dataesr/scientific-paragraphs-categorization huggingface dataset:
773, 0. 902, and 0. 815 on the ET, WT, and TC. For visual comparison, the individual results of the 5-fold cross-validation are presented as box plots in Figure  6 , along with p-values from Student's t-test performed to compare the different models. Regarding the Dice metric, no significant difference was observed between 3D and 2. 5D models in the WT and TC regions, but a significant difference was observed in the ET region (Figure  6A ). Incorporating the 2. 5D Multi-View Inception Multi-Input module to the AU-Net improved the result in this region compared with 3D input. The best performance was obtained in all regions with the AE AU-Net, which overperformed 3D and 2. 5D AU-Nets with a statistically significant difference (p < 0. 008). Regarding the Hausdorff distance, it can be observed that the variability of the results was higher compared with the Dice metric (Figure  6B ).
No statistically significant difference was found between 3D and 2. 5D models in the TC and ET regions. In WT, 2. 5D AU-Net obtained  This study has extended our previous work  (52) , which corresponds to a single model from 2. 5D AU-Net architecture. Table  2  shows the results of this single model in the validation and test dataset of the BraTS 2019. We can observe that the 2. 5D model achieved a similar overall performance in both validation and test datasets, illustrating that the method did not overfit the validation dataset and generalized well to the test dataset. We have improved the model and the segmentation performance in the validation dataset by implementing the proposed AE AU-Net as an ensemble of seven asymmetric models and using additional post-processing techniques. We also implemented our proposed AE AU-Net framework in BraTS 2018 for comparison purposes.
We got better performance in terms of Hausdorff distance than cascade networks in the ET and in the WT regions and similar performance with the competition winners. Table  4  shows a comparative performance between the proposed AE AU-Net model and the two best-performing networks presented to BraTS 2019  (15, 22). It can be observed in Table  4  that our method performs closely, although a little less, to the second-ranked submission in the validation dataset and that our model performs better than the secondplace model in the enhancing tumor region. The difference in performance in both BraTS datasets, 2018 and 2019, could be explained by the fact that the second performer  (19)  and  (15)  did not only use the BraTS dataset to train their model but also an additional Decathlon dataset (54).
Single-Input Modalities Organizing a homogeneous database of MRI images that includes the same modalities for all glioblastoma patients is a challenging task to achieve. In most health centers, the imaging modalities available for each patient may vary, without considering that the image acquisition parameters also change in each protocol. The images in the BraTS database contain the same four imaging modalities for each patient, but in many other databases, they may have only three or even fewer. To evaluate the impact on our model when being trained with only one imaging modality, we trained a single input version of our proposed model with only one of the four modalities separately and also with a combination of two modalities and with all the modalities stacked as a multi-channel input.
DISCUSSION Most of the proposed methods for medical image segmentation involve the use of architectures that stack all modalities as a multi-channel single input. In previous studies, using multi-input entrance for independent modalities has improved segmenting multiple sclerosis lesions (55) using MRI images, reporting an increase in Dice score metric of around 3%. In the context of the BraTS challenge, this is the first time that the use of multiinput for different image modalities has been evaluated. We designed a multi-input module that aims at extracting features from independent modalities before downsampling the images since, during this process, the specific details from the more informative modalities can be missed by mixing with other modalities. On the contrary, the multi-input approach allows the extraction of more informative modality-specific features for better segmentation.
The results that we obtained in the BraTS dataset confirmed our hypothesis. The 2. 5D model with multiinput generates more accurate segmentation than the identical 3D model, where all modalities are stacked into a single input. Although the difference in overall performance is slight on average, it was significant in some of the tumor regions, higher by about 2% in the Dice score. This is a non-negligible difference considering that it is difficult to measure improvements due to changes in model architecture  (56). However, we found that both the 2. 5D the 3D models predicted relevant information that could be captured in the AE AU-Net that benefits from both. In this study, we found that T1 with gadolinium is the most valuable modality to segment enhancing-tumor regions; the other modalities contribute slightly to the detection of these small regions and when using only the other three modalities independently, our model is not capable to identify this region (see Table  5 ).
Since all the models in the literature that are trained on BraTS datasets use the same four modalities, we believe that their performance is strongly dependent on their availability, in a similar way than our model. In numerous studies, many strategies have been implemented to combine features of different scales and different views in the image. In this study, we have shown for the first time the use of multi-inputs to combine multi-scale extracted features coming from three orthogonal views of four independent image modalities. While an approach was proposed to combine multiscale and multi-view features simultaneously  (51) , it required training separate models for each view. In the proposed 2. 5D approach, the three branches in the input module allow the training of a unique 2. 5D model.
is convenient in terms of training time since it is unnecessary to train several models with different views or scales, and it can be optimized as a unique process. Besides, using 2. 5D convolutions reduces the computational cost. We also compared our implementation with a Multi-View Fusion strategy. Results in Table  6  show the independent assessments of our model trained in different views and the combination of them through an averaging ensemble model. The ensemble improves the accuracy of the segmentation but is inferior to the proposed AE AU-Net approach based on both 3D and 2. 5D modules (results in Table  4 ). When examining the influence of differently oriented 2. 5D convolutions in axial, coronal, and sagittal views, results in Table  6  illustrated that in our experiments, the models trained in axial view provide more accurate segmentation in enhancing tumor and tumor core regions.
In the 2018 BraTS challenge, the first-place competitor (21) proposed a variational autoencoder branch for an encoder/decoder model to reconstruct the input image simultaneously with segmentation. The second place used an ensemble of 10 3D U-Net with minor modification models and implemented a co-training strategy using additional labeled images. We also compared our results with the cascade network in addition to test time augmentation (TTA) and a conditional random field for post-processing, implemented by  (51). TTA is a data augmentation technique, such as spatial transformations, but applied at test time to obtain N different segmentation results combined to get the final prediction. In terms of optimization in the 2019 BraTS challenge, the first and second place used warming up learning and multitask learning strategies.
The authors obtained similar performances using their single best model compared with fusing 12 models in an ensemble. Tables  3, 4  show that we got competitive results with top performance methods in the 2018 and 2019 BraTS databases, while training our models with fewer images than Isensee et al. (19)  and Zhao et al. (15). We used less GPU memory capacity (12 GB) than Myronenko  (21) , who trained his models on a 32 GB GPU. We obtained similar performance than cascade networks with better performance in the complete and enhancing tumor regions and with the advantage that our method segments all regions in one step. The cascade networks do it in three stages. First, the whole tumor is segmented. The tumor core is obtained using the previous segmentation to get the segmentation of the smallest region; then, the inference time is also lower in our implementation.
In addition, since they used a Multi-View Fusion strategy, they need to train three networks, one for each of three different planes, to make an ensemble of the predictions; in total, nine models should be trained, three for each region. Our implementation requires training seven models. Our qualitative results comparing top-performing methods with our proposed framework suggest that minor differences in Dice values will not always represent significant qualitative differences that could impact clinical practice. Comparing the results obtained with the 2. 5D AU-Net model in the validation and the test dataset (see Table  2 ), we are confident that the proposed model generalizes well since similar general performances were obtained. Results obtained in the test set presented an increase in the values obtained in the ET region and a decrease in the WT region.
This behavior is also observed in most participating models, making it possible that the dataset test images were easier to segment in the enhancing tumor region and more difficult in the whole tumor region. Even if we did not observe overfitting behavior, we could improve the performance of our model by adding more complex data augmentation methods, such as elastic and pixel-wise transformations, since some of the top-performing methods in past editions of the BraTS challenge implemented them. However, this was not proposed as there is no clear evidence of the impact of performing more data augmentation  (15). Outside the context of the BraTS challenge, there are some works on brain tumor segmentation. Most of them use hand-crafted features combined with machine learning techniques  (45, 58, 59 ).
Soltaninejad et al. (59) used the diffusion tensor imaging (DTI) additionally to the T1, T2, and FLAIR sequences, to improve their method, which combines super voxels and random forest models. While the reported results are comparatively lower than those reported on BraTS challenges, with a Dice value of 0. 84 for the whole tumor, they propose innovative modifications that could improve the transferability of trained models. Deep learning models like the ones trained in BraTS database can be adapted to different types of images, as illustrated by the results of another grand challenge, the Decathlon (54). In this competition, the models must be able to segment a great variety of regions of six different organs, among them the brain, the heart, and the liver, and adapt to other imaging modalities such as CT and MRI.
CONCLUSIONS We have proposed a novel end-to-end FCN architecture for pre-operative MRI tumor segmentation. We have assessed, for the first time, the use of independent input entrances into an asymmetric U-Net architecture for brain tumor segmentation. Our experimentations illustrated the benefit of the proposed 2. 5D Multi-View Inception and Multi-Input module that mixes different views of the 3D images using 2. 5D convolutions and the benefit of using it complementarily with a more classical 3D concatenated input in an ensemble of models. The proposed AE AU-Net segmentation aims to balance combining multiple views and different receptive fields and to maintain memory consumption low. We obtained dice scores of 0. 902 ± 0. 066, 0. 815 ± 0. 161, and 0. 773 ± 0. 257 for the complete, core, and enhancing tumor regions, with an overall dice of 0.
83 in the BraTS 2019 validation dataset. We also applied our method in BraTS 2018 database with corresponding Dice score values of 0. 908 ± 0. 055, 0. 838 ± 0. 150, and 0. 800 ± 0. 230, respectively. We obtained comparative results with top-performing methods without using any additional training data and requiring less memory to train than the first place in both competitions. In addition, our approach to estimate uncertainties was comparatively superior to other six methods reported in the literature. Finally, the study of voxel-wise and structure-wise uncertainties, and their relationship with the segmentation errors, give perspectives to improve segmentation accuracy further.
Hepatitis B virus (HBV) and human papillomavirus (HPV) infection are known risk factors for developing several cancers. However, the effect of viral genotype and co-infection in actually driving oncogenesis remains unclear. We have developed and deployed a new scalable, high throughput tool (ViralMine) for sensitive and precise oncoviral genotype deconvolution using tumor RNA sequencing data from 537 virally infected liver, cervical, and head and neck tumors. We provide the first comprehensive integrative landscape of tumor-viral gene expression, viral antigen immunogenicity, patient survival, and mutational profiling organized by tumor onco-viral genotype. We find that HBV and HPV genotype, and surprisingly high rates of multi-genotype co-infection, serve as significant predictors of patient survival, tumor immune responsiveness, and APOBEC activity modulation.
ViralMine: in-silico cross-cohort genotyping validation and co-infection discovery In order to validate the key genotyping capability of ViralMine ( Figure  1A , see methods ) , we obtained tumor RNA-seq data for the "core set" of cervical cancers from the TCGA with previous HPV infection information (n=178, 169 HPV+) and a group of 50 Chinese HCC patients screened for HBV infection (GSE65485, n=50, 44 HBV+) ( Figure  1B  )  ( 15 , 17 )  , for a total of 213 virally infected samples. We inferred viral genotypes in both cohorts against the existing genotype information, obtaining perfect sensitivity in both datasets, and specificities of 0. 97 and 0. 95 in the HPV+ cervical cancers and HBV associated HCC tumors, respectively ( Supplemental Figure  1  ). These results demonstrate that the overall performance of ViralMine in the cervical TCGA (CESC) core set cohort nearly perfectly matched consensus calls made using a combination of different assays, including MassArray, BioBloom Tools and PathSeq RNA-Sequencing inference techniques  ( 15 ).
We also observed robust average performance using read downsampling with patient randomization, indicating even relatively low viral expression is sufficient for accurate genotyping with ViralMine ( Supplemental Figure  9  ), though we do note that immunotherapy treatments which stimulate viral clearance may have an adverse effect on viral sequence recovery ( Supplemental Figure  10  ). Applying ViralMine to large-scale tumor expression datasets, especially those with missing or incomplete viral information, we exhaustively screened liver cancer patients from the TCGA (LIHC, n=334) for HBV infection and found 115 positive patients. This included 44 patients previously reported HBV+  ( 18 )  , with the majority (85/115, 74%) infected with HBV genotype C ( Figure  2A, B  ). We also screened a different dataset of 21 HCC patients for combined HBV integration and genotyping analysis ( Figure  1B ; Figure  2B  ) (GSE94660, n=21), and found 11 patients positive for HBV genotype C, 9 HBV for genotype B, and 1 patient HBV-  ( 19 ).
HPV gene expression is more closely correlated with genotype than to cancer type In order to examine the effect of cancer cell-type in viral expression from the genotype-specific perspective, we compared cervical and head and neck cancer at both the total HPV expression level and across HPV genes by leveraging high-quality viral whole-transcript recovery from ViralMine ( Figure  7  ). While there was no statistically significant difference in total HPV expression between HPV clades across cervical and head and neck tumors ( Figure  7 , panel 9 ), we found normalized HPV gene expression for cervical tumors with HPV infections in the a7 (n=73) clade is lower across E1, E2, E5, E7 , and L1 genes compared to tumors with HPV a9 (n=205) infections ( Figure  7 , panels 1, 2, 4, 6, 7; p max < 0. 05).
Discussion Using multiple publically available independent datasets across three distinct oncovirally-associated cancer types, we have unraveled key correlates of HPV and HBV genotype and co-infection in oncogenic viral and host expression signaling, patient survival, tumor mutational signatures, and immune response. Apart from demonstrating that large-scale, high-fidelity in situ oncoviral profiling is feasible, we build upon previous studies ( 24 , 27 -30 ) demonstrating associations with cancer risk by showing that genotype and co-infection modalities can also affect facets of tumor evolution. Revisiting known associations of HBV genotype C with increased risk of HCC  ( 30 , 31 )  , we find that HBV genotype is also a significant predictor of overall HCC patient survival in the LIHC, with HBV genotype C driving poor survival.
In conclusion, we present a detailed view of key cancer types through the lens of their oncoviral cofactors by applying a scalable, highly accurate method of DNA viral gene expression, genotyping, and co-infection typing on in situ tumor RNA-seq data. By easily applying our method to large publically available datasets, we demonstrate that our initial analyses are sufficiently powered to reveal novel genotype, co-infection, and viral gene expression specific signals, which we hope can be built upon to help inform the next generation of clinical guidelines in assessing and treating not only HPV and HBV related cancers but other DNA virus (e. g. Epstein-Barr and Burkitt's lymphoma) related tumors.
RNA-Seq analysis RNA-Seq, MAF files, and associated clinical data for the TCGA patients were retrieved using GDACfirehose ( firehose_get , v0. 4. 13), and RNA-Seq fastq and clinical data files from GEO studies GSE94660 and GSE65485 were obtained using SRA toolkit (v2. 8. 0). Fastq sequences were aligned to Hg38 with STAR (v2. 5. 1b) in two-pass mode  ( 39 )  , and gene counts for Gencode v23 ( www. gencodegenes. org ) gene annotated counts were generated using featureCounts. Mapped reads were used to allelotype (MHC class-I loci) each patient  ( 40 )  and estimate the putative TIL burden per patient by profiling TCR and BCR sequences with MiXCR (  41 ) , normalizing by patient library size.
Viral genotyping with ViralMine HBV and HPV genotype calls were produced using ViralMine ( https://github. com/LosicLab/ViralMine ). Briefly, raw RNA sequencing reads that did not map to the GRCh38 reference genome were assembled into contigs using Trinity (using -no_run_chrysalis -no_run_butterfly flags, which only invokes the Inchworm step) to perform greedy kmer-25 contig assembly  ( 42 ). Resulting contigs at least 100 bp long and supported by at least 20 reads were retained and CD-Hit was used to consolidate highly similar contig sequences (e-values < 1e-10)  ( 43 , 44 ). Contigs were then aligned with BLAST ( -max_target_seqs 5 , -evalue 1e-6 ) against HBVdb ( 45 ) reference genomes (genotypes A-F, and G) for HCC patients, and against RefSeq HPV reference genomes (genotypes HPV16,  18, 31, 33, 35, 45, 51, 52, 56, 58, 59 , 68, 73, and 82) for cervical and head and neck cancers, effectively identifying the viral positive tumors.
Differential expression and gene set enrichment analysis Human genes from STAR featureCounts (see RNA-Seq analysis above) were filtered to genes with 15 or more reads across all samples in a given cohort, followed by library size normalization and expression model fitting using a negative binomial distribution with DESeq2 ( 46 ). Differentially expressed genes (DEGs) were identified using an absolute log-fold change < 2 and a FDR < 0. 01. Design and contrast matrices were constructed based on sample comparison groups, and gene set enrichment of resulting DEGs was performed using 'camera' from limma analysis package for R  ( 47 ). The top 15 enriched pathways for each analysis, by p-value, were retained used for barcode plotting ( Figures  2, 3 ; Supplemental Table  1  ).
Integration site analysis Viral reference genomes (HBV genotypes A, B, and C, and all tested HPV genotypes) from HBVdb ( 45 ) and RefSeq were included in a modified hg38 reference genome GTF as new chromosomes. Patient RNA sequences were then aligned to the respective references with STAR (v2. 5. 1b) in two-pass mode, and fusion junctions generated (flags --chimSegmentMin 20 --chimJunctionOverhangMin 20 --chimScoreSeparation 6 ). Large gapped chimeric alignments were filtered out by CIGAR string. For the HCC cohorts, insertions were called with a minimum of 3 supporting reads using STARchip ( https://github. com/LosicLab/starchip ) ( 48 ) (STARchip detection parameters splitReads 3 , uniqueReads 2 , spancutoff 2 , and consensus TRUE ). Due to a large perceived number of false negative integration calls in the LIHC cohort, the same fusion parameters were used without the consensus flag ( consensus FALSE ).
Survival modeling Cox proportional hazard models were constructed using the 'cph' function from the rms package (  49 ), with model feature coefficients calculated using glmnet (alpha = 0. 5). Samples with missing survival times, viral genotypes, and tumor clinical stage were removed, while other missing feature data was imputed. Relative predictive power of survival models against others in the same cohort was assessed using the 'pec' R package, computing Integrated Brier scores with. 632+ bootstrap resampling across 200 iterations ( B =200). The weights of the Brier scores correspond to the probability of not being censored, and the Kaplan-Meier estimator was used for censoring times in each resampling. Model prediction error curves were compared to the reference and other model curves for Brier scores at times comprising approximately 80% of total patient events.
Tumor mutational signature profiling Mutations and RNA-Seq data for tumors from each TCGA cohort were stratified by viral genotype and de novo mutational signatures generated using sigProfiler ( https://github. com/AlexandrovLab )  ( 25 ). Comparisons of the single-base substitution (SBS) de novo signatures with established SBS signatures from COSMIC (v3, https://cancer. sanger. ac. uk/cosmic/signatures/SBS/ ) identified group level enrichments for each cancer type. Percent SBS enrichments in each established signature were reported for each viral subgroup using the average enrichment across patient tumors in each group. Groups were considered enriched in a given SBS signature given one de novo profile contained a >0. 5% match with the established SBS signature and the signature was present at an average of >1% across patients in the group.
Tumor neoantigen prediction To predict patient neoantigen burden and immunogenicity, we used Topiary ( 50 ) ( https://github. com/openvax/topiary ) to call mutation-derived cancer T-cell neoepitopes from somatic variants (from MAF files), tumor RNA-seq data, and patient MHC class-I HLA type. This tool matches mutations with gene annotations, filters out non-protein coding changes, and finally creates a window around amino acid changes, which is then fed into netMHCpan for each patient HLA allele across tiles of 9-12 amino-acids in length. Given that HLA-I processes neoantigens by degradation to non-conformational 8-11 amino-acid residues, we excluded neoepitopes with mutations obscured to T-cells within HLA-I binding pockets. In the case of frameshift mutations, in principle this window starts from the mutation minus the length of the peptide up to the first stop codon.