Search Results - "Medical image analysis"

The Liver Tumor Segmentation Benchmark (LiTS) by Bilic, Patrick, Christ, Patrick, Vorontsov, Eugene, Kaissis, Georgios, Szeskin, Adi, Jacobs, Colin, Mamani, Gabriel Efrain Humpire, Chartrand, Gabriel, Lohöfer, Fabian, Holch, Julian Walter, Sommer, Wieland, Hofmann, Felix, Hostettler, Alexandre, Lev-Cohain, Naama, Drozdzal, Michal, Amitai, Michal Marianne, Sosna, Jacob, Ezhov, Ivan, Sekuboyina, Anjany, Navarro, Fernando, Kofler, Florian, Paetzold, Johannes C., Shit, Suprosanna, Hu, Xiaobin, Lipková, Jana, Rempfler, Markus, Kirschke, Jan, Wiestler, Benedikt, Zhang, Zhiheng, Beetz, Marcel, Ettlinger, Florian, Antonelli, Michela, Bellver, Míriam, Bi, Lei, Chen, Hao, Chlebus, Grzegorz, Dam, Erik B., Dou, Qi, Fu, Chi-Wing, Giró-i-Nieto, Xavier, Gruen, Felix, Han, Xu, Heng, Pheng-Ann, Hesser, Jürgen, Moltz, Jan Hendrik, Igel, Christian, Isensee, Fabian, Jäger, Paul, Jia, Fucang, Kaluva, Krishna Chaitanya, Khened, Mahendra, Kim, Ildoo, Kim, Jae-Hun, Kim, Sungwoong, Kohl, Simon, Konopczynski, Tomasz, Kori, Avinash, Li, Fan, Li, Hongchao, Li, Junbo, Li, Xiaomeng, Lowengrub, John, Ma, Jun, Maier-Hein, Klaus, Maninis, Kevis-Kokitsi, Meine, Hans, Merhof, Dorit, Pai, Akshay, Perslev, Mathias, Petersen, Jens, Pont-Tuset, Jordi, Qi, Jin, Qi, Xiaojuan, Rippel, Oliver, Roth, Karsten, Sarasua, Ignacio, Schenk, Andrea, Shen, Zengming, Torres, Jordi, Wachinger, Christian, Wang, Chunliang, Weninger, Leon, Wu, Jianrong, Xu, Daguang, Yang, Xiaoping, Yu, Simon Chun-Ho, Yuan, Yading, Yue, Miao, Zhang, Liping, Cardoso, Jorge, Bakas, Spyridon, Braren, Rickmer, Heinemann, Volker, Pal, Christopher, Tang, An, Kadoury, Samuel, Soler, Luc, van Ginneken, Bram, Greenspan, Hayit, Menze, Bjoern

“…In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International…”
Get full text

Explainable artificial intelligence (XAI) in deep learning-based medical image analysis by van der Velden, Bas H.M., Kuijf, Hugo J., Gilhuijs, Kenneth G.A., Viergever, Max A.

“…•This paper surveys over 200 papers using explainable artificial intelligence (XAI) in deep learning-based medical image analysis.•The surveyed papers are…”
Get full text

Deep-COVID: Predicting COVID-19 from chest X-ray images using deep transfer learning by Minaee, Shervin, Kafieh, Rahele, Sonka, Milan, Yazdani, Shakib, Jamalipour Soufi, Ghazaleh

“…•Preparing a dataset of around 5000 X-ray images for COVID-19 detection.•Training 4 state-of-the-art convolutional networks for COVID-19 detection.•Presenting…”
Get full text

Generative adversarial network in medical imaging: A review by Yi, Xin, Walia, Ekta, Babyn, Paul

“…•The number of publications in medical imaging using adversarial training scheme are increasing rapidly.•By surveying 150 published articles (including…”
Get full text

BS-Net: Learning COVID-19 pneumonia severity on a large chest X-ray dataset by Signoroni, Alberto, Savardi, Mattia, Benini, Sergio, Adami, Nicola, Leonardi, Riccardo, Gibellini, Paolo, Vaccher, Filippo, Ravanelli, Marco, Borghesi, Andrea, Maroldi, Roberto, Farina, Davide

“…•Novel end-to-end multi-network deep learning architecture specifically designed for pneumonia severity assessment on CXRs.•Publicly released large fully…”
Get full text

Attention gated networks: Learning to leverage salient regions in medical images by Schlemper, Jo, Oktay, Ozan, Schaap, Michiel, Heinrich, Mattias, Kainz, Bernhard, Glocker, Ben, Rueckert, Daniel

“…[Display omitted] We propose a novel attention gate (AG) model for medical image analysis that automatically learns to focus on target structures of varying…”
Get full text

Transformers in medical imaging: A survey by Shamshad, Fahad, Khan, Salman, Zamir, Syed Waqas, Khan, Muhammad Haris, Hayat, Munawar, Khan, Fahad Shahbaz, Fu, Huazhu

“…Following unprecedented success on the natural language tasks, Transformers have been successfully applied to several computer vision problems, achieving…”
Get full text

Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation by Tajbakhsh, Nima, Jeyaseelan, Laura, Li, Qian, Chiang, Jeffrey N., Wu, Zhihao, Ding, Xiaowei

“…•Medical image segmentation typically faces limited datasets.•Dataset limitations are broadly grouped into scarce and weak annotations.•Scarce annotations can…”
Get full text

f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks by Schlegl, Thomas, Seeböck, Philipp, Waldstein, Sebastian M., Langs, Georg, Schmidt-Erfurth, Ursula

“…•A fast, generative adversarial network (GAN) based anomaly detection approach.•f−AnoGAN is suitable for real-time anomaly detection applications.•Enables…”
Get full text

Recent advances and clinical applications of deep learning in medical image analysis by Chen, Xuxin, Wang, Ximin, Zhang, Ke, Fung, Kar-Ming, Thai, Theresa C., Moore, Kathleen, Mannel, Robert S., Liu, Hong, Zheng, Bin, Qiu, Yuchen

“…•We especially focused on the latest unsupervised/self-supervised and semi-supervised learning methods in medical image analysis.•We comprehensively summarized…”
Get full text

A survey on deep learning in medical image analysis by Litjens, Geert, Kooi, Thijs, Bejnordi, Babak Ehteshami, Setio, Arnaud Arindra Adiyoso, Ciompi, Francesco, Ghafoorian, Mohsen, van der Laak, Jeroen A.W.M., van Ginneken, Bram, Sánchez, Clara I.

“…•A summary of all deep learning algorithms used in medical image analysis is given.•The most successful algorithms for key image analysis tasks are…”
Get full text

Deep learning with noisy labels: Exploring techniques and remedies in medical image analysis by Karimi, Davood, Dou, Haoran, Warfield, Simon K., Gholipour, Ali

“…•Supervised training of deep learning models requires large labeled datasets.•Label noise can significantly impact the performance of deep learning models.•We…”
Get full text

The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: Results of the KiTS19 challenge by Heller, Nicholas, Isensee, Fabian, Maier-Hein, Klaus H., Hou, Xiaoshuai, Xie, Chunmei, Li, Fengyi, Nan, Yang, Mu, Guangrui, Lin, Zhiyong, Han, Miofei, Yao, Guang, Gao, Yaozong, Zhang, Yao, Wang, Yixin, Hou, Feng, Yang, Jiawei, Xiong, Guangwei, Tian, Jiang, Zhong, Cheng, Ma, Jun, Rickman, Jack, Dean, Joshua, Stai, Bethany, Tejpaul, Resha, Oestreich, Makinna, Blake, Paul, Kaluzniak, Heather, Raza, Shaneabbas, Rosenberg, Joel, Moore, Keenan, Walczak, Edward, Rengel, Zachary, Edgerton, Zach, Vasdev, Ranveer, Peterson, Matthew, McSweeney, Sean, Peterson, Sarah, Kalapara, Arveen, Sathianathen, Niranjan, Papanikolopoulos, Nikolaos, Weight, Christopher

“…•The KiTS19 Challenge measured the state of the art in kidney and tumor segmentation.•Deep 3D CNNs were by far the most popular method used by submissions.•The…”
Get full text

BrainGNN: Interpretable Brain Graph Neural Network for fMRI Analysis by Li, Xiaoxiao, Zhou, Yuan, Dvornek, Nicha, Zhang, Muhan, Gao, Siyuan, Zhuang, Juntang, Scheinost, Dustin, Staib, Lawrence H., Ventola, Pamela, Duncan, James S.

“…Understanding which brain regions are related to a specific neurological disorder or cognitive stimuli has been an important area of neuroimaging research. We…”
Get full text

REFUGE Challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs by Orlando, José Ignacio, Fu, Huazhu, Barbosa Breda, João, van Keer, Karel, Bathula, Deepti R., Diaz-Pinto, Andrés, Fang, Ruogu, Heng, Pheng-Ann, Kim, Jeyoung, Lee, JoonHo, Lee, Joonseok, Li, Xiaoxiao, Liu, Peng, Lu, Shuai, Murugesan, Balamurali, Naranjo, Valery, Phaye, Sai Samarth R., Shankaranarayana, Sharath M., Sikka, Apoorva, Son, Jaemin, van den Hengel, Anton, Wang, Shujun, Wu, Junyan, Wu, Zifeng, Xu, Guanghui, Xu, Yongli, Yin, Pengshuai, Li, Fei, Zhang, Xiulan, Xu, Yanwu, Bogunović, Hrvoje

“…•REFUGE, the first challenge on glaucoma assessment from fundus images.•A data set of 1200 images with reliable ground truth labels is publicly released.•An…”
Get full text

CHAOS Challenge - combined (CT-MR) healthy abdominal organ segmentation by Kavur, A. Emre, Gezer, N. Sinem, Barış, Mustafa, Aslan, Sinem, Conze, Pierre-Henri, Groza, Vladimir, Pham, Duc Duy, Chatterjee, Soumick, Ernst, Philipp, Özkan, Savaş, Baydar, Bora, Lachinov, Dmitry, Han, Shuo, Pauli, Josef, Isensee, Fabian, Perkonigg, Matthias, Sathish, Rachana, Rajan, Ronnie, Sheet, Debdoot, Dovletov, Gurbandurdy, Speck, Oliver, Nürnberger, Andreas, Maier-Hein, Klaus H., Bozdağı Akar, Gözde, Ünal, Gözde, Dicle, Oğuz, Selver, M. Alper

“…•A comprehensive grand-challenge is organized for abdominal organ segmentation.•The results obtained from top models among 1500 participants are analyzed in…”
Get full text

PadChest: A large chest x-ray image dataset with multi-label annotated reports by Bustos, Aurelia, Pertusa, Antonio, Salinas, Jose-Maria, de la Iglesia-Vayá, Maria

“…•A large-scale, labeled high resolution chest x-ray dataset is presented.•Radiographic findings, differential diagnosis and anatomic locations are…”
Get full text

Transformer-based unsupervised contrastive learning for histopathological image classification by Wang, Xiyue, Yang, Sen, Zhang, Jun, Wang, Minghui, Zhang, Jing, Yang, Wei, Huang, Junzhou, Han, Xiao

“…A large-scale and well-annotated dataset is a key factor for the success of deep learning in medical image analysis. However, assembling such large annotations…”
Get full text

Not-so-supervised: A survey of semi-supervised, multi-instance, and transfer learning in medical image analysis by Cheplygina, Veronika, de Bruijne, Marleen, Pluim, Josien P.W.

“…•We discuss different forms of supervision in medical image analysis.•Over 140 papers using semi-supervised, multi-instance or transfer learning are…”
Get full text

Convolutional neural networks for classification of Alzheimer's disease: Overview and reproducible evaluation by Wen, Junhao, Thibeau-Sutre, Elina, Diaz-Melo, Mauricio, Samper-González, Jorge, Routier, Alexandre, Bottani, Simona, Dormont, Didier, Durrleman, Stanley, Burgos, Ninon, Colliot, Olivier

“…•We reviewed the state-of-the-art on classification of AD based on CNN and T1 MRI.•We unveiled data leakage, leading to biased results, in some reviewed…”
Get full text