WT DSCs indicate a heavier reliance on a full set of MRI sequences for WT segmentation, which is plausible given the hyperintensity of oedema on FLAIR and T2w. Previous studies have also used generative adversarial networks (GAN) to synthesise missing sequences with very promising results  48  , therefore direct comparison of this approach and the sparsified trained used in the current study is encouraged. study. NvNet's underwhelming results on incomplete datasets (Table  5 ) could be due to reduced effectiveness of the auto-encoder regularization in combination with sparsified training. DeepMedic won the 2017 BraTS challenge but generated the weakest HD in the current study, especially when predicting the LGG scans. The.
Depending on the intended use case for automated glioma segmentations, having a less subjective, more consistent measurement may generate a more accurate representation of true tumor infiltration, and the associated increased (inter-rater) variability. The WHO glioma classification have been updated in 2021: WHO CNS5 has some variations by further advancing the role of molecular diagnostics in the classification of CNS tumors, but remains rooted in its established methods of histology and immunohistochemistry in tumor characterisation  49. The classification of GBM and LGG is very relevant to a model trained on combined LGG and GBM data, especially when it works on all gliomas. Furthermore, we did not target hyperparameter optimisation for the sparsified training, nor did we make specific architecture optimisations for training and testing these models on a much larger dataset.
Conclusions In this study, we have shown the feasibility of using sparsified training alongside three top-performing BraTS challenge models to produce high-quality glioma segmentations of real-world hospital data with missing sequences. When segmenting scans with incomplete MRI sequences there was no statistically significant decrease in performance. While performance was slightly reduced in an external test set, the segmentations remained within clinically acceptable ranges. nn-Unet was the most consistent performer with highest DSCs, lowest HDs, tightest IQRs, and smallest outlier rates across the vast majority of experiments.
Acknowledgements The authors would like to thank all patients whose data was used in this study. HP is a full-time employee of Deloitte. FB, SB, FP and JW are supported by the  National Institute for Health Research (NIHR)  biomedical research centre at  UCLH. FP received a Guarantors of Brain fellowship  2017-2020  and is also supported by the  Biomedical Research Centre initiative  at  University College London Hospitals (UCLH). The  PICTURE  project is sponsored by an unrestricted grant of  Stichting Hanarth fonds , " Machine learning for better neurosurgical decisions in patients with glioblastoma "; a grant for public-private partnerships ( Amsterdam UMC PPP-grant ) sponsored by the  Dutch government (Ministry of Economic Affairs)  through the  Rijksdienst voor Ondernemend Nederland (RVO)  and  Topsector Life Sciences and Health (LSH) , " Picturing predictions for patients with brain tumors "; a grant from the  Innovative Medical Devices Initiative program , project number  10-10400-96-14003 ; The  Netherlands Organisation for Scientific Research (NWO) ,  2020.
Code availability See Table  2  for links to all software availability and https:// gitlab. com/ pictu re-produ ction/ pictu re-qni-robustglioma-segme ntati on/ for code used in this study. The nn-Unet model has also been integrated in the picturennunet python package https:// gitlab. com/ pictu re-produ ction/ pictu re-nnunet-packa ge.
We used manual segmentations of preoperative imaging of 1251 gliomas (unspecified mix of GBM and LGG) from the BraTS 2021 dataset and 275 GBM and 205 LGG (median age, 63. 7 IQR [54. 3-72. 0] years; median survival, 323 [142-609] days; surgery extent: 348 resections, 83 biopsies, 49 unknown) from the PICTURE project. The PICTURE dataset was collected across 12 hospitals worldwide, all patients of at least 18 years old with a newly-diagnosed LGG, or GBM at first-time surgery between 1/1/2012 and 12/31/2013 were included. Since the PICTURE data was collected in 2012 and 2013, the classification of GBM and LGG was in line with WHO 2007 criteria. Demographics for the PICTURE dataset are documented in Appendix 1 of the supplementary material.
source Tumor type Dataset type Group Total T1w missing T2w missing FLAIR missing Train n = 1000 BraTS 1000 0 0 0 BraTS GBM + LGG n = 1251 Validation n = 63 BraTS 63 0 0 0 Test n = 188 BraTS 188 0 0 0 Train Hospital 1 52 6 0 3 n = 95 Hospital 2 43 1 1 0 Validation Hospital 1 3 0 0 0 n = 7 Hospital 2 4 0 1 0 Test Hospital 1 8 0 0 0 n = 15 Hospital 2 7 1 0 0 Hospital 3 4 3 1 2 GBM n = 275 Hospital 4 15 1 12 0 Hospital 5 1 0 0 0 Hospital 6 1 0 0 0 External test set n = 158 Hospital 7 1 0 0 0 Hospital 8 23 13 0 0 PICTURE Hospital 9 19 3 0 7 Hospital 10 8 0 2 3 Hospital 11 86 2 1 0 Hospital 2 83 78 78 0 Train n = 107 Hospital 12 14 3 2 0 Hospital 3 10 10 10 0 Validation Hospital 2 4 4 4 0 LGG n = 6 Hospital 3 2 2 2 0 n = 205 Hospital 2 17 17 17 0 Test n = 23 Hospital 12 4 3 2 1 Hospital 3 2 2 2 0 External test set Hospital 9 13 0 0 0 n = 69 Hospital 11 56 55 51 3 Total 1731 204 186 19.
nvidia. com/ catal og/ resou rces/ nvidia: clara: clara_ ai_ brain_ tumor_ pipel ine/ files? versi on=0. 7. 1-2008. 1) A multi-scale, 3D patch-based fully convolutional classification network. In DeepMedic contrast to U-net, DeepMedic does not have an up-sampling 'side'. It predicts 1 × 1x1 voxels based on a high-and low-res input of 17 × 17x17 voxel, the low-res input uses a down sampled version of the image 25, 52   (https:// github. com/ deepm edic/ deepm edic) nn-Unet ('no-new-Net') A U-net network architecture using a 2D, 3D and a cascaded U-net. Three U-net structures are trained simultaneously, and the best trained model is automatically selected Model training and testing.
" The Challenges to Becoming Competent in Reading Raw EEG Philosophically, and from a practical standpoint, what unites most participants at this event is the fundamental assumption that interpreting a qEEG requires the ability to read and interpret the raw EEG first before processing it with higher order statistics (i. e. , Fast Fourier Transforms, comparison with normative databases, and displays of head maps). Processed algorithms are averaged over all epochs in a recording, which means that important but less frequently occurring features, such as isolated epileptiform discharges, may not show up in the final analysis. Understanding how filters can alter waveform morphology could explain the appearance of multiple peaks in the power spectrum. Furthermore, it is not always possible to remove all sources of artifacts; therefore, one needs training to develop confidence in determining that the higher order statistics and brain maps are a true reflection of the raw EEG features and have not been influenced by artifacts or skewed by frequencies outside of their range, such as alpha loading into theta or beta bands, since the brain maps have fixed frequencies.
Mastering EEG Tools There are a great many analytic tools available, each with its strengths and weaknesses. A practitioner must know how to choose the correct tool for each kind of analysis. "You are only as good as your data and your ability to quantitatively process that data"  (Gunkelman, 2014). For example, the "correct" montage is the one that allows you to verify the signal source in a particular way. The Linked Ears montage is the derivation used to create databases, so this montage needs to be used when referencing a normative database. This montage can be contaminated with ear lead and temporal artifacts, however, which creates the appearance of elevated coherence. Neurologists use bipolar montages to best localize the origin of a particular waveform  (Nunez & Srinivasan, 2006).
The Next Generation of iMediSync's Digital Mental Care Platform Seung Wan Kan and Daekeun Kim provided their vision for the next 10 years of iMediSync and a summary of the company's history since its founding in 2012. Their goal was to create an integrated and user-friendly EEG system so that more health professionals could make use of EEG data in their clinical practice. iMediSync has since developed an age-and-sex-matched qEEG normative database, iSyncBrain, which is available as a paid service for processing EEG data from many standard EEG devices. The iSyncBrain processing software can rapidly and automatically artifact the data using an AI-based algorithm and then compile the results into a downloadable report. iMediSync has also developed a machine learning algorithm for the detection of cognitive impairment, along the spectrum of Alzheimer's disease disorders.
Participants had the opportunity to try their new integrated EEG helmet called iSyncWave. This helmet integrates high-impedance dry sensors, an EEG amplifier, NIR-LEDs for photobiomodulation, and a wireless communication module so that data can be recorded directly to a Samsung tablet and then uploaded to the iSyncBrain server for processing. The collection software automates the recording of 2 min of eyes open and then 2 min of eyes-closed data. While several devices are currently available on the market to deliver transcranial photobiomodulation (tPBM), the iMediSync team proposes to turn the integration of EEG and tPBM into a viable telemedicine diagnostic and personalized therapeutic device for mild cognitive decline (MCI) and dementia. iMediSync hopes to expand the scope of their AIbased EEG analysis tools and to bring their vertically integrated EEG solutions to diverse health professionals.
Toxins All Around Dr. Turner illustrated the longstanding, yet unrecognized, adverse effects of artificial environmental electromagnetic radiation (EMR) on all life, specifically human life, and the value that careful EEG review has in demonstrating external effects of environmental EMR. Exponentially increasing EMR exposure of humans worldwide is essentially unrecognized despite its pervasive, albeit invisible, presence throughout the world. Clues arise in careful review of the EEG data being collected every day by clinicians in our field. This EMR exposure consists of increasing use of wireless networks, cell phones and towers, etc. and is demonstrated from a multitude of publications over the past 60 years. Adverse effects are also seen on genetic and reproductive health and especially on the developing nervous system.
)  for "the R in ISNR," a collaborative citizen-science approach to sharing data from across multiple practices and agencies to create an open-source database in which participants can upload their respective data sets and do analysis via queries of the online database, possibly housed at the university where he teaches. Swatzyna and Jones facilitated discussion on the various nuances of research designs, IRB requirements, and eventually establishing standardized assessments and protocols related to specific syndromes to facilitate robust statistical analysis.
The d-Matrix implements Cohen's d obtained from before-after comparisons visualized in a channel-by-frequency matrix. Starting with the ribbon analysis, Michael will demonstrate how sorting by frequencies gives the clinician a radically different temporal visualization into fluctuations of the alpha band and their relationship to clients' symptomatology. Next, he will compare pre-post EEG data using WinEEG software and then use the same data to demonstrate the d-Matrix (within an EEGLAB environment). Neurofeedback practitioners seek to alter specific frequency ranges by channel location; thus, significance (p value) is the least relevant, while effect size is the most pertinent to the client. The d-Matrix provides a beautiful spatial overview of cortical power levels, giving a unique window into the hitherto unseen effects engendered by neurofeedback.
EEG during Seung Wan Kan and Daekeun Kim's talk, "Vision of New Decade: Building a digital mental care platform with qEEG brain mapping and neuromodulation. " For those new to the field, Dave Siever reviewed the power of audio-visual entrainment during his talk, "Physiology and Clinical Applications of Audio-Visual Entrainment Technology" (Siever & Collura, 2017). significance (p-values) may not be relevant given the Early machine learning applications, such as neural intended application of neurofeedback. networks, were motivated by an idealized model of how collections of neurons worked together to solve As a more appropriate method for comparison of problems. Consequently, language developed such either pre-post or individual-database EEG time that terms like neurons, synaptic weights, channels, series data, Villanueva proposed the application of and epochs hold a different meaning for data Cohen's D, or the standardized mean difference.
for identifying yet-unknown features or correlates of Technological Advancements EEG signals, but also present unique challenges. Michael Villanueva, a clinical psychologist and One such challenge is the time-consuming step of neurofeedback practitioner brought the concepts Three talks introduced new technologies in the form generating labeled data for supervised algorithms by introduced by Gast into a practical clinical of algorithms or new hardware. Peter Gast and humans with expertise in the domain of relevant framework. Most EEG analysis software relies on Michael Villanueva gave a joint presentation entitled knowledge. A second challenge is to develop spectral averages to create comparisons with "Art of Dimension" where they demonstrated strategies for handling imbalanced datasets, which normative databases.
But given the transient nature motivations for alternative representations of data for frequently occur in clinical data where the number of of EEG, as demonstrated in the audio analogy, we improved insight and analysis. Rogene Eichler West normal examples to learn from outnumber the may grow dissatisfied with the lack of any temporal clinically significant ones. A third challenge is to resolution in such comparisons. Furthermore, develop a feature space to differentiate salient although p-values associated with differences in classes. spectral power density between an individual and the database may help identify areas of interest, Those who have received training most certainly would not have had exposure to the application of EEG to medication selection, as this is a relatively new specialty.
Using this extensive dataset, we quantified (1) environmental factors influencing amino acid fitness, (2) thermodynamic couplings (including unexpected interactions) between protein sites, and (3) the global divergence between evolutionary amino acid usage and protein folding stability. We also examined how our approach could identify stability determinants in designed proteins and evaluate design methods. The cDNA display proteolysis method is fast, accurate and uniquely scalable, and promises to reveal the quantitative rules for how amino acid sequences encode folding stability.
New high-throughput experiments have the potential to transform our understanding of stability by quantifying the effects of mutations across a vast number of protein contexts, revealing new biophysical insights and empowering modern machine learning methods. Here we introduce cDNA display proteolysis, a powerful highthroughput stability assay, and use it to produce a large dataset of 776,298 folding stability measurements. This method combines the strengths of cell-free molecular biology and next-generation sequencing and requires no on-site equipment larger than a quantitative PCR (qPCR) instrument. Assaying one library (up to 900,000 sequences in our experiments) requires one week and reagents costing about US$2,000, excluding the cost of DNA synthesis and sequencing. Compared with mass spectrometry-based high-throughput stability assays  16, 17  , cDNA display proteolysis achieves a 100-fold larger scale and can easily be applied to study mutational libraries that pose difficulties for proteomics.