no code implementations • 5 May 2024 • Fadillah Maani, Anees Ur Rehman Hashmi, Numan Saeed, Mohammad Yaqub
This work proposes a brain tumor segmentation method as part of the BraTS-GoAT challenge.
no code implementations • 21 Apr 2024 • Nada Saadi, Numan Saeed, Mohammad Yaqub, Karthik Nandakumar
In this work, we propose a parameter-efficient multi-modal adaptation (PEMMA) framework for lightweight upgrading of a transformer-based segmentation model trained only on CT scans to also incorporate PET scans.
no code implementations • 25 Mar 2024 • Kudaibergen Abutalip, Numan Saeed, Ikboljon Sobirov, Vincent Andrearczyk, Adrien Depeursinge, Mohammad Yaqub
Deploying deep learning (DL) models in medical applications relies on predictive performance and other critical factors, such as conveying trustworthy predictive uncertainty.
no code implementations • 19 Mar 2024 • Muhammad Ridzuan, Mai Kassem, Numan Saeed, Ikboljon Sobirov, Mohammad Yaqub
This paper introduces HuLP, a Human-in-the-Loop for Prognosis model designed to enhance the reliability and interpretability of prognostic models in clinical contexts, especially when faced with the complexities of missing covariates and outcomes.
no code implementations • 15 Mar 2024 • Fadillah Adamsyah Maani, Numan Saeed, Aleksandr Matsun, Mohammad Yaqub
Deep learning (DL) models have been advancing automatic medical image analysis on various modalities, including echocardiography, by offering a comprehensive end-to-end training pipeline.
no code implementations • 15 Mar 2024 • Numan Saeed, Muhammad Ridzuan, Fadillah Adamsyah Maani, Hussain Alasmawi, Karthik Nandakumar, Mohammad Yaqub
Predicting the likelihood of survival is of paramount importance for individuals diagnosed with cancer as it provides invaluable information regarding prognosis at an early stage.
1 code implementation • 14 Mar 2024 • Aleksandr Matsun, Numan Saeed, Fadillah Adamsyah Maani, Mohammad Yaqub
We evaluate our method on the complex task of multicenter histopathology image classification, comparing it against state-of-the-art (SOTA) SDG baselines.
no code implementations • 14 Mar 2024 • Fadillah Maani, Anees Ur Rehman Hashmi, Mariam Aljuboory, Numan Saeed, Ikboljon Sobirov, Mohammad Yaqub
This study outlines our methodology for segmenting tumors in the context of two distinct tasks from the BraTS 2023 challenge: Adult Glioma and Pediatric Tumors.
no code implementations • 30 Sep 2023 • Fadillah Maani, Asim Ukaye, Nada Saadi, Numan Saeed, Mohammad Yaqub
From calculating biomarkers such as ejection fraction to the probability of a patient's heart failure, accurate segmentation of the heart structures allows doctors to assess the heart's condition and devise treatments with greater precision and accuracy.
Ranked #1 on LV Segmentation on Echonet-Dynamic
no code implementations • 30 May 2023 • Numan Saeed, Muhammad Ridzuan, Roba Al Majzoub, Mohammad Yaqub
Medical image segmentation is a vital healthcare endeavor requiring precise and efficient models for appropriate diagnosis and treatment.
1 code implementation • 22 Apr 2023 • Kudaibergen Abutalip, Numan Saeed, Mustaqeem Khan, Abdulmotaleb El Saddik
This distribution shift can negatively impact the performance of deep learning models on unseen samples, presenting a significant challenge for developing new computational pathology applications.
no code implementations • 3 Apr 2023 • Numan Saeed, Muhammad Ridzuan, Hussain Alasmawi, Ikboljon Sobirov, Mohammad Yaqub
The number of studies on deep learning for medical diagnosis is expanding, and these systems are often claimed to outperform clinicians.
no code implementations • CVPR 2023 • Matthias Eisenmann, Annika Reinke, Vivienn Weru, Minu Dietlinde Tizabi, Fabian Isensee, Tim J. Adler, Sharib Ali, Vincent Andrearczyk, Marc Aubreville, Ujjwal Baid, Spyridon Bakas, Niranjan Balu, Sophia Bano, Jorge Bernal, Sebastian Bodenstedt, Alessandro Casella, Veronika Cheplygina, Marie Daum, Marleen de Bruijne, Adrien Depeursinge, Reuben Dorent, Jan Egger, David G. Ellis, Sandy Engelhardt, Melanie Ganz, Noha Ghatwary, Gabriel Girard, Patrick Godau, Anubha Gupta, Lasse Hansen, Kanako Harada, Mattias Heinrich, Nicholas Heller, Alessa Hering, Arnaud Huaulmé, Pierre Jannin, Ali Emre Kavur, Oldřich Kodym, Michal Kozubek, Jianning Li, Hongwei Li, Jun Ma, Carlos Martín-Isla, Bjoern Menze, Alison Noble, Valentin Oreiller, Nicolas Padoy, Sarthak Pati, Kelly Payette, Tim Rädsch, Jonathan Rafael-Patiño, Vivek Singh Bawa, Stefanie Speidel, Carole H. Sudre, Kimberlin Van Wijnen, Martin Wagner, Donglai Wei, Amine Yamlahi, Moi Hoon Yap, Chun Yuan, Maximilian Zenk, Aneeq Zia, David Zimmerer, Dogu Baran Aydogan, Binod Bhattarai, Louise Bloch, Raphael Brüngel, Jihoon Cho, Chanyeol Choi, Qi Dou, Ivan Ezhov, Christoph M. Friedrich, Clifton Fuller, Rebati Raman Gaire, Adrian Galdran, Álvaro García Faura, Maria Grammatikopoulou, SeulGi Hong, Mostafa Jahanifar, Ikbeom Jang, Abdolrahim Kadkhodamohammadi, Inha Kang, Florian Kofler, Satoshi Kondo, Hugo Kuijf, Mingxing Li, Minh Huan Luu, Tomaž Martinčič, Pedro Morais, Mohamed A. Naser, Bruno Oliveira, David Owen, Subeen Pang, Jinah Park, Sung-Hong Park, Szymon Płotka, Elodie Puybareau, Nasir Rajpoot, Kanghyun Ryu, Numan Saeed, Adam Shephard, Pengcheng Shi, Dejan Štepec, Ronast Subedi, Guillaume Tochon, Helena R. Torres, Helene Urien, João L. Vilaça, Kareem Abdul Wahid, Haojie Wang, Jiacheng Wang, Liansheng Wang, Xiyue Wang, Benedikt Wiestler, Marek Wodzinski, Fangfang Xia, Juanying Xie, Zhiwei Xiong, Sen yang, Yanwu Yang, Zixuan Zhao, Klaus Maier-Hein, Paul F. Jäger, Annette Kopp-Schneider, Lena Maier-Hein
The "typical" lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning.
no code implementations • 16 Dec 2022 • Matthias Eisenmann, Annika Reinke, Vivienn Weru, Minu Dietlinde Tizabi, Fabian Isensee, Tim J. Adler, Patrick Godau, Veronika Cheplygina, Michal Kozubek, Sharib Ali, Anubha Gupta, Jan Kybic, Alison Noble, Carlos Ortiz de Solórzano, Samiksha Pachade, Caroline Petitjean, Daniel Sage, Donglai Wei, Elizabeth Wilden, Deepak Alapatt, Vincent Andrearczyk, Ujjwal Baid, Spyridon Bakas, Niranjan Balu, Sophia Bano, Vivek Singh Bawa, Jorge Bernal, Sebastian Bodenstedt, Alessandro Casella, Jinwook Choi, Olivier Commowick, Marie Daum, Adrien Depeursinge, Reuben Dorent, Jan Egger, Hannah Eichhorn, Sandy Engelhardt, Melanie Ganz, Gabriel Girard, Lasse Hansen, Mattias Heinrich, Nicholas Heller, Alessa Hering, Arnaud Huaulmé, Hyunjeong Kim, Bennett Landman, Hongwei Bran Li, Jianning Li, Jun Ma, Anne Martel, Carlos Martín-Isla, Bjoern Menze, Chinedu Innocent Nwoye, Valentin Oreiller, Nicolas Padoy, Sarthak Pati, Kelly Payette, Carole Sudre, Kimberlin Van Wijnen, Armine Vardazaryan, Tom Vercauteren, Martin Wagner, Chuanbo Wang, Moi Hoon Yap, Zeyun Yu, Chun Yuan, Maximilian Zenk, Aneeq Zia, David Zimmerer, Rina Bao, Chanyeol Choi, Andrew Cohen, Oleh Dzyubachyk, Adrian Galdran, Tianyuan Gan, Tianqi Guo, Pradyumna Gupta, Mahmood Haithami, Edward Ho, Ikbeom Jang, Zhili Li, Zhengbo Luo, Filip Lux, Sokratis Makrogiannis, Dominik Müller, Young-tack Oh, Subeen Pang, Constantin Pape, Gorkem Polat, Charlotte Rosalie Reed, Kanghyun Ryu, Tim Scherr, Vajira Thambawita, Haoyu Wang, Xinliang Wang, Kele Xu, Hung Yeh, Doyeob Yeo, Yixuan Yuan, Yan Zeng, Xin Zhao, Julian Abbing, Jannes Adam, Nagesh Adluru, Niklas Agethen, Salman Ahmed, Yasmina Al Khalil, Mireia Alenyà, Esa Alhoniemi, Chengyang An, Talha Anwar, Tewodros Weldebirhan Arega, Netanell Avisdris, Dogu Baran Aydogan, Yingbin Bai, Maria Baldeon Calisto, Berke Doga Basaran, Marcel Beetz, Cheng Bian, Hao Bian, Kevin Blansit, Louise Bloch, Robert Bohnsack, Sara Bosticardo, Jack Breen, Mikael Brudfors, Raphael Brüngel, Mariano Cabezas, Alberto Cacciola, Zhiwei Chen, Yucong Chen, Daniel Tianming Chen, Minjeong Cho, Min-Kook Choi, Chuantao Xie Chuantao Xie, Dana Cobzas, Julien Cohen-Adad, Jorge Corral Acero, Sujit Kumar Das, Marcela de Oliveira, Hanqiu Deng, Guiming Dong, Lars Doorenbos, Cory Efird, Sergio Escalera, Di Fan, Mehdi Fatan Serj, Alexandre Fenneteau, Lucas Fidon, Patryk Filipiak, René Finzel, Nuno R. Freitas, Christoph M. Friedrich, Mitchell Fulton, Finn Gaida, Francesco Galati, Christoforos Galazis, Chang Hee Gan, Zheyao Gao, Shengbo Gao, Matej Gazda, Beerend Gerats, Neil Getty, Adam Gibicar, Ryan Gifford, Sajan Gohil, Maria Grammatikopoulou, Daniel Grzech, Orhun Güley, Timo Günnemann, Chunxu Guo, Sylvain Guy, Heonjin Ha, Luyi Han, Il Song Han, Ali Hatamizadeh, Tian He, Jimin Heo, Sebastian Hitziger, SeulGi Hong, Seungbum Hong, Rian Huang, Ziyan Huang, Markus Huellebrand, Stephan Huschauer, Mustaffa Hussain, Tomoo Inubushi, Ece Isik Polat, Mojtaba Jafaritadi, SeongHun Jeong, Bailiang Jian, Yuanhong Jiang, Zhifan Jiang, Yueming Jin, Smriti Joshi, Abdolrahim Kadkhodamohammadi, Reda Abdellah Kamraoui, Inha Kang, Junghwa Kang, Davood Karimi, April Khademi, Muhammad Irfan Khan, Suleiman A. Khan, Rishab Khantwal, Kwang-Ju Kim, Timothy Kline, Satoshi Kondo, Elina Kontio, Adrian Krenzer, Artem Kroviakov, Hugo Kuijf, Satyadwyoom Kumar, Francesco La Rosa, Abhi Lad, Doohee Lee, Minho Lee, Chiara Lena, Hao Li, Ling Li, Xingyu Li, Fuyuan Liao, Kuanlun Liao, Arlindo Limede Oliveira, Chaonan Lin, Shan Lin, Akis Linardos, Marius George Linguraru, Han Liu, Tao Liu, Di Liu, Yanling Liu, João Lourenço-Silva, Jingpei Lu, Jiangshan Lu, Imanol Luengo, Christina B. Lund, Huan Minh Luu, Yi Lv, Uzay Macar, Leon Maechler, Sina Mansour L., Kenji Marshall, Moona Mazher, Richard McKinley, Alfonso Medela, Felix Meissen, Mingyuan Meng, Dylan Miller, Seyed Hossein Mirjahanmardi, Arnab Mishra, Samir Mitha, Hassan Mohy-ud-Din, Tony Chi Wing Mok, Gowtham Krishnan Murugesan, Enamundram Naga Karthik, Sahil Nalawade, Jakub Nalepa, Mohamed Naser, Ramin Nateghi, Hammad Naveed, Quang-Minh Nguyen, Cuong Nguyen Quoc, Brennan Nichyporuk, Bruno Oliveira, David Owen, Jimut Bahan Pal, Junwen Pan, Wentao Pan, Winnie Pang, Bogyu Park, Vivek Pawar, Kamlesh Pawar, Michael Peven, Lena Philipp, Tomasz Pieciak, Szymon Plotka, Marcel Plutat, Fattaneh Pourakpour, Domen Preložnik, Kumaradevan Punithakumar, Abdul Qayyum, Sandro Queirós, Arman Rahmim, Salar Razavi, Jintao Ren, Mina Rezaei, Jonathan Adam Rico, ZunHyan Rieu, Markus Rink, Johannes Roth, Yusely Ruiz-Gonzalez, Numan Saeed, Anindo Saha, Mostafa Salem, Ricardo Sanchez-Matilla, Kurt Schilling, Wei Shao, Zhiqiang Shen, Ruize Shi, Pengcheng Shi, Daniel Sobotka, Théodore Soulier, Bella Specktor Fadida, Danail Stoyanov, Timothy Sum Hon Mun, Xiaowu Sun, Rong Tao, Franz Thaler, Antoine Théberge, Felix Thielke, Helena Torres, Kareem A. Wahid, Jiacheng Wang, Yifei Wang, Wei Wang, Xiong Wang, Jianhui Wen, Ning Wen, Marek Wodzinski, Ye Wu, Fangfang Xia, Tianqi Xiang, Chen Xiaofei, Lizhan Xu, Tingting Xue, Yuxuan Yang, Lin Yang, Kai Yao, Huifeng Yao, Amirsaeed Yazdani, Michael Yip, Hwanseung Yoo, Fereshteh Yousefirizi, Shunkai Yu, Lei Yu, Jonathan Zamora, Ramy Ashraf Zeineldin, Dewen Zeng, Jianpeng Zhang, Bokai Zhang, Jiapeng Zhang, Fan Zhang, Huahong Zhang, Zhongchen Zhao, Zixuan Zhao, Jiachen Zhao, Can Zhao, Qingshuo Zheng, Yuheng Zhi, Ziqi Zhou, Baosheng Zou, Klaus Maier-Hein, Paul F. Jäger, Annette Kopp-Schneider, Lena Maier-Hein
Of these, 84% were based on standard architectures.
1 code implementation • 12 Sep 2022 • Numan Saeed, Ikboljon Sobirov, Roba Al Majzoub, Mohammad Yaqub
We propose TMSS, an end-to-end Transformer based Multimodal network for Segmentation and Survival prediction that leverages the superiority of transformers that lies in their abilities to handle different modalities.
no code implementations • 5 May 2022 • Ikboljon Sobirov, Numan Saeed, Mohammad Yaqub
In medical imaging analysis, deep learning has shown promising results.
1 code implementation • 25 Feb 2022 • Numan Saeed, Roba Al Majzoub, Ikboljon Sobirov, Mohammad Yaqub
The main issue when using clinical and imaging data to train a deep learning model is to decide on how to combine the information from these sources.
no code implementations • 16 Jan 2022 • Numan Saeed, Shahad Hardan, Kudaibergen Abutalip, Mohammad Yaqub
A couple of recent publications proposed a connection between the MGMT promoter state and the MRI scans of the tumor and hence suggested the use of deep learning models for this purpose.