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Proceedings Paper
Computer Science, Artificial Intelligence
Ali Hatamizadeh et al.
Summary: Fully Convolutional Neural Networks (FCNNs) have been successful in medical image segmentation, but their limited ability to learn long-range dependencies is a challenge. Inspired by transformers in NLP, we propose a novel architecture called UNet Transformers (UNETR) to redefine volumetric medical image segmentation as a sequence prediction problem. By combining transformers and U-shaped network design in the encoder and decoder, we effectively capture global information and achieve semantic segmentation output.
2022 IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV 2022)
(2022)
Article
Computer Science, Artificial Intelligence
Zongwei Zhou et al.
Summary: Transfer learning from natural image to medical image has been established as one of the most practical paradigms in deep learning for medical image analysis. To overcome the limitations of 3D imaging in prominent modalities like CT and MRI, a set of models called Models Genesis have been created to provide better performance in 3D medical imaging applications. The Models Genesis utilize self-supervised learning to automatically learn common anatomical representation, outperforming existing methods in both segmentation and classification tasks.
MEDICAL IMAGE ANALYSIS
(2021)
Article
Biochemical Research Methods
Fabian Isensee et al.
Summary: nnU-Net is a deep learning-based image segmentation method that automatically configures itself for diverse biological and medical image segmentation tasks, offering state-of-the-art performance as an out-of-the-box tool.
Article
Computer Science, Interdisciplinary Applications
Fatemeh Haghighi et al.
Summary: This paper introduces a new concept called TransVW, which aims to improve the annotation efficiency of deep learning in medical image analysis by automatically harvesting visual words and utilizing self-supervised learning. Experimental results demonstrate that TransVW offers higher performance and faster convergence while reducing annotation costs.
IEEE TRANSACTIONS ON MEDICAL IMAGING
(2021)
Article
Computer Science, Interdisciplinary Applications
Yucheng Tang et al.
Summary: Body part regression is a promising new technique that enables content navigation through self-supervised learning, obtaining global quantitative spatial locations from CT scans. The proposed BUSN method, developed without manual annotation, improves prediction consistency and increases total mean Dice score in multi-organ segmentation when introduced as a preprocessing step.
IEEE TRANSACTIONS ON MEDICAL IMAGING
(2021)
Article
Computer Science, Artificial Intelligence
Yucheng Tang et al.
Summary: This paper presents a novel patch-based network with random spatial initialization and statistical fusion for improving performance on abdominal organ segmentation in high-resolution CT. The approach outperforms other state-of-the-art methods and provides a memory-conservative framework for 3D segmentation.
MEDICAL IMAGE ANALYSIS
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Yufan He et al.
Summary: A novel NAS framework focusing on 3D medical image segmentation is proposed, with a flexible multi-path network topology, high search efficiency, and limited GPU memory usage. The method achieves state-of-the-art performance and top ranking on the Medical Segmentation Decathlon (MSD) challenge leaderboard.
2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, CVPR 2021
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Shekoofeh Azizi et al.
Summary: Self-supervised pretraining followed by supervised fine-tuning has been successful in medical image classification, significantly improving accuracy. The Multi-Instance Contrastive Learning method shows advantages in constructing informative positive pairs for self-supervised learning. Big self-supervised models are robust to distribution shift and can efficiently learn even with a small number of labeled medical images.
2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2021)
(2021)
Article
Computer Science, Artificial Intelligence
Jiuwen Zhu et al.
MEDICAL IMAGE ANALYSIS
(2020)
Proceedings Paper
Computer Science, Artificial Intelligence
Qihang Yu et al.
2020 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR)
(2020)
Article
Computer Science, Artificial Intelligence
Liang Chen et al.
MEDICAL IMAGE ANALYSIS
(2019)
Proceedings Paper
Computer Science, Theory & Methods
Holger R. Roth et al.
MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2018, PT IV
(2018)
