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Semantic image segmentation, the process of classifying each pixel in an image into a particular class, plays an important role in many visual understanding systems.
Features of similarity
Amos Tversky · 1977
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Conditional random fields: Probabilistic models for segmenting and labeling sequence data
John D. Lafferty, Andrew McCallum, and Fernando Pereira · 2001
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Revisiting squared-error and cross-entropy functions for training neural network classifiers
Doug M. Kline and Victor L. Berardi · 2005
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Efficient inference in fully connected crfs with gaussian edge potentials
Philipp Krähenbühl and Vladlen Koltun · 2011
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Simultaneous detection and segmentation
Bharath Hariharan, Pablo Arbeláez, Ross Girshick, and Jitendra Malik · 2014
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Deep convolutional encoder networks for multiple sclerosis lesion segmentation
T. Brosch, Youngjin Yoo, Lisa Tang, David K.B. Li, Anthony L. Traboulsee, and Roger C. Tam · 2015
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U-net: Convolutional networks for biomedical image segmentation, 2015
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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The cityscapes dataset for semantic urban scene understanding
M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, U. Franke, S. Roth, and B. Schiele · 2016
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Bridging category-level and instance-level semantic image segmentation
Zifeng Wu, Chunhua Shen, and Anton van den Hengel · 2016
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Fully convolutional neural networks for volumetric medical image segmentation
F Milletari, N Navab, SA Ahmadi, and V-net · 2016
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Optimizing intersection-over-union in deep neural networks for image segmentation
Md.Atiqur Rahman and Yang Wang · 2016
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Focal loss for dense object detection
Tsung-Yi Lin, Priya Goyal, Ross B. Girshick, Kaiming He, and Piotr Dollár · 2017
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The lovasz-softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks
Maxim Berman, A. Triki, and Matthew B. Blaschko · 2017
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Tversky loss function for image segmentation using 3d fully convolutional deep networks
Seyed Sadegh Mohseni Salehi, Deniz Erdogmus, and Ali Gholipour · 2017
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Boundary-aware instance segmentation
Zeeshan Hayder, Xuming He, and Mathieu Salzmann · 2017
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Loss max-pooling for semantic image segmentation
Samuel Rota Bulo, Gerhard Neuhold, and Peter Kontschieder · 2017
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Panoptic segmentation
Alexander Kirillov, Kaiming He, Ross B. Girshick, Carsten Rother, and Piotr Dollár · 2018
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Generalised wasserstein dice score for imbalanced multi-class segmentation using holistic convolutional networks
Lucas Fidon, Wenqi Li, Luis C Garcia-Peraza-Herrera, Jinendra Ekanayake, Neil Kitchen, Sébastien Ourselin, and Tom Vercauteren · 2018
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A novel focal tversky loss function with improved attention u-net for lesion segmentation
Nabila Abraham and Naimul Mefraz Khan · 2018
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Boundary loss for highly unbalanced segmentation
Hoel Kervadec, Jihene Bouchtiba, Christian Desrosiers, Eric Granger, José Dolz, and Ismail Ben Ayed · 2018
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Combo loss: Handling input and output imbalance in multi-organ segmentation
Saeid Asgari Taghanaki, Yefeng Zheng, Shaohua Kevin Zhou, Bogdan Georgescu, Puneet S. Sharma, Daguang Xu, Dorin Comaniciu, and G. Hamarneh · 2018
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3d segmentation with exponential logarithmic loss for highly unbalanced object sizes
Ken C. L. Wong, Mehdi Moradi, Hui Tang, and Tanveer F. Syeda-Mahmood · 2018
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nnu-net: Self-adapting framework for u-net-based medical image segmentation
Fabian Isensee, Jens Petersen, Andre Klein, David Zimmerer, Paul F Jaeger, Simon Kohl, Jakob Wasserthal, Gregor Koehler, Tobias Norajitra, Sebastian Wirkert, et al · 2018
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Distance map loss penalty term for semantic segmentation
Francesco Calivá, Claudia Iriondo, Alejandro Morales Martinez, Sharmila Majumdar, and Valentina Pedoia · 2019
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Region mutual information loss for semantic segmentation
Shuai Zhao, Yang Wang, Zheng Yang, and Deng Cai · 2019
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Reducing the hausdorff distance in medical image segmentation with convolutional neural networks
Davood Karimi and Septimiu E. Salcudean · 2019
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Global aggregation then local distribution in fully convolutional networks
Xiangtai Li, Li Zhang, Ansheng You, Maoke Yang, Kuiyuan Yang, and Yunhai Tong · 2019
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Prior-aware neural network for partially-supervised multi-organ segmentation
Yuyin Zhou, Zhe Li, Song Bai, Chong Wang, Xinlei Chen, Mei Han, Elliot Fishman, and Alan L Yuille · 2019
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A survey of loss functions for semantic segmentation
Shruti Jadon · 2020
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A survey of loss functions for semantic segmentation
Shruti Jadon · 2020
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Object-contextual representations for semantic segmentation
Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation
Michael Yeung, Evis Sala, Carola-Bibiane Schönlieb, and Leonardo Rundo · 2022
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Vision transformer adapter for dense predictions
Zhe Chen, Yuchen Duan, Wenhai Wang, Junjun He, Tong Lu, Jifeng Dai, and Yu Qiao · 2022
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Phtrans: Parallelly aggregating global and local representations for medical image segmentation
Wentao Liu, Tong Tian, Weijin Xu, Huihua Yang, Xipeng Pan, Songlin Yan, and Lemeng Wang · 2022
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Self-supervised pre-training of swin transformers for 3d medical image analysis
Yucheng Tang, Dong Yang, Wenqi Li, Holger R Roth, Bennett Landman, Daguang Xu, Vishwesh Nath, and Ali Hatamizadeh · 2022
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Pixel-wise triplet learning for enhancing boundary discrimination in medical image segmentation
Yang Wen, Leiting Chen, Yu Deng, Zhong Zhang, and Chuan Zhou · 2022
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Yuhui Yuan, Xilin Chen, and Jingdong Wang · 2020
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Panoptic-deeplab: A simple, strong, and fast baseline for bottom-up panoptic segmentation
Bowen Cheng, Maxwell D Collins, Yukun Zhu, Ting Liu, Thomas S Huang, Hartwig Adam, and Liang-Chieh Chen · 2020
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Introducing the boundary-aware loss for deep image segmentation
Minh Ôn Vû Ngoc, Yizi Chen, Nicolas Boutry, Joseph Chazalon, Edwin Carlinet, Jonathan Fabrizio, Clément Mallet, and Thierry Géraud · 2021
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Inverseform: A loss function for structured boundary-aware segmentation
Shubhankar Borse, Ying Wang, Yizhe Zhang, and Fatih Murat Porikli · 2021
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Dcnas: Densely connected neural architecture search for semantic image segmentation
Xiong Zhang, Hongmin Xu, Hong Mo, Jianchao Tan, Cheng Yang, Lei Wang, and Wenqi Ren · 2021
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Regularized frank-wolfe for dense crfs: Generalizing mean field and beyond
D Khu L Huu and Karteek Alahari · 2021
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Hs3: Learning with proper task complexity in hierarchically supervised semantic segmentation
Shubhankar Borse, Hong Cai, Yizhe Zhang, and Fatih Porikli · 2021
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High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
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Foundational models in medical imaging: A comprehensive survey and future vision
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Region-wise loss for biomedical image segmentation
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