Segmenting Medical Images with Limited Data
- URL: http://arxiv.org/abs/2407.09189v1
- Date: Fri, 12 Jul 2024 11:41:18 GMT
- Title: Segmenting Medical Images with Limited Data
- Authors: Zhaoshan Liua, Qiujie Lv, Chau Hung Lee, Lei Shen,
- Abstract summary: We present a semi-supervised, consistency-based approach termed the data-efficient medical segmenter (DEMS)
The DEMS features an encoder-decoder architecture and incorporates the developed online automatic augmenter (OAA) and residual robustness enhancement (RRE) blocks.
Under extreme data shortage scenarios, our DEMS achieves 16.85% and 10.37% improvement in dice score compared with the U-Net and top-performed state-of-the-art method, respectively.
- Score: 7.83073107607804
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: While computer vision has proven valuable for medical image segmentation, its application faces challenges such as limited dataset sizes and the complexity of effectively leveraging unlabeled images. To address these challenges, we present a novel semi-supervised, consistency-based approach termed the data-efficient medical segmenter (DEMS). The DEMS features an encoder-decoder architecture and incorporates the developed online automatic augmenter (OAA) and residual robustness enhancement (RRE) blocks. The OAA augments input data with various image transformations, thereby diversifying the dataset to improve the generalization ability. The RRE enriches feature diversity and introduces perturbations to create varied inputs for different decoders, thereby providing enhanced variability. Moreover, we introduce a sensitive loss to further enhance consistency across different decoders and stabilize the training process. Extensive experimental results on both our own and three public datasets affirm the effectiveness of DEMS. Under extreme data shortage scenarios, our DEMS achieves 16.85\% and 10.37\% improvement in dice score compared with the U-Net and top-performed state-of-the-art method, respectively. Given its superior data efficiency, DEMS could present significant advancements in medical segmentation under small data regimes. The project homepage can be accessed at https://github.com/NUS-Tim/DEMS.
Related papers
- Discriminative Hamiltonian Variational Autoencoder for Accurate Tumor Segmentation in Data-Scarce Regimes [2.8498944632323755]
We propose an end-to-end hybrid architecture for medical image segmentation.
We use Hamiltonian Variational Autoencoders (HVAE) and a discriminative regularization to improve the quality of generated images.
Our architecture operates on a slice-by-slice basis to segment 3D volumes, capitilizing on the richly augmented dataset.
arXiv Detail & Related papers (2024-06-17T15:42:08Z) - Adaptive Affinity-Based Generalization For MRI Imaging Segmentation Across Resource-Limited Settings [1.5703963908242198]
This paper introduces a novel relation-based knowledge framework by seamlessly combining adaptive affinity-based and kernel-based distillation.
To validate our innovative approach, we conducted experiments on publicly available multi-source prostate MRI data.
arXiv Detail & Related papers (2024-04-03T13:35:51Z) - Dual-scale Enhanced and Cross-generative Consistency Learning for Semi-supervised Medical Image Segmentation [49.57907601086494]
Medical image segmentation plays a crucial role in computer-aided diagnosis.
We propose a novel Dual-scale Enhanced and Cross-generative consistency learning framework for semi-supervised medical image (DEC-Seg)
arXiv Detail & Related papers (2023-12-26T12:56:31Z) - Rotated Multi-Scale Interaction Network for Referring Remote Sensing Image Segmentation [63.15257949821558]
Referring Remote Sensing Image (RRSIS) is a new challenge that combines computer vision and natural language processing.
Traditional Referring Image (RIS) approaches have been impeded by the complex spatial scales and orientations found in aerial imagery.
We introduce the Rotated Multi-Scale Interaction Network (RMSIN), an innovative approach designed for the unique demands of RRSIS.
arXiv Detail & Related papers (2023-12-19T08:14:14Z) - Pseudo Label-Guided Data Fusion and Output Consistency for
Semi-Supervised Medical Image Segmentation [9.93871075239635]
We propose the PLGDF framework, which builds upon the mean teacher network for segmenting medical images with less annotation.
We propose a novel pseudo-label utilization scheme, which combines labeled and unlabeled data to augment the dataset effectively.
Our framework yields superior performance compared to six state-of-the-art semi-supervised learning methods.
arXiv Detail & Related papers (2023-11-17T06:36:43Z) - EMIT-Diff: Enhancing Medical Image Segmentation via Text-Guided
Diffusion Model [4.057796755073023]
We develop controllable diffusion models for medical image synthesis, called EMIT-Diff.
We leverage recent diffusion probabilistic models to generate realistic and diverse synthetic medical image data.
In our approach, we ensure that the synthesized samples adhere to medically relevant constraints.
arXiv Detail & Related papers (2023-10-19T16:18:02Z) - PREM: A Simple Yet Effective Approach for Node-Level Graph Anomaly
Detection [65.24854366973794]
Node-level graph anomaly detection (GAD) plays a critical role in identifying anomalous nodes from graph-structured data in domains such as medicine, social networks, and e-commerce.
We introduce a simple method termed PREprocessing and Matching (PREM for short) to improve the efficiency of GAD.
Our approach streamlines GAD, reducing time and memory consumption while maintaining powerful anomaly detection capabilities.
arXiv Detail & Related papers (2023-10-18T02:59:57Z) - Genetic InfoMax: Exploring Mutual Information Maximization in
High-Dimensional Imaging Genetics Studies [50.11449968854487]
Genome-wide association studies (GWAS) are used to identify relationships between genetic variations and specific traits.
Representation learning for imaging genetics is largely under-explored due to the unique challenges posed by GWAS.
We introduce a trans-modal learning framework Genetic InfoMax (GIM) to address the specific challenges of GWAS.
arXiv Detail & Related papers (2023-09-26T03:59:21Z) - TransAttUnet: Multi-level Attention-guided U-Net with Transformer for
Medical Image Segmentation [33.45471457058221]
This paper proposes a novel Transformer based medical image semantic segmentation framework called TransAttUnet.
In particular, we establish additional multi-scale skip connections between decoder blocks to aggregate the different semantic-scale upsampling features.
Our method consistently outperforms the state-of-the-art baselines.
arXiv Detail & Related papers (2021-07-12T09:17:06Z) - Learning Representational Invariances for Data-Efficient Action
Recognition [52.23716087656834]
We show that our data augmentation strategy leads to promising performance on the Kinetics-100, UCF-101, and HMDB-51 datasets.
We also validate our data augmentation strategy in the fully supervised setting and demonstrate improved performance.
arXiv Detail & Related papers (2021-03-30T17:59:49Z) - MS-Net: Multi-Site Network for Improving Prostate Segmentation with
Heterogeneous MRI Data [75.73881040581767]
We propose a novel multi-site network (MS-Net) for improving prostate segmentation by learning robust representations.
Our MS-Net improves the performance across all datasets consistently, and outperforms state-of-the-art methods for multi-site learning.
arXiv Detail & Related papers (2020-02-09T14:11:50Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.