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386 results on '"Mukherjee, Tanmoy"'

1. The STOIC2021 COVID-19 AI challenge: applying reusable training methodologies to private data

Author

Boulogne, Luuk H., Lorenz, Julian, Kienzle, Daniel, Schon, Robin, Ludwig, Katja, Lienhart, Rainer, Jegou, Simon, Li, Guang, Chen, Cong, Wang, Qi, Shi, Derik, Maniparambil, Mayug, Muller, Dominik, Mertes, Silvan, Schroter, Niklas, Hellmann, Fabio, Elia, Miriam, Dirks, Ine, Bossa, Matias Nicolas, Berenguer, Abel Diaz, Mukherjee, Tanmoy, Vandemeulebroucke, Jef, Sahli, Hichem, Deligiannis, Nikos, Gonidakis, Panagiotis, Huynh, Ngoc Dung, Razzak, Imran, Bouadjenek, Reda, Verdicchio, Mario, Borrelli, Pasquale, Aiello, Marco, Meakin, James A., Lemm, Alexander, Russ, Christoph, Ionasec, Razvan, Paragios, Nikos, van Ginneken, Bram, and Dubois, Marie-Pierre Revel

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Challenges drive the state-of-the-art of automated medical image analysis. The quantity of public training data that they provide can limit the performance of their solutions. Public access to the training methodology for these solutions remains absent. This study implements the Type Three (T3) challenge format, which allows for training solutions on private data and guarantees reusable training methodologies. With T3, challenge organizers train a codebase provided by the participants on sequestered training data. T3 was implemented in the STOIC2021 challenge, with the goal of predicting from a computed tomography (CT) scan whether subjects had a severe COVID-19 infection, defined as intubation or death within one month. STOIC2021 consisted of a Qualification phase, where participants developed challenge solutions using 2000 publicly available CT scans, and a Final phase, where participants submitted their training methodologies with which solutions were trained on CT scans of 9724 subjects. The organizers successfully trained six of the eight Final phase submissions. The submitted codebases for training and running inference were released publicly. The winning solution obtained an area under the receiver operating characteristic curve for discerning between severe and non-severe COVID-19 of 0.815. The Final phase solutions of all finalists improved upon their Qualification phase solutions.HSUXJM-TNZF9CHSUXJM-TNZF9C

Published
2023

3. Representation Learning with Information Theory for COVID-19 Detection

Author

Berenguer, Abel Díaz, Mukherjee, Tanmoy, Bossa, Matias, Deligiannis, Nikos, and Sahli, Hichem

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Successful data representation is a fundamental factor in machine learning based medical imaging analysis. Deep Learning (DL) has taken an essential role in robust representation learning. However, the inability of deep models to generalize to unseen data can quickly overfit intricate patterns. Thereby, we can conveniently implement strategies to aid deep models in discovering useful priors from data to learn their intrinsic properties. Our model, which we call a dual role network (DRN), uses a dependency maximization approach based on Least Squared Mutual Information (LSMI). The LSMI leverages dependency measures to ensure representation invariance and local smoothness. While prior works have used information theory measures like mutual information, known to be computationally expensive due to a density estimation step, our LSMI formulation alleviates the issues of intractable mutual information estimation and can be used to approximate it. Experiments on CT based COVID-19 Detection and COVID-19 Severity Detection benchmarks demonstrate the effectiveness of our method.

Published
2022

4. The STOIC2021 COVID-19 AI challenge: Applying reusable training methodologies to private data

Author

Boulogne, Luuk H., Lorenz, Julian, Kienzle, Daniel, Schön, Robin, Ludwig, Katja, Lienhart, Rainer, Jégou, Simon, Li, Guang, Chen, Cong, Wang, Qi, Shi, Derik, Maniparambil, Mayug, Müller, Dominik, Mertes, Silvan, Schröter, Niklas, Hellmann, Fabio, Elia, Miriam, Dirks, Ine, Bossa, Matías Nicolás, Berenguer, Abel Díaz, Mukherjee, Tanmoy, Vandemeulebroucke, Jef, Sahli, Hichem, Deligiannis, Nikos, Gonidakis, Panagiotis, Huynh, Ngoc Dung, Razzak, Imran, Bouadjenek, Reda, Verdicchio, Mario, Borrelli, Pasquale, Aiello, Marco, Meakin, James A., Lemm, Alexander, Russ, Christoph, Ionasec, Razvan, Paragios, Nikos, van Ginneken, Bram, and Revel, Marie-Pierre

Published
2024
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5. NLX-GPT: A Model for Natural Language Explanations in Vision and Vision-Language Tasks

Author

Sammani, Fawaz, Mukherjee, Tanmoy, and Deligiannis, Nikos

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Computation and Language
Abstract

Natural language explanation (NLE) models aim at explaining the decision-making process of a black box system via generating natural language sentences which are human-friendly, high-level and fine-grained. Current NLE models explain the decision-making process of a vision or vision-language model (a.k.a., task model), e.g., a VQA model, via a language model (a.k.a., explanation model), e.g., GPT. Other than the additional memory resources and inference time required by the task model, the task and explanation models are completely independent, which disassociates the explanation from the reasoning process made to predict the answer. We introduce NLX-GPT, a general, compact and faithful language model that can simultaneously predict an answer and explain it. We first conduct pre-training on large scale data of image-caption pairs for general understanding of images, and then formulate the answer as a text prediction task along with the explanation. Without region proposals nor a task model, our resulting overall framework attains better evaluation scores, contains much less parameters and is 15$\times$ faster than the current SoA model. We then address the problem of evaluating the explanations which can be in many times generic, data-biased and can come in several forms. We therefore design 2 new evaluation measures: (1) explain-predict and (2) retrieval-based attack, a self-evaluation framework that requires no labels. Code is at: https://github.com/fawazsammani/nlxgpt., Comment: Accepted to CVPR 2022

Published
2022

9. Representation Learning with Information Theory to Detect COVID-19 and Its Severity

Author

Berenguer, Abel Díaz, Mukherjee, Tanmoy, Da, Yifei, Bossa, Matías Nicolás, Kvasnytsia, Maryna, Vandemeulebroucke, Jef, Deligiannis, Nikos, Sahli, Hichem, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Karlinsky, Leonid, editor, Michaeli, Tomer, editor, and Nishino, Ko, editor

Published
2023
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10. Multiview learning with sparse and unannotated data

Author

Mukherjee, Tanmoy

Subjects
006.3
Abstract

Obtaining annotated training data for supervised learning, is a bottleneck in many contemporary machine learning applications. The increasing prevalence of multi-modal and multi-view data creates both new opportunities for circumventing this issue, and new application challenges. In this thesis we explore several approaches to alleviating annotation issues in multi-view scenarios. We start by studying the problem of zero-shot learning (ZSL) for image recognition, where class-level annotations for image recognition are eliminated by transferring information from text modality instead. We next look at cross-modal matching, where paired instances across views provide the supervised label information for learning. We develop methodology for unsupervised and semi-supervised learning of pairing, thus eliminating the need for annotation requirements. We rst apply these ideas to unsupervised multi-view matching in the context of bilingual dictionary induction (BLI), where instances are words in two languages and nding a correspondence between the words produces a cross-lingual word translation model. We then return to vision and language and look at learning unsupervised pairing between images and text. We will see that this can be seen as a limiting case of ZSL where text-image pairing annotation requirements are completely eliminated. Overall these contributions in multi-view learning provide a suite of methods for reducing annotation requirements: both in conventional classi cation and cross-view matching settings.

Published
2021

13. Stoma-free Survival After Rectal Cancer Resection With Anastomotic Leakage: Development and Validation of a Prediction Model in a Large International Cohort

Author

Greijdanus, Nynke G., Wienholts, Kiedo, Ubels, Sander, Talboom, Kevin, Hannink, Gerjon, Wolthuis, Albert, de Lacy, Francisco B., Lefevre, Jérémie H., Solomon, Michael, Frasson, Matteo, Rotholtz, Nicolas, Denost, Quentin, Perez, Rodrigo O., Konishi, Tsuyoshi, Panis, Yves, Rutegård, Martin, Hompes, Roel, Rosman, Camiel, van Workum, Frans, Tanis, Pieter J., de Wilt, Johannes H.W., Bremers, Andreas J.A., Ferenschild, Floris T., de Vriendt, Stefanie, D’Hoore, André, Bislenghi, Gabriele, Farguell, Jordi, Lacy, Antonio M., Atienza, Paula González, van Kessel, Charlotte S., Parc, Yann, Voron, Thibault, Collard, Maxime K., Muriel, Jorge Sancho, Cholewa, Hannia, Mattioni, Laura A., Frontali, Alice, Polle, Sebastiaan W., Polat, Fatih, Obihara, Ndidi J., Vailati, Bruna B., Kusters, Miranda, Tuynmann, Jurriaan B., Hazen, Sanne J.A., Grüter, Alexander A.J., Amano, Takahiro, Fujiwara, Hajime, Salomon, Mario, Ruiz, Hernán, Gonzalez, Ricardo, Estefanía, Diego, Avellaneda, Nicolas, Carrie, Augusto, Santillan, Mateo, Pantoja Pachajoa, Diana A., Parodi, Matias, Gielis, Manuel, Binder, Alf-Dorian, Gürtler, Thomas, Riedl, Peter, Badiani, Sarit, Berney, Christophe, Morgan, Matthew, Hollington, Paul, da Silva, Nigel, Nair, Gavin, Ho, Yiu M., Lamparelli, Michael, Kapadia, Raj, Kroon, Hidde M., Dudi-Venkata, Nagendra N., Liu, Jianliang, Sammour, Tarik, Flamey, Nicolas, Pattyn, Paul, Chaoui, Ahmed, Vansteenbrugge, Louis, van den Broek, Nathalie E.J., Vanclooster, Patrick, de Gheldere, Charles, Pletinckx, Pieter, Defoort, Barbara, Dewulf, Maxime, Slavchev, Mihail, Belev, Nikolay, Atanasov, Boyko, Krastev, Panche, Sokolov, Manol, Maslyankov, Svilen, Gribnev, Petar, Pavlov, Vasil, Ivanov, Tsvetomir, Karamanliev, Martin, Filipov, Emil, Tonchev, Pencho, Aigner, Felix, Mitteregger, Martin, Allmer, Caterina, Seitinger, Gerald, Colucci, Nicola, Buchs, Nicolas, Ris, Frédéric, Toso, Christian, Gialamas, Eleftherios, Vuagniaux, Aurélie, Chautems, Roland, Sauvain, Marc-Olivier, Daester, Silvio, von Flüe, Markus, Guenin, Marc-Olivier, Taha-Mehlitz, Stephanie, Hess, Gabriel F., Martínek, Lubomír, Skrovina, Matej, Machackova, Maria, Benčurik, Vladimir, Uluk, Deniz, Pratschke, Johann, Dittrich, Luca S., Guel-Klein, Safak, Perez, Daniel, Grass, Julia-Kristin, Melling, Nathaniel, Mueller, Simone, Iversen, Lene H., Eriksen, Jacob D., Baatrup, Gunnar, Al-Najami, Issam, Bjørsum-Meyer, Thomas, Teras, Jüri, Teras, Roland M., Monib, Fatma A., Abu Elnga Ahmed, Nagm Eldin, Alkady, Eithar, Ali, Ahmed K., Khedr, Gehan Abd Elatti, Abdelaal, Ahmed Samir, Bassyouni Ashoush, Fouad M., Ewedah, Moataz, Elshennawy, Eslam M., Hussein, Mohamed, Fernández-Martínez, Daniel, García-Flórez, Luis J., Fernández-Hevia, María, Suárez-Sánchez, Aida, del Hoyo Aretxabala, Izaskun, Docampo, Iria Losada, Zabala, Jesús Gómez, Tejedor, Patricia, Morales Bernaldo de Quirós, Javier T., Quiroga, Ignacio Bodega, Navarro-Sánchez, Antonio, Darias, Iván Soto, Fernández, Cristina López, de La Cruz Cuadrado, Cristina, Sánchez-Guillén, Luis, López-Rodríguez-Arias, Francisco, Soler-Silva, Álvaro, Arroyo, Antonio, Bernal-Sprekelsen, Juan C., Gómez-Abril, Segundo Á., Gonzálvez, Paula, Torres, María T., Sánchez, Teresa Rubio, Antona, Francisco Blanco, Sánchez Lara, Juan E., Alcázar Montero, José A., Fernández-Martínez, Daniel, García-Flórez, Luis J., Fernández-Hevia, María, Suárez-Sánchez, Aida, Colás-Ruiz, Enrique, Tasende-Presedo, Marta M., Fernández-Hurtado, Ignacio, Cifuentes-Ródenas, José A., Suárez, Marta Castro, Losada, Manuel, Hernández, Miguel, Alonso, Alfredo, Diéguez, Beatriz, Serralta, Daniel, Medina Quintana, Rita E., Gil Lopez, Jose M., Pinto, Francisca Lima, Nieto-Moreno, Elena, Bonito, Alba Correa, Santacruz, Carlos Cerdán, Marcos, Elena Bermejo, Septiem, Javier García, Calero-Lillo, Aránzazu, Alanez-Saavedra, Javier, Muñoz-Collado, Salvador, López-Lara, Manuel, Martínez, María Labalde, Herrero, Eduardo Ferrero, Borda, Francisco Javier García, Villar, Óscar García, Escartín, Jorge, Blas, Juan L., Ferrer, Rocío, Egea, Jorge García, Rodríguez-Infante, Antonio, Mínguez-Ruiz, Germán, Carreño-Villarreal, Guillermo, Pire-Abaitua, Gerardo, Dziakova, Jana, Rodríguez, Carlos Sáez-Cazallas, Pizarro Aranda, María J., Muguerza Huguet, José M., Borda-Arrizabalaga, Nerea, Enriquez-Navascués, José M., Echaniz, Garazi Elorza, Ansorena, Yolanda Saralegui, Estaire-Gómez, Mercedes, Martínez-Pinedo, Carlos, Barbero-Valenzuela, Alejandro, Ruíz-García, Pablo, Kraft, Miquel, Gómez-Jurado, María J., Pellino, Gianluca, Espín-Basany, Eloy, Cotte, Eddy, Panel, Nathalie, Goutard, Claire-Angéline, deÁngelis, Nicola, Lauka, Lelde, Shaikh, Shafaque, Osborne, Laura, Ramsay, George, Nichita, Vladimir-Ion, Bhandari, Santosh, Sarmah, Panchali, Bethune, Rob M., Pringle, Heather C.M., Massey, Lisa, Fowler, George E., Hamid, Hytham K.S., de Simone, Belinda D., Kynaston, James, Bradley, Nicholas, Stienstra, Roxane M., Gurjar, Shashank, Mukherjee, Tanmoy, Chandio, Ashfaq, Ahmed, Safia, Singh, Baljit, Runau, Francois, Chaudhri, Sanjay, Siaw, Oliver, Sarveswaran, Janahan, Miu, Victor, Ashmore, Daniel, Darwich, Haitham, Singh-Ranger, Deepak, Singh, Nirbhaibir, Shaban, Mohamed, Gareb, Fahed, Petropolou, Thalia, Polydorou, Adreas, Dattani, Mit, Afzal, Asma, Bavikatte, Akshay, Sebastian, Boby, Ward, Nicholas, Mishra, Amitabh, Manatakis, Dimitrios, Agalianos, Christos, Tasis, Nikolaos, Antonopoulou, Maria-Ioanna, Karavokyros, Ioannis, Charalabopoulos, Alexandros, Schizas, Dimitrios, Baili, Efstratia, Syllaios, Athanasios, Karydakis, Lysandros, Vailas, Michail, Balalis, Dimitrios, Korkolis, Dimitrios, Plastiras, Aris, Rompou, Aliki, Xenaki, Sofia, Xynos, Evangelos, Chrysos, Emmanuel, Venianaki, Maria, Christodoulidis, Grigorios, Perivoliotis, Konstantinos, Tzovaras, George, Baloyiannis, Ioannis, Ho, Man-Fung, Ng, Simon Siu-man, Mak, Tony Wing-chung, Futaba, Kaori, Šantak, Goran, Šimleša, Damir, osić, Jurica, Zukanović, Goran, Kelly, Michael E., Larkin, John O., McCormick, Paul H., Mehigan, Brian J., Connelly, Tara M., Neary, Peter, Ryan, Jessica, McCullough, Peter, Al-Juaifari, Maytham A., Hammoodi, Hayder, Abbood, Ali Hashim, Calabrò, Marcello, Muratore, Andrea, Terra, Antonio La, Farnesi, Francesca, Feo, Carlo V., Fabbri, Nicolò, Pesce, Antonio, Fazzin, Marta, Roscio, Francesco, Clerici, Federico, Lucchi, Andrea, Vittori, Laura, Agostinelli, Laura, Ripoli, Maria Cristina, Sambucci, Daniele, Porta, Andrea, Sinibaldi, Giovanni, Crescentini, Giacomo, larcinese, Antonella, Picone, Emanuele, Persiani, Roberto, Biondi, Alberto, Pezzuto, Roberto, Lorenzon, Laura, Rizzo, Gianluca, Coco, Claudio, D’Agostino, Luca, Spinelli, Antonino, Sacchi, Matteo M., Carvello, Michele, Foppa, Caterina, Spinelli, Antonino, Sacchi, Matteo M., Carvello, Michele, Foppa, Caterina, Maroli, Annalisa, Palini, Gian M., Garulli, Gianluca, Zanini, Nicola, Delrio, Paolo, Rega, Daniela, Carbone, Fabio, Aversano, Alessia, Pirozzolo, Giovanni, Recordare, Alfonso, DʼAlimonte, Lucrezia, Vignotto, Chiara, Corbellini, Carlo, Sampietro, Gianluca M., Lorusso, Leonardo, Manzo, Carlo A., Ghignone, Federico, Ugolini, Giampaolo, Montroni, Isacco, Pasini, Franceso, Pasini, Francesco, Ballabio, Michele, Bisagni, Pietro, Armao, Francesca T., Longhi, Marco, Ghazouani, Omar, Galleano, Raffaele, Tamini, Nicolò, Oldani, Massimo, Nespoli, Luca, Picciariello, Arcangelo, Altomare, Donato F., Tomasicchio, Giovanni, Lantone, Giuliano, Catena, Fausto, Giuffrida, Mario, Annicchiarico, Alfredo, Perrone, Gennaro, Grossi, Ugo, Santoro, Giulio A., Zanus, Giacomo, Iacomino, Alessandro, Novello, Simone, Passuello, Nicola, Zucchella, Martino, Puca, Lucia, deGiuli, Maurizio, Reddavid, Rossella, Scabini, Stefano, Aprile, Alessandra, Soriero, Domenico, Fioravanti, Emanuela, Rottoli, Matteo, Romano, Angela, Tanzanu, Marta, Belvedere, Angela, Mariani, Nicolò M., Ceretti, Andrea P., Opocher, Enrico, Gallo, Gaetano, Sammarco, Giuseppe, de Paola, Gilda, Pucciarelli, Salvatore, Marchegiani, Francesco, Spolverato, Gaya, Buzzi, Gianluca, Di Saverio, Salomone, Meroni, Paola, Parise, Cristiano, Bottazzoli, Elisa I., Lapolla, Pierfrancesco, Brachini, Gioia, Cirillo, Bruno, Mingoli, Andrea, Sica, Giuseppe, Siragusa, Leandro, Bellato, Vittoria, Cerbo, Daniele, de Pasqual, Carlo A., de Manzoni, Giovanni, di Cosmo, Maria A., Alrayes, Bourhan M.H., Qandeel, Mahmoud W. M., Hani, Mohammad Bani, Rabadi, Alexander, el Muhtaseb, Mohammad S., Abdeen, Basel, Karmi, Fahed, Žilinskas, Justas, Latkauskas, Tadas, Tamelis, Algimantas, Pikūnienė, Ingrida, Šlenfuktas, Vygintas, Poskus, Tomas, Kryzauskas, Marius, Jakubauskas, Matas, Mikalauskas, Saulius, Jakubauskiene, Lina, Hassan, Soha Y., Altrabulsi, Amani, Abdulwahed, Eman, Ghmagh, Reem, Deeknah, Abdulqudus, Alshareea, Entisar, Elhadi, Muhammed, Abujamra, Saleh, Msherghi, Ahmed A., Tababa, Osama W.E., Majbar, Mohammed A., Souadka, Amine, Benkabbou, Amine, Mohsine, Raouf, Echiguer, Sabrillah, Moctezuma-Velázquez, Paulina, Salgado-Nesme, Noel, Vergara-Fernández, Omar, Sainz-Hernández, Juan C., Alvarez-Bautista, Francisco E., Zakaria, Andee D., Zakaria, Zaidi, Wong, Michael P.K., Ismail, Razif, Ibrahim, Aini F., Abdullah, Nik A.N., Julaihi, Rokayah, Bhat, Sameer, OʼGrady, Greg, Bissett, Ian, Lamme, Bas, Musters, Gijsbert D., Dinaux, Anne M., Grotenhuis, Brechtje A., Steller Arend G.J. Aalbers, Ernst J., Leeuwenburgh, Marjolein M., Rutten, Harm J.T., Burger, Jacobus W.A., Bloemen, Johanne G., Ketelaers, Stijn H.J., Waqar, Usama, Chawla, Tabish, Rauf, Hareem, Rani, Pallavi, Talsma, Aaldert K., Scheurink, Lieke, van Praagh, Jasper B., Segelman, Josefin, Nygren, Jonas, Anderin, Kajsa, Tiefenthal, Marit, de Andrés, Beatriz, Beltrán de Heredia, Juan P., Vázquez, Andrea, Gómez, Tania, Golshani, Parisa, Kader, Rawaz, Mohamed, Abudi, Westerterp, Marinke, Marinelli, Andreas, Niemer, Quirine, Doornebosch, Pascal G., Shapiro, Joël, Vermaas, Maarten, de Graaf, Eelco J.R., van Westreenen, Hendrik L., Zwakman, Marije, van Dalsen, Annette D., Vles, Wouter J., Nonner, Joost, Toorenvliet, Boudewijn R., Janssen, Paul T.J., Verdaasdonk, Emiel G.G., Amelung, Femke J., Peeters Renu R. Bahadoer, Koen C.M.J., Holman, Fabian A., Heemskerk, Jeroen, Vosbeek, Noortje, Leijtens, Jeroen W.A., Taverne, Sophie B.M., Heijnen, Bob H.M., El-Massoudi, Youssef, de Groot-van Veen, Irene, Hoff, Christiaan, Jou-Valencia, Daniela, Consten, Esther C.J., Burghgraef, Thijs A., Geitenbeek, Ritch, Hulshof, Lorenzo G.W.L., Slooter, Gerrit D., Reudink, Muriël, Bouvy, Nicole D., Wildeboer, Aurelia C. L., Verstappen, Sonja, Pennings, Alexander J., van den Hengel, Berber, Wijma, Allard G., de Haan, Jael, de Nes, Lindsey C.F., Heesink, Vera, Karsten, Tom, Heidsma, Charlotte M., Koemans, Willem J., Dekker, Jan-Willem T., van der Zijden, Charlène J., Roos, Daphne, Demirkiran, Ahmet, van der Burg, Sjirk, Oosterling, Steven J., Hoogteijling, Tijs J., Wiering, Bastiaan, Smeeing, Diederik P.J., Havenga, Klaas, Lutfi, Hamid, Consten, Esther C.J., Tsimogiannis, Konstantinos, Sköldberg, Filip, Folkesson, Joakim, den Boer, Frank, van Schaik, Ted G., van Gerven, Pieter, Sietses, Colin, Hol, Jeroen C., Boerma, Evert-Jan G., Creemers, Davy M.J., Schultz, Johannes K., Frivold, Tone, Riis, Rolf, Gregussen, Hilde, Busund, Sondre, Sjo, Ole H., Gaard, Maria, Krohn, Nina, Ersryd, Amanda L., Leung, Edmund, Waqar, Usama, Chawla, Tabish, Rauf, Hareem, Rani, Pallavi, Sultan, Hytham, Hajjaj, Baraa Nabil, Alhisi, Ahmed Jehad, Khader, Ahmed A.E., Mendes, Ana Filipa Dias, Semião, Miguel, Faria, Luis Queiroz, Azevedo, Constança, da Costa Devesa, Helena M., Martins, Sónia Fortuna, Rodrigues Jarimba, Aldo M., Ribeiro Marques, Sónia M., Ferreira, Rita Marques, Oliveira, António, Ferreira, Cátia, Pereira, Ricardo, Surlin, Valeriu M., Graure, Giorgiana M., Ramboiu, Stefan Patrascu Sandu D., Negoi, Ionut, Ciubotaru, Cezar, Stoica, Bogdan, Tanase, Ioan, Stoica, Bogdan, Ciubotaru, Cezar, Negoita, Valentina M., Florea, Sabrina, Macau, Florin, Vasile, Mihai, Stefanescu, Victor, Dimofte, Gabriel-Mihail, Luncă, Sorinel, Roată, Cristian-Ene, Mu[Latin Small Letter s with comma below]ină, Ana-Maria, Garmanova, Tatiana, Agapov, Mikhail N., Markaryan, Daniil G., Eduard, Galliamov, Yanishev, Alexey, Abelevich, Alexander, Bazaev, Andrey, Rodimov, Sergey V., Filimonov, Victor B., Melnikov, Andrey A., Suchkov, Igor A., Drozdov, EvgeniyS., Kostromitskiy, Dmitriy N., Sjöström, Olle, Matthiessen, Peter, Baban, Bayar, Gadan, Soran, Jadid, Kaveh Dehlaghi, Staffan, Maria, Park, Jennifer M., Rydbeck, Daniel, Lydrup, Marie-Louise, Buchwald, Pamela, Jutesten, Henrik, Darlin, Lotten, Lindqvist, Ebba, Nilsson, Karl, Larsson, Per-Anders, Jangmalm, Staffan, Košir, Jurij A., Tomažič, Aleš, Grosek, Jan, Božič, Tajda Košir, Zazo, Aya, Zazo, Rama, Fares, Hala, Ayoub, Kusay, Niazi, Ammar, Mansour, Ali, Abbas, Ayman, Tantoura, Mohammad, Hamdan, Alaa, Hassan, Naya, Hasan, Bassam, Saad, Ahmad, Sebai, Amine, Haddad, Anis, Maghrebi, Houcine, Kacem, Montasser, Yalkın, Ömer, Samsa, Mehmet Veysi, Atak, İbrahim, Balci, Bengi, Haberal, Elifcan, Dogan, Lütfi, Gecim, Ibrahim E., Akyol, Cihangir, Koc, Mehmet A., Sivrikoz, Emre, Piyadeoğlu, Deniz, Larkin, John O., avanagh, Dara O., Sökmen, Selman, Bişgin, Tayfun, Günenç, Erşan, Güzel, Melek, Leventoğlu, Sezai, Yüksel, Osman, Kozan, Ramazan, Göbüt, Hüseyin, Cengiz, Fevzi, Erdinc, Kemal, Acar, Nihan Coşgun, Kamer, Erdinc, Özgür, İlker, Aydın, Oguzhan, Keskin, Metin, Bulut, Mehmet Türker, Kulle, Cemil B., Kara, Yasin, Sıbıç, Osman, Özata, İbrahim H., Buğra, Dursun, Balık, Emre, Kulle, Cemil B., Çakır, Murat, Alhardan, Anas, Colak, Elif, CiftciEngin Aybar, Ahmet B., Sari, Ahmet Can, Atici, Semra Demirli, Kaya, Tayfun, Dursun, Ayberk, Calik, Bulent, Özkan, Ömer Faruk, Ülgür, Hanife Şeyda, Düzgün, Özgül, Monson, John, George, Sarah, Woods, Kayla, Al-Eryani, Fatima, Albakry, Rudaina, Coetzee, Emile, Boutall, Adam, Herman, Ayesiga, Warden, Claire, Mugla, Naser, Forgan, Tim, Mia, Imraan, and Lambrechts, Anton

Published
2023
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15. Deep Matching Autoencoders

Author

Mukherjee, Tanmoy, Yamada, Makoto, and Hospedales, Timothy M.

Subjects
Computer Science - Computer Vision and Pattern Recognition, Statistics - Machine Learning
Abstract

Increasingly many real world tasks involve data in multiple modalities or views. This has motivated the development of many effective algorithms for learning a common latent space to relate multiple domains. However, most existing cross-view learning algorithms assume access to paired data for training. Their applicability is thus limited as the paired data assumption is often violated in practice: many tasks have only a small subset of data available with pairing annotation, or even no paired data at all. In this paper we introduce Deep Matching Autoencoders (DMAE), which learn a common latent space and pairing from unpaired multi-modal data. Specifically we formulate this as a cross-domain representation learning and object matching problem. We simultaneously optimise parameters of representation learning auto-encoders and the pairing of unpaired multi-modal data. This framework elegantly spans the full regime from fully supervised, semi-supervised, and unsupervised (no paired data) multi-modal learning. We show promising results in image captioning, and on a new task that is uniquely enabled by our methodology: unsupervised classifier learning., Comment: 10 pages

Published
2017

16. The role of extracellular vesicles in the pathogenesis of gynecological cancer.

Author

Chatterjee, Madhura, Gupta, Saurabh, Mukherjee, Tanmoy, Parashar, Deepak, Kumar, Umesh, Maitra, Arindam, and Das, Kaushik

Subjects
GESTATIONAL trophoblastic disease, VULVAR cancer, UTERINE cancer, VAGINAL cancer, FEMALE reproductive organs, GYNECOLOGIC cancer
Abstract

Gynecological cancer, the most common form of cancers in women worldwide, initiates in the reproductive organs of females. More often, the common treatment measures, i.e. surgery, radiation, and medical oncology are found to be unsuccessful in the treatment of gynecological tumors. Emerging evidence indicates that extracellular vesicles (EVs) play a significant role in the pathogenesis of gynecological cancers by distinct mechanisms. The present review highlights how EVs contribute to the progression of different types of gynecological cancers such as cervical cancer, endometrial cancer, ovarian cancer, vaginal cancer, uterine sarcoma, gestational trophoblastic disease (GTD), and vulvar cancer. The primary focus is to understand how EVs’ cargo alters the phenotypic response of the recipient cells, thereby contributing to the progression of the disease, thus can be considered as a prognostic and diagnostic biomarker. A brief discussion on the role of EVs in the diagnosis and prognosis of different gynecological cancer types is also highlighted. Targeting the biogenesis of the EVs, their inside cargo, and EVs uptake by the recipient cells could be a potential therapeutic approach in the treatment of gynecological cancer beside conventional therapeutic means. [ABSTRACT FROM AUTHOR]

Published
2024
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19. A Noble Approach Toward Security Implementation in Cloud Virtualization Framework

Author

Mukherjee, Tanmoy, Sahana, Sudipta, Sarddar, Debabrata, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Tan, Kay Chen, Series Editor, Kundu, Sumit, editor, Acharya, U. Shripathi, editor, De, Chanchal Kr., editor, and Mukherjee, Surajit, editor

Published
2020
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21. Go-Park: A Parking Lot Allocation System in Smart Cities

Author

Mukherjee, Tanmoy, Gupta, Shayon, Sen, Poulomi, Pandey, Vijay, Karmakar, Kamalesh, Barbosa, Simone Diniz Junqueira, Series Editor, Filipe, Joaquim, Series Editor, Kotenko, Igor, Series Editor, Sivalingam, Krishna M., Series Editor, Washio, Takashi, Series Editor, Yuan, Junsong, Series Editor, Zhou, Lizhu, Series Editor, Ghosh, Ashish, Series Editor, Singh, Mayank, editor, Gupta, P. K., editor, Tyagi, Vipin, editor, Flusser, Jan, editor, and Ören, Tuncer, editor

Published
2018
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22. Coagulation Protease-Driven Cancer Immune Evasion: Potential Targets for Cancer Immunotherapy.

Author

Paul, Subhojit, Mukherjee, Tanmoy, and Das, Kaushik

Subjects
*IMMUNOTHERAPY, *TUMOR markers, *CELLULAR signal transduction, *PROTHROMBIN, *BIOTRANSFORMATION (Metabolism), *PROTEOLYTIC enzymes, *TUMORS, *CELL receptors, *DISEASE progression, *DISEASE complications
Abstract

Simple Summary: Blood coagulation and cancer are intrinsically related; hyper-thrombotic complications are often observed in certain types of tumors. On the other hand, coagulation proteases are shown to promote the progression of cancer in a variety of unique mechanisms. The present review highlights how coagulation protease-induced signaling contributes to the immune evasion of different cancers, leading to the development and progression of cancer. Understanding the mechanistic insights will aid developing novel therapeutics against coagulation protease-driven response, which could be used in combination with conventional immune checkpoint inhibitors to increase the efficacy of cancer treatment and patient survival. Blood coagulation and cancer are intrinsically connected, hypercoagulation-associated thrombotic complications are commonly observed in certain types of cancer, often leading to decreased survival in cancer patients. Apart from the common role in coagulation, coagulation proteases often trigger intracellular signaling in various cancers via the activation of a G protein-coupled receptor superfamily protease: protease-activated receptors (PARs). Although the role of PARs is well-established in the development and progression of certain types of cancer, their impact on cancer immune response is only just emerging. The present review highlights how coagulation protease-driven PAR signaling plays a key role in modulating innate and adaptive immune responses. This is followed by a detailed discussion on the contribution of coagulation protease-induced signaling in cancer immune evasion, thereby supporting the growth and development of certain tumors. A special section of the review demonstrates the role of coagulation proteases, thrombin, factor VIIa, and factor Xa in cancer immune evasion. Targeting coagulation protease-induced signaling might be a potential therapeutic strategy to boost the immune surveillance mechanism of a host fighting against cancer, thereby augmenting the clinical consequences of targeted immunotherapeutic regimens. [ABSTRACT FROM AUTHOR]

Published
2024
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25. Pattern of Utilization and Indications of Use of Blood and Blood Products in a Tertiary Care Hospital of West Bengal.

Author

Ishore, Kaushik, Nandy, Pratyasha, and Mukherjee, Tanmoy

Subjects
BLOOD products, TERTIARY care, PLASMA products, ERYTHROCYTES, BLOOD banks, BLOOD platelet transfusion, RED blood cell transfusion
Abstract

Background: Blood cannot be artificially manufactured, nor have we found a proper, well-rounded substitute. This makes proper utilization of the donated blood a task of immense value. Contamination of the blood makes it non-utilizable; hence, proper use of blood and blood products is extremely important to avoid any wastage of these scarce resources. Objectives: The objective of this study was to assess the utilization pattern of blood and blood components and the indications of use of blood components in a tertiary care hospital. Material and Methods: An observational cross-sectional study was conducted for a period of months (July 2023 - September 2023) in the blood bank department of a tertiary care hospital in North Bengal. Secondary data were collected by reviewing the records, registers, and computer-stored data at the blood bank and pre-designed, pre-tested data collection schedule. Data were organized and presented using the principles of descriptive statistics for demographic and clinical variables. Results: There were a total of 4965 transfusion requisitions during the study period. Packed red blood cells (RBCs) were the most demanded blood component (42%), followed by fresh frozen plasma (24%), platelets (23%), and then whole blood (11%). The commonest indication for blood transfusion was pregnancy/childbirthrelated cause (27%) followed by elective surgery (23%). Requisition of the blood components was found to be maximum by the medicine department (43%), followed by the gyne and obstetrics department (16.5%). Conclusion: Periodic review and audit of blood component usage is very much essential to assess the blood utilization pattern in any hospital. [ABSTRACT FROM AUTHOR]

Published
2024
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35. Summarizing Answers for Community Question Answer Services

Author

Pande, Vinay, Mukherjee, Tanmoy, Varma, Vasudeva, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Gurevych, Iryna, editor, Biemann, Chris, editor, and Zesch, Torsten, editor

Published
2013
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37. Metabolites enhance innate resistance to human Mycobacterium tuberculosis infection

Author

Tripathi, Deepak, primary, Devalraju, Kamakshi Prudhula, additional, Neela, Venkata Sanjeev Kumar, additional, Mukherjee, Tanmoy, additional, Paidipally, Padmaja, additional, Radhakrishnan, Rajesh Kumar, additional, Dozmorov, Igor, additional, Vankayalapati, Abhinav, additional, Ansari, Mohammad Soheb, additional, Mallidi, Varalakshmi, additional, Bogam, Anvesh Kumar, additional, Singh, Karan P., additional, Samten, Buka, additional, Valluri, Vijaya Lakshmi, additional, and Vankayalapati, Ramakrishna, additional

Published
2022
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