CS$$^2$$: A Controllable and Simultaneous Synthesizer of Images and Annotations with Minimal Human Intervention

Xing, Xiaodan; Huang, Jiahao; Yang, Nan; Wu, Yinzhe; Wang, Chengjia; Gao, Zhifan; Walsh, Simon; Yang, Guang

doi:10.1007/978-3-031-16452-1_1

Cited by 4 publications

(1 citation statement)

References 17 publications

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“…Second, for large medical images, better sampling or training strategies can be used to further improve the resolution of the segmentation results. Furthermore, since the low quality of the images can significantly reduce the segmentation accuracy, more comprehensive data augmentation and data harmonization [56] strategies and larger training datasets can be considered, or a generative network can be used to synthesize highquality images [57,58]. In addition, we plan to validate whether the superior performance of our method also holds in newly released datasets.…”

Section: Discussionmentioning

confidence: 99%

Large-Kernel Attention for 3D Medical Image Segmentation

Nan

Ser

et al. 2023

Cogn Comput

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Automated segmentation of multiple organs and tumors from 3D medical images such as magnetic resonance imaging (MRI) and computed tomography (CT) scans using deep learning methods can aid in diagnosing and treating cancer. However, organs often overlap and are complexly connected, characterized by extensive anatomical variation and low contrast. In addition, the diversity of tumor shape, location, and appearance, coupled with the dominance of background voxels, makes accurate 3D medical image segmentation difficult. In this paper, a novel 3D large-kernel (LK) attention module is proposed to address these problems to achieve accurate multi-organ segmentation and tumor segmentation. The advantages of biologically inspired self-attention and convolution are combined in the proposed LK attention module, including local contextual information, long-range dependencies, and channel adaptation. The module also decomposes the LK convolution to optimize the computational cost and can be easily incorporated into CNNs such as U-Net. Comprehensive ablation experiments demonstrated the feasibility of convolutional decomposition and explored the most efficient and effective network design. Among them, the best Mid-type 3D LK attention-based U-Net network was evaluated on CT-ORG and BraTS 2020 datasets, achieving state-of-the-art segmentation performance when compared to avant-garde CNN and Transformer-based methods for medical image segmentation. The performance improvement due to the proposed 3D LK attention module was statistically validated.

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Section: Discussionmentioning

confidence: 99%

Large-Kernel Attention for 3D Medical Image Segmentation

Nan

Ser

et al. 2023

Cogn Comput

Self Cite

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Self-supervised pre-training with contrastive and masked autoencoder methods for dealing with small datasets in deep learning for medical imaging

Wolf,

Payer,

Lisson

et al. 2023

Sci Rep

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Deep learning in medical imaging has the potential to minimize the risk of diagnostic errors, reduce radiologist workload, and accelerate diagnosis. Training such deep learning models requires large and accurate datasets, with annotations for all training samples. However, in the medical imaging domain, annotated datasets for specific tasks are often small due to the high complexity of annotations, limited access, or the rarity of diseases. To address this challenge, deep learning models can be pre-trained on large image datasets without annotations using methods from the field of self-supervised learning. After pre-training, small annotated datasets are sufficient to fine-tune the models for a specific task. The most popular self-supervised pre-training approaches in medical imaging are based on contrastive learning. However, recent studies in natural image processing indicate a strong potential for masked autoencoder approaches. Our work compares state-of-the-art contrastive learning methods with the recently introduced masked autoencoder approach “SparK” for convolutional neural networks (CNNs) on medical images. Therefore, we pre-train on a large unannotated CT image dataset and fine-tune on several CT classification tasks. Due to the challenge of obtaining sufficient annotated training data in medical imaging, it is of particular interest to evaluate how the self-supervised pre-training methods perform when fine-tuning on small datasets. By experimenting with gradually reducing the training dataset size for fine-tuning, we find that the reduction has different effects depending on the type of pre-training chosen. The SparK pre-training method is more robust to the training dataset size than the contrastive methods. Based on our results, we propose the SparK pre-training for medical imaging tasks with only small annotated datasets.

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3D Brain and Heart Volume Generative Models: A Survey

Liu,

Dwivedi,

Boussaid

et al. 2024

ACM Comput. Surv.

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Generative models such as generative adversarial networks and autoencoders have gained a great deal of attention in the medical field due to their excellent data generation capability. This paper provides a comprehensive survey of generative models for three-dimensional (3D) volumes, focusing on the brain and heart. A new and elaborate taxonomy of unconditional and conditional generative models is proposed to cover diverse medical tasks for the brain and heart: unconditional synthesis, classification, conditional synthesis, segmentation, denoising, detection, and registration. We provide relevant background, examine each task and also suggest potential future directions. A list of the latest publications will be updated on GitHub to keep up with the rapid influx of papers at https://github.com/csyanbin/3D-Medical-Generative-Survey.

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CS$$^2$$: A Controllable and Simultaneous Synthesizer of Images and Annotations with Minimal Human Intervention

Cited by 4 publications

References 17 publications

Large-Kernel Attention for 3D Medical Image Segmentation

Large-Kernel Attention for 3D Medical Image Segmentation

Self-supervised pre-training with contrastive and masked autoencoder methods for dealing with small datasets in deep learning for medical imaging

3D Brain and Heart Volume Generative Models: A Survey

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