Fetching the paper…
Reading the bibliography…
Training segmentation models for medical images continues to be challenging due to the limited availability of data annotations.
The OpenCV Library
G. Bradski · 2000
Earlier work this paper cites.
Current methods in medical image segmentation
Dzung L Pham, Chenyang Xu, and Jerry L Prince · 2000
Earlier work this paper cites.
Two public chest x-ray datasets for computer-aided screening of pulmonary diseases
Stefan Jaeger, Sema Candemir, Sameer Antani, Yì-Xiáng J Wáng, Pu-Xuan Lu, and George Thoma · 2014
Earlier work this paper cites.
The multimodal brain tumor image segmentation benchmark (brats)
Bjoern H Menze, Andras Jakab, Stefan Bauer, Jayashree Kalpathy-Cramer, Keyvan Farahani, Justin Kirby, Yuliya Burren, Nicole Porz, Johannes Slotboom, Roland Wiest, et al · 2014
Earlier work this paper cites.
Computer-aided detection and diagnosis for prostate cancer based on mono and multi-parametric mri: a review
Guillaume Lemaître, Robert Martí, Jordi Freixenet, Joan C Vilanova, Paul M Walker, and Fabrice Meriaudeau · 2015
Earlier work this paper cites.
Ultrasound nerve segmentation, 2016
Montoya Anna, Hasnin, kaggle446, shirzad, Cukierski Will, and yffud · 2016
Earlier work this paper cites.
Cnn-based segmentation of medical imaging data
Baris Kayalibay, Grady Jensen, and Patrick van der Smagt · 2017
Earlier work this paper cites.
Spinal cord grey matter segmentation challenge
Ferran Prados, John Ashburner, Claudia Blaiotta, Tom Brosch, Julio Carballido-Gamio, Manuel Jorge Cardoso, Benjamin N Conrad, Esha Datta, Gergely Dávid, Benjamin De Leener, et al · 2017
Earlier work this paper cites.
Medical image analysis using convolutional neural networks: a review
Syed Muhammad Anwar, Muhammad Majid, Adnan Qayyum, Muhammad Awais, Majdi Alnowami, and Muhammad Khurram Khan · 2018
Earlier work this paper cites.
Iteratively trained interactive segmentation
Sabarinath Mahadevan, Paul Voigtlaender, and B. Leibe · 2018
Earlier work this paper cites.
Deep learning for medical image processing: Overview, challenges and the future
Muhammad Imran Razzak, Saeeda Naz, and Ahmad Zaib · 2018
Earlier work this paper cites.
A machine learning approach to radiogenomics of breast cancer: a study of 922 subjects and 529 dce-mri features
Ashirbani Saha, Michael R Harowicz, Lars J Grimm, Connie E Kim, Sujata V Ghate, Ruth Walsh, and Maciej A Mazurowski · 2018
Earlier work this paper cites.
Deep learning techniques for medical image segmentation: achievements and challenges
Mohammad Hesam Hesamian, Wenjing Jia, Xiangjian He, and Paul Kennedy · 2019
Earlier work this paper cites.
CT-ORG: A Dataset of CT Volumes With Multiple Organ Segmentations, 2019
Blaine Rister, Kaushik Shivakumar, Tomomi Nobashi, and Daniel L. Rubin · 2019
Cited alongside, same era.
Amber L Simpson, Michela Antonelli, Spyridon Bakas, Michel Bilello, Keyvan Farahani, Bram Van Ginneken, Annette Kopp-Schneider, Bennett A Landman, Geert Litjens, Bjoern Menze, et al · 2019
Cited alongside, same era.
Deep high-resolution representation learning for visual recognition
Jingdong Wang, Ke Sun, Tianheng Cheng, Borui Jiang, Chaorui Deng, Yang Zhao, Dong Liu, Yadong Mu, Mingkui Tan, Xinggang Wang, Wenyu Liu, and Bin Xiao · 2019
Cited alongside, same era.
Dataset of breast ultrasound images
Walid Al-Dhabyani, Mohammed Gomaa, Hussien Khaled, and Aly Fahmy · 2020
Cited alongside, same era.
Knowledge-aware zero-shot learning: Survey and perspective
Jiaoyan Chen, Yuxia Geng, Zhuo Chen, Ian Horrocks, Jeff Z. Pan, and Huajun Chen · 2021
Cited alongside, same era.
Exploring plain vision transformer backbones for object detection
Yanghao Li, Hanzi Mao, Ross B. Girshick, and Kaiming He · 2022
Later among the works it cites.
Simpleclick: Interactive image segmentation with simple vision transformers
Qin Liu, Zhenlin Xu, Gedas Bertasius, and Marc Niethammer · 2022
Later among the works it cites.
Ct2us: Cross-modal transfer learning for kidney segmentation in ultrasound images with synthesized data
Yuxin Song, Jing Zheng, Long Lei, Zhipeng Ni, Baoliang Zhao, and Ying Hu · 2022
Later among the works it cites.
Qi Zhao, Shuchang Lyu, Wenpei Bai, Linghan Cai, Binghao Liu, Meijing Wu, Xiubo Sang, Min Yang, and Lijiang Chen · 2022
Later among the works it cites.
The liver tumor segmentation benchmark (lits)
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby · 2021
Cited alongside, same era.
X-ray images of the hip joints
Daniel Gut · 2021
Cited alongside, same era.
Deep learning segmentation of transverse musculoskeletal ultrasound images for neuromuscular disease assessment
Francesco Marzola, Nens van Alfen, Jonne Doorduin, and Kristen M Meiburger · 2021
Cited alongside, same era.
Reviving iterative training with mask guidance for interactive segmentation
Konstantin Sofiiuk, Ilya A. Petrov, and Anton Konushin · 2021
Cited alongside, same era.
Segformer: Simple and efficient design for semantic segmentation with transformers
Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M Alvarez, and Ping Luo · 2021
Cited alongside, same era.
Focalclick: Towards practical interactive image segmentation
Xi Chen, Zhiyan Zhao, Yilei Zhang, Manni Duan, Donglian Qi, and Hengshuang Zhao · 2022
Cited alongside, same era.
A whole-body fdg-pet/ct dataset with manually annotated tumor lesions
Sergios Gatidis, Tobias Hepp, Marcel Früh, Christian La Fougère, Konstantin Nikolaou, Christina Pfannenberg, Bernhard Schölkopf, Thomas Küstner, Clemens Cyran, and Daniel Rubin · 2022
Cited alongside, same era.
Patrick Bilic, Patrick Christ, Hongwei Bran Li, Eugene Vorontsov, Avi Ben-Cohen, Georgios Kaissis, Adi Szeskin, Colin Jacobs, Gabriel Efrain Humpire Mamani, Gabriel Chartrand, et al · 2023
Closest in time.
Ruining Deng, Can Cui, Quan Liu, Tianyuan Yao, Lucas W. Remedios, Shunxing Bao, Bennett A. Landman, Lee Wheless, Lori A. Coburn, Keith T. Wilson, Yaohong Wang, Shilin Zhao, Agnes B. Fogo, Haichun Yang, Yucheng Tang, and Yuankai Huo · 2023
Closest in time.
Accuracy of segment-anything model (sam) in medical image segmentation tasks
Sheng He, Rina Bao, Jingpeng Li, P Ellen Grant, and Yangming Ou · 2023
Closest in time.
Segment anything model for medical images?
Yuhao Huang, Xin Yang, Lian Liu, Han Zhou, Ao Chang, Xinrui Zhou, Rusi Chen, Junxuan Yu, Jiongquan Chen, Chaoyu Chen, et al · 2023
Closest in time.
Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C Berg, Wan-Yen Lo, et al · 2023
Closest in time.
Samm (segment any medical model): A 3d slicer integration to sam
Yihao Liu, Jiaming Zhang, Zhangcong She, Amir Kheradmand, and Mehran Armand · 2023
Closest in time.
Segment anything in medical images
Jun Ma and Bo Wang · 2023
Closest in time.
Gpt-4 technical report, 2023
OpenAI · 2023
Closest in time.
A comprehensive survey on pretrained foundation models: A history from bert to chatgpt
Ce Zhou, Qian Li, Chen Li, Jun Yu, Yixin Liu, Guangjing Wang, Kai Zhang, Cheng Ji, Qiben Yan, Lifang He, et al · 2023
Closest in time.