J
J. Miguel Sanches
Researcher at Instituto Superior Técnico
Publications - 41
Citations - 690
J. Miguel Sanches is an academic researcher from Instituto Superior Técnico. The author has contributed to research in topics: Multiplicative noise & Asymptomatic. The author has an hindex of 11, co-authored 41 publications receiving 479 citations. Previous affiliations of J. Miguel Sanches include University of Lisbon & Technical University of Lisbon.
Papers
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Journal ArticleDOI
Symtosis: A liver ultrasound tissue characterization and risk stratification in optimized deep learning paradigm.
Mainak Biswas,Venkatanareshbabu Kuppili,Damodar Reddy Edla,Harman S. Suri,Luca Saba,Rui Tato Marinhoe,J. Miguel Sanches,Jasjit S. Suri +7 more
TL;DR: A Deep Learning (DL)-based paradigm that computes nearly seven million weights per image when passed through a 22 layered neural network during the cross-validation (training and testing) paradigm shows a superior performance for liver detection and risk stratification compared to conventional machine learning systems: SVM and ELM.
Journal ArticleDOI
Extreme Learning Machine Framework for Risk Stratification of Fatty Liver Disease Using Ultrasound Tissue Characterization
Venkatanareshbabu Kuppili,Mainak Biswas,Aswini Sreekumar,Harman S. Suri,Luca Saba,Damodar Reddy Edla,Rui Tato Marinhoe,J. Miguel Sanches,Jasjit S. Suri +8 more
TL;DR: This study proposes a reliable and fast Extreme Learning Machine (ELM)-based tissue characterization system (a class of Symtosis) for risk stratification of ultrasound liver images using ELM to train single layer feed forward neural network (SLFFNN).
Journal ArticleDOI
COVID-19 pathways for brain and heart injury in comorbidity patients: A role of medical imaging and artificial intelligence-based COVID severity classification: A review.
Jasjit S. Suri,Anudeep Puvvula,Mainak Biswas,Misha Majhail,Luca Saba,Gavino Faa,Inder M. Singh,Ronald Oberleitner,Monika Turk,Paramjit S. Chadha,Amer M. Johri,J. Miguel Sanches,Narendra N. Khanna,Klaudija Višković,Sophie Mavrogeni,John R Laird,Gyan Pareek,Martin Miner,David W. Sobel,Antonella Balestrieri,Petros P. Sfikakis,George Tsoulfas,Athanasios Protogerou,Durga Prasanna Misra,Vikas Agarwal,George D Kitas,Puneet Ahluwalia,Raghu Kolluri,Jagjit S Teji,Mustafa Al Maini,Ann Agbakoba,Surinder Dhanjil,Meyypan Sockalingam,Ajit Saxena,Andrew Nicolaides,Aditya Sharma,Vijay Rathore,J N A Ajuluchukwu,Mostafa Fatemi,Azra Alizad,Vijay Viswanathan,Pudukode R. Krishnan,Subbaram Naidu +42 more
TL;DR: The role of image-based AI is considered, which can be used to characterize the tissues of a COVID-19 patient and classify the severity of their infection, which is more important than ever as the pandemic surges and countries worldwide grapple with limited medical resources for detection and diagnosis.
Journal ArticleDOI
A narrative review on characterization of acute respiratory distress syndrome in COVID-19-infected lungs using artificial intelligence.
Jasjit S. Suri,Sushant Agarwal,Suneet K. Gupta,Anudeep Puvvula,Mainak Biswas,Luca Saba,Arindam Bit,Gopal S. Tandel,Mohit Agarwal,Anubhav Patrick,Gavino Faa,Inder M. Singh,Ronald Oberleitner,Monika Turk,Paramjit S. Chadha,Amer M. Johri,J. Miguel Sanches,Narendra N. Khanna,Klaudija Višković,Sophie Mavrogeni,John R. Laird,Gyan Pareek,Martin Miner,David W. Sobel,Antonella Balestrieri,Petros P. Sfikakis,George Tsoulfas,Athanasios Protogerou,Durga Prasanna Misra,Vikas Agarwal,George D. Kitas,Puneet Ahluwalia,Jagjit S Teji,Mustafa Al-Maini,Surinder Dhanjil,Meyypan Sockalingam,Ajit Saxena,Andrew Nicolaides,Aditya Sharma,Vijay Rathore,J N A Ajuluchukwu,Mostafa Fatemi,Azra Alizad,Vijay Viswanathan,P.K. Krishnan,Subbaram Naidu +45 more
TL;DR: In this paper, the authors present the pathological layout of the lungs in the COVID-19 scenario, followed by understanding and then explaining the comorbid statistical distributions in the ARDS framework.
Journal ArticleDOI
Image reconstruction under multiplicative speckle noise using total variation
TL;DR: A method for reconstructing images or volumes from a partial set of observations, under the Rayleigh distributed multiplicative noise model, which is the appropriate algebraic model in ultrasound (US) imaging is presented.