A probabilistic approach to melodic similarity

Melodic similarity is an important research topic in music information retrieval. The representation of symbolic music by means of trees has proven to be suitable in melodic similarity computation, because they are able to code rhythm in their structure leaving only pitch representations as a degree of freedom for coding. In order to compare trees, different edit distances have been previously used. In this paper, stochastic k-testable tree-models, formerly used in other domains like structured document compression or natural language processing, have been used for computing a similarity measure between melody trees as a probability and their performance has been compared to a classical tree edit distance.This work is supported by the Spanish Ministry projects: DPI2006-15542-C04, TIN2006-14932-C02, both partially supported by EU ERDF, the Consolider Ingenio 2010 research programme (project MIPRCV, CSD2007-00018) and the Pascal Network of Excellence

Genetic Counseling in Renal Masses

All urologists have faced patients suffering a renal cancer asking for the occurrence of the disease in their offspring and very often the answer to this question has not been well founded from the scientific point of view, and only in few cases a familial segregation tree is performed. The grate shift seen in the detection of small renal masses and renal cancer in the last decades will prompt us to know the indications for familial studies, which and when are necessary, and probably to refer those patients with a suspected familial syndrome to specialized oncological centers where the appropriate molecular and familial studies could be done. Use of molecular genetic testing for early identification of at-risk family members improves diagnostic certainty and would reduce costly screening procedures in at-risk members who have not inherited disease-causing mutations. This review will focus on the molecular bases of familial syndromes associated with small renal masses and the indications of familial studies in at-risk family members

Molecular characterization and clinical impact of TMPRSS2-ERG rearrangement on prostate cancer: comparison between FISH and RT-PCR

Prostate cancer (PCa) is a very heterogeneous disease, and there are constraints in its current diagnosis. Serum PSA levels, digital rectal examination (DRE), and histopathologic analysis often drive to overdiagnosis and overtreatment. Since 2005, the presence of the genetic rearrangement between transmembrane-serine protease gene (TMPRSS2) and the erythroblast transformation-specific (ETS)member ERG (v-ets erythroblastosis virus E26 oncogene homolog avian) has been demonstrated in almost half of PCa cases. Both FISH and RT-PCR are useful tools for detecting these rearrangements, but very few comparatives between both techniques have been published. In this study, we included FFPE tumors from 294 PCa patients treated with radical prostatectomy with more than 5 years of followup.We constructed a total of 20 tissue microarrays in order to perform break-apart and tricolor probe FISH approaches that were compared with RT-PCR, showing a concordance of 80.6% ( P < 0.001). The presence of TMPRSS2-ERG rearrangement was observed in 56.6% of cases. No association between TMPRSS2-ERG status and clinicopathological parameters nor biochemical progression and clinical progression free survival was found. In conclusion, this study demonstrates that both FISH and RT-PCR are useful tools in the assessment of the TMPRSS2-ERG fusion gene status in PCa patients and that this genetic feature per se lacks prognostic value.This study has been funded by the Grants FIS PI06/01619 and PI10/01206 from the Instituto de Salud Carlos III, Madrid, ACOMP 12/029 from the Generalitat Valenciana, Valencia, and Astra Zeneca, Spain

Robust Resolution-Enhanced Prostate Segmentation in Magnetic Resonance and Ultrasound Images through Convolutional Neural Networks

[EN] Prostate segmentations are required for an ever-increasing number of medical applications, such as image-based lesion detection, fusion-guided biopsy and focal therapies. However, obtaining accurate segmentations is laborious, requires expertise and, even then, the inter-observer variability remains high. In this paper, a robust, accurate and generalizable model for Magnetic Resonance (MR) and three-dimensional (3D) Ultrasound (US) prostate image segmentation is proposed. It uses a densenet-resnet-based Convolutional Neural Network (CNN) combined with techniques such as deep supervision, checkpoint ensembling and Neural Resolution Enhancement. The MR prostate segmentation model was trained with five challenging and heterogeneous MR prostate datasets (and two US datasets), with segmentations from many different experts with varying segmentation criteria. The model achieves a consistently strong performance in all datasets independently (mean Dice Similarity Coefficient -DSC- above 0.91 for all datasets except for one), outperforming the inter-expert variability significantly in MR (mean DSC of 0.9099 vs. 0.8794). When evaluated on the publicly available Promise12 challenge dataset, it attains a similar performance to the best entries. In summary, the model has the potential of having a significant impact on current prostate procedures, undercutting, and even eliminating, the need of manual segmentations through improvements in terms of robustness, generalizability and output resolutionThis work has been partially supported by a doctoral grant of the Spanish Ministry of Innovation and Science, with reference FPU17/01993Pellicer-Valero, OJ.; González-Pérez, V.; Casanova Ramón-Borja, JL.; Martín García, I.; Barrios Benito, M.; Pelechano Gómez, P.; Rubio-Briones, J.... (2021). 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Deeply-supervised CNN for prostate segmentation. 2017 International Joint Conference on Neural Networks (IJCNN). doi:10.1109/ijcnn.2017.7965852To, M. N. N., Vu, D. Q., Turkbey, B., Choyke, P. L., & Kwak, J. T. (2018). Deep dense multi-path neural network for prostate segmentation in magnetic resonance imaging. International Journal of Computer Assisted Radiology and Surgery, 13(11), 1687-1696. doi:10.1007/s11548-018-1841-4Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017). Densely Connected Convolutional Networks. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). doi:10.1109/cvpr.2017.243Zhu, Y., Wei, R., Gao, G., Ding, L., Zhang, X., Wang, X., & Zhang, J. (2018). Fully automatic segmentation on prostate MR images based on cascaded fully convolution network. Journal of Magnetic Resonance Imaging, 49(4), 1149-1156. doi:10.1002/jmri.26337Wang, Y., Ni, D., Dou, H., Hu, X., Zhu, L., Yang, X., … Wang, T. (2019). Deep Attentive Features for Prostate Segmentation in 3D Transrectal Ultrasound. IEEE Transactions on Medical Imaging, 38(12), 2768-2778. doi:10.1109/tmi.2019.2913184Lemaître, G., Martí, R., Freixenet, J., Vilanova, J. C., Walker, P. M., & Meriaudeau, F. (2015). Computer-Aided Detection and diagnosis for prostate cancer based on mono and multi-parametric MRI: A review. Computers in Biology and Medicine, 60, 8-31. doi:10.1016/j.compbiomed.2015.02.009Litjens, G., Toth, R., van de Ven, W., Hoeks, C., Kerkstra, S., van Ginneken, B., … Madabhushi, A. (2014). Evaluation of prostate segmentation algorithms for MRI: The PROMISE12 challenge. Medical Image Analysis, 18(2), 359-373. doi:10.1016/j.media.2013.12.002Zhu, Q., Du, B., & Yan, P. (2020). Boundary-Weighted Domain Adaptive Neural Network for Prostate MR Image Segmentation. IEEE Transactions on Medical Imaging, 39(3), 753-763. doi:10.1109/tmi.2019.2935018He, K., Zhang, X., Ren, S., & Sun, J. (2015). Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. 2015 IEEE International Conference on Computer Vision (ICCV). doi:10.1109/iccv.2015.123Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345-1359. doi:10.1109/tkde.2009.191Smith, L. N. (2017). Cyclical Learning Rates for Training Neural Networks. 2017 IEEE Winter Conference on Applications of Computer Vision (WACV). doi:10.1109/wacv.2017.58Abraham, N., & Khan, N. M. (2019). A Novel Focal Tversky Loss Function With Improved Attention U-Net for Lesion Segmentation. 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019). doi:10.1109/isbi.2019.8759329Lei, Y., Tian, S., He, X., Wang, T., Wang, B., Patel, P., … Yang, X. (2019). Ultrasound prostate segmentation based on multidirectional deeply supervised V‐Net. Medical Physics, 46(7), 3194-3206. doi:10.1002/mp.13577Orlando, N., Gillies, D. 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Types of pathological progression within Spanish Register on Active Surveillance and their relevance in radical prostatectomy specimens

Resumen presentado en: 11th European Multidisciplinary Congress on Urological Cancers (EMUC19) In active surveillance (AS) management, there are controversies for the different pathological progression (PP) criteria from prostate cancer (PCa) GGI (Gleason 3+3). Our objective is to know if PP just in tumoral volumen is irrelevant vs PP in grade..

Query parsing using probabilistic tree grammars

Appearing in Proceedings of the 29th International Conference on Machine Learning, Edinburgh, Scotland, UK, 2012.The tree representation, using rhythm for defining the tree structure and pitch information for node labeling has proven to be effective in melodic similarity computation. In this paper we propose a solution representing melodies by tree grammars. For that, we infer a probabilistic context-free grammars for the melodies in a database, using their tree coding (with duration and pitch) and classify queries represented as a string of pitches. We aim to assess their ability to identify a noisy snippet query among a set of songs stored in symbolic format

Optimization of the different therapeutic strategies in muscle invasive bladder cancer using biomarkers

3si: Predicting response to definitive treatmentsis a fascinating challenge which develops throughthe evolution of a panel of convincing molecularbiomarkers capable of adding in clinical decissionsdespite interpatient and intratumoral heterogenicity.Muscle-invasive bladder cancer (MIBC) can be locallytreated either with radical cystectomy (RC) with or withoutneoadyuvant chemotherapy or bladder preservationapproaches such as trimodal therapy (TMT) includingmaximal transurethral resection of bladder tumor(TURBt) followed by external beam radiotherapy withconcurrent systemic radio-sensitizing chemotherapy.Conventional or novel/targeted systemic agents areessential parts of perioperative multidisciplinary managementconsidering both neoadjuvant and adjuvantsetting. Advances in molecular biology such as next generation sequencing and whole genome or transcriptomicanalysis, provided novel insights to achieve a fullunderstanding of the biology behind MIBC helping toidentify emerging predictive signatures. Although severalprogresses have been made, real-world applicationof molecular biomarkers in MIBC scenario is hinderedby lack of standardization, and low reproducibility. Inthis review we aim to present the emerging role of novelmolecular biomarkers in predicting response to localtreatments and systemic agents in MIBC.noneopenClaps, Francesco; Mir, Carmen; Rubio-Briones, JoséClaps, Francesco; Mir, Carmen; Rubio-Briones, Jos