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10. MS-DCANet: A Novel Segmentation Network For Multi-Modality COVID-19 Medical Images

Author

Pan, Xiaoyu, Zhu, Huazheng, Du, Jinglong, Hu, Guangtao, Han, Baoru, and Jia, Yuanyuan

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

The Coronavirus Disease 2019 (COVID-19) pandemic has increased the public health burden and brought profound disaster to humans. For the particularity of the COVID-19 medical images with blurred boundaries, low contrast and different infection sites, some researchers have improved the accuracy by adding more complexity. Also, they overlook the complexity of lesions, which hinder their ability to capture the relationship between segmentation sites and the background, as well as the edge contours and global context. However, increasing the computational complexity, parameters and inference speed is unfavorable for model transfer from laboratory to clinic. A perfect segmentation network needs to balance the above three factors completely. To solve the above issues, this paper propose a symmetric automatic segmentation framework named MS-DCANet. We introduce Tokenized MLP block, a novel attention scheme that use a shift-window mechanism to conditionally fuse local and global features to get more continuous boundaries and spatial positioning capabilities. It has greater understanding of irregular lesions contours. MS-DCANet also uses several Dual Channel blocks and a Res-ASPP block to improve the ability to recognize small targets. On multi-modality COVID-19 tasks, MS-DCANet achieved state-of-the-art performance compared with other baselines. It can well trade off the accuracy and complexity. To prove the strong generalization ability of our proposed model, we apply it to other tasks (ISIC 2018 and BAA) and achieve satisfactory results., Comment: 21pages,13 figures,9 tables

Published
2022
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34. FHEDN: A based on context modeling Feature Hierarchy Encoder-Decoder Network for face detection

Author

Zhou, Zexun, He, Zhongshi, Chen, Ziyu, Jia, Yuanyuan, Wang, Haiyan, Du, Jinglong, and Chen, Dingding

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Because of affected by weather conditions, camera pose and range, etc. Objects are usually small, blur, occluded and diverse pose in the images gathered from outdoor surveillance cameras or access control system. It is challenging and important to detect faces precisely for face recognition system in the field of public security. In this paper, we design a based on context modeling structure named Feature Hierarchy Encoder-Decoder Network for face detection(FHEDN), which can detect small, blur and occluded face with hierarchy by hierarchy from the end to the beginning likes encoder-decoder in a single network. The proposed network is consist of multiple context modeling and prediction modules, which are in order to detect small, blur, occluded and diverse pose faces. In addition, we analyse the influence of distribution of training set, scale of default box and receipt field size to detection performance in implement stage. Demonstrated by experiments, Our network achieves promising performance on WIDER FACE and FDDB benchmarks.

Published
2017

39. 3D-MRI Super-Resolution Algorithm Fusing Attention and Dilated Encoder-Decoder.

Author

ZHANG Jindi, JIA Yuanyuan, ZHU Huazheng, LI Hongbi, and DU Jinglong

Subjects
CONVOLUTIONAL neural networks, FEATURE extraction, IMAGE reconstruction algorithms, LINEAR network coding, MAGNETIC resonance imaging, ALGORITHMS, IMAGE reconstruction, EYE tracking
Abstract

In order to improve the spatial resolution of brain magnetic resonance imaging (MRI) images, the super resolution (SR) reconstruction method based on deep convolutional neural network (CNN) has achieved remarkable results. Increasing network depth or width usually can expand the receptive field of the network to improve the quality of reconstruction, but it is difficult to train the network due to the large parameter quantity and huge computing requirements. Meanwhile, the network does not pay enough attention to the medium and high frequency features of the image, which affects the quality of reconstruction. To solve the above problems, this paper proposes a cascaded channels-space attention and encode-decode network for 3D-MRI image SR reconstruction. Firstly, feature extraction is carried out in the low resolution space, and the image features are extracted by the symmetric connected encoder-decoder with dilated convolution to alleviate the checkerboard artifacts. Secondly, a serial connected channel-space attention module is constructed to capture the interdependence between the feature channels by channel attention, and the spatial attention is increased to assign weight to information of different positions, effectively enhancing the network' s learning of medium and high frequency information. Finally, subpixel convolution is used to sample the feature map to the target image size and meanwhile reduce memory consumption. Experimental results on public Kirby21 and BraTS datasets show that the proposed method is superior in both quantitative peak signal-to-noise ratio (PSNR), structural similarity (SSIM) and subjective visual quality when compared with traditional SR algorithms and mainstream CNN-based SR algorithms. [ABSTRACT FROM AUTHOR]

Published
2024
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44. Altimetry for the future: Building on 25 years of progress

Author

Abdalla, Saleh, Abdeh Kolahchi, Abdolnabi, Ablain, Michaël, Adusumilli, Susheel, Aich Bhowmick, Suchandra, Alou-Font, Eva, Amarouche, Laiba, Andersen, Ole Baltazar, Antich, Helena, Aouf, Lotfi, Arbic, Brian, Armitage, Thomas, Arnault, Sabine, Artana, Camila, Aulicino, Giuseppe, Ayoub, Nadia, Badulin, Sergei, Baker, Steven, Banks, Chris, Bao, Lifeng, Barbetta, Silvia, Barceló-Llull, Bàrbara, Barlier, François, Basu, Sujit, Bauer-Gottwein, Peter, Becker, Matthias, Beckley, Brian, Bellefond, Nicole, Belonenko, Tatyana, Benkiran, Mounir, Benkouider, Touati, Bennartz, Ralf, Benveniste, Jérôme, Bercher, Nicolas, Berge-Nguyen, Muriel, Bettencourt, Joao, Blarel, Fabien, Blazquez, Alejandro, Blumstein, Denis, Bonnefond, Pascal, Borde, Franck, Bouffard, Jérôme, Boy, François, Boy, Jean-Paul, Brachet, Cédric, Brasseur, Pierre, Braun, Alexander, Brocca, Luca, Brockley, David, Brodeau, Laurent, Brown, Shannon, Bruinsma, Sean, Bulczak, Anna, Buzzard, Sammie, Cahill, Madeleine, Calmant, Stéphane, Calzas, Michel, Camici, Stefania, Cancet, Mathilde, Capdeville, Hugues, Carabajal, Claudia Cristina, Carrere, Loren, Cazenave, Anny, Chassignet, Eric P., Chauhan, Prakash, Cherchali, Selma, Chereskin, Teresa, Cheymol, Cecile, Ciani, Daniele, Cipollini, Paolo, Cirillo, Francesca, Cosme, Emmanuel, Coss, Steve, Cotroneo, Yuri, Cotton, David, Couhert, Alexandre, Coutin-Faye, Sophie, Crétaux, Jean-François, Cyr, Frederic, d’Ovidio, Francesco, Darrozes, José, David, Cedric, Dayoub, Nadim, De Staerke, Danielle, Deng, Xiaoli, Desai, Shailen, Desjonqueres, Jean-Damien, Dettmering, Denise, Di Bella, Alessandro, Díaz-Barroso, Lara, Dibarboure, Gerald, Dieng, Habib Boubacar, Dinardo, Salvatore, Dobslaw, Henryk, Dodet, Guillaume, Doglioli, Andrea, Domeneghetti, Alessio, Donahue, David, Dong, Shenfu, Donlon, Craig, Dorandeu, Joël, Drezen, Christine, Drinkwater, Mark, Du Penhoat, Yves, Dushaw, Brian, Egido, Alejandro, Erofeeva, Svetlana, Escudier, Philippe, Esselborn, Saskia, Exertier, Pierre, Fablet, Ronan, Falco, Cédric, Farrell, Sinead Louise, Faugere, Yannice, Femenias, Pierre, Fenoglio, Luciana, Fernandes, Joana, Fernández, Juan Gabriel, Ferrage, Pascale, Ferrari, Ramiro, Fichen, Lionel, Filippucci, Paolo, Flampouris, Stylianos, Fleury, Sara, Fornari, Marco, Forsberg, Rene, Frappart, Frédéric, Frery, Marie-laure, Garcia, Pablo, Garcia-Mondejar, Albert, Gaudelli, Julia, Gaultier, Lucile, Getirana, Augusto, Gibert, Ferran, Gil, Artur, Gilbert, Lin, Gille, Sarah, Giulicchi, Luisella, Gómez-Enri, Jesús, Gómez-Navarro, Laura, Gommenginger, Christine, Gourdeau, Lionel, Griffin, David, Groh, Andreas, Guerin, Alexandre, Guerrero, Raul, Guinle, Thierry, Gupta, Praveen, Gutknecht, Benjamin D., Hamon, Mathieu, Han, Guoqi, Hauser, Danièle, Helm, Veit, Hendricks, Stefan, Hernandez, Fabrice, Hogg, Anna, Horwath, Martin, Idžanović, Martina, Janssen, Peter, Jeansou, Eric, Jia, Yongjun, Jia, Yuanyuan, Jiang, Liguang, Johannessen, Johnny A., Kamachi, Masafumi, Karimova, Svetlana, Kelly, Kathryn, Kim, Sung Yong, King, Robert, Kittel, Cecile M.M., Klein, Patrice, Klos, Anna, Knudsen, Per, Koenig, Rolf, Kostianoy, Andrey, Kouraev, Alexei, Kumar, Raj, Labroue, Sylvie, Lago, Loreley Selene, Lambin, Juliette, Lasson, Léa, Laurain, Olivier, Laxenaire, Rémi, Lázaro, Clara, Le Gac, Sophie, Le Sommer, Julien, Le Traon, Pierre-Yves, Lebedev, Sergey, Léger, Fabien, Legresy, Benoı̂t, Lemoine, Frank, Lenain, Luc, Leuliette, Eric, Levy, Marina, Lillibridge, John, Liu, Jianqiang, Llovel, William, Lyard, Florent, Macintosh, Claire, Makhoul Varona, Eduard, Manfredi, Cécile, Marin, Frédéric, Mason, Evan, Massari, Christian, Mavrocordatos, Constantin, Maximenko, Nikolai, McMillan, Malcolm, Medina, Thierry, Melet, Angelique, Meloni, Marco, Mertikas, Stelios, Metref, Sammy, Meyssignac, Benoit, Minster, Jean-François, Moreau, Thomas, Moreira, Daniel, Morel, Yves, Morrow, Rosemary, Moyard, John, Mulet, Sandrine, Naeije, Marc, Nerem, Robert Steven, Ngodock, Hans, Nielsen, Karina, Nilsen, Jan Even Øie, Niño, Fernando, Nogueira Loddo, Carolina, Noûs, Camille, Obligis, Estelle, Otosaka, Inès, Otten, Michiel, Oztunali Ozbahceci, Berguzar, P. Raj, Roshin, Paiva, Rodrigo, Paniagua, Guillermina, Paolo, Fernando, Paris, Adrien, Pascual, Ananda, Passaro, Marcello, Paul, Stephan, Pavelsky, Tamlin, Pearson, Christopher, Penduff, Thierry, Peng, Fukai, Perosanz, Felix, Picot, Nicolas, Piras, Fanny, Poggiali, Valerio, Poirier, Étienne, Ponce de León, Sonia, Prants, Sergey, Prigent, Catherine, Provost, Christine, Pujol, M-Isabelle, Qiu, Bo, Quilfen, Yves, Rami, Ali, Raney, R. Keith, Raynal, Matthias, Remy, Elisabeth, Rémy, Frédérique, Restano, Marco, Richardson, Annie, Richardson, Donald, Ricker, Robert, Ricko, Martina, Rinne, Eero, Rose, Stine Kildegaard, Rosmorduc, Vinca, Rudenko, Sergei, Ruiz, Simón, Ryan, Barbara J., Salaün, Corinne, Sanchez-Roman, Antonio, Sandberg Sørensen, Louise, Sandwell, David, Saraceno, Martin, Scagliola, Michele, Schaeffer, Philippe, Scharffenberg, Martin G., Scharroo, Remko, Schiller, Andreas, Schneider, Raphael, Schwatke, Christian, Scozzari, Andrea, Ser-giacomi, Enrico, Seyler, Frederique, Shah, Rashmi, Sharma, Rashmi, Shaw, Andrew, Shepherd, Andrew, Shriver, Jay, Shum, C.K., Simons, Wim, Simonsen, Sebatian B., Slater, Thomas, Smith, Walter, Soares, Saulo, Sokolovskiy, Mikhail, Soudarin, Laurent, Spatar, Ciprian, Speich, Sabrina, Srinivasan, Margaret, Srokosz, Meric, Stanev, Emil, Staneva, Joanna, Steunou, Nathalie, Stroeve, Julienne, Su, Bob, Sulistioadi, Yohanes Budi, Swain, Debadatta, Sylvestre-baron, Annick, Taburet, Nicolas, Tailleux, Rémi, Takayama, Katsumi, Tapley, Byron, Tarpanelli, Angelica, Tavernier, Gilles, Testut, Laurent, Thakur, Praveen K., Thibaut, Pierre, Thompson, LuAnne, Tintoré, Joaquín, Tison, Céline, Tourain, Cédric, Tournadre, Jean, Townsend, Bill, Tran, Ngan, Trilles, Sébastien, Tsamados, Michel, Tseng, Kuo-Hsin, Ubelmann, Clément, Uebbing, Bernd, Vergara, Oscar, Verron, Jacques, Vieira, Telmo, Vignudelli, Stefano, Vinogradova Shiffer, Nadya, Visser, Pieter, Vivier, Frederic, Volkov, Denis, von Schuckmann, Karina, Vuglinskii, Valerii, Vuilleumier, Pierrik, Walter, Blake, Wang, Jida, Wang, Chao, Watson, Christopher, Wilkin, John, Willis, Josh, Wilson, Hilary, Woodworth, Philip, Yang, Kehan, Yao, Fangfang, Zaharia, Raymond, Zakharova, Elena, Zaron, Edward D., Zhang, Yongsheng, Zhao, Zhongxiang, Zinchenko, Vadim, and Zlotnicki, Victor

Published
2021
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