DALSA: Domain Adaptation for Supervised Learning from Sparsely Annotated MR Images

Основні поняття

The author proposes DALSA as a method to correct sampling selection errors introduced by sparse annotations, facilitating efficient tumor segmentation without sacrificing accuracy.

Анотація

The content discusses the challenges of manual tumor segmentation and introduces DALSA, a domain adaptation technique. It highlights the time efficiency and effectiveness of learning from sparse annotations in MRI images for tumor classification.

Key points include:

• Proposal of DALSA to correct sampling errors in supervised learning.
• Challenges in manual segmentation due to unclear borders and similar tissue appearances.
• Comparison of training methods using complete annotations, random sampling, and sparse annotations.
• Results showing improved segmentation quality with DALSA compared to other methods.
• Application of DALSA in different scenarios and datasets for efficient tumor classification.

The study demonstrates the potential of DALSA in enhancing tumor segmentation processes through domain adaptation techniques.

Статистика

Compared to training on fully labeled data, we reduced the time for labeling and training by a factor greater than 70 and 180 respectively without sacrificing accuracy.
The mean coverage ratio of segmented voxels to brain voxels for the SURs created by rater 1, 2, and 3 were 0.53% ± 0.23%, 0.41% ± 0.11%, and 0.18% ± 0.05%, respectively.

Цитати

"We propose a new method that employs transfer learning techniques to effectively correct sampling selection errors introduced by sparse annotations during supervised learning."
"The proposed method derives high-quality classifiers for different tissue classes from sparse and unambiguous annotations."

Ключові висновки, отримані з

DALSA

by Mich... о arxiv.org 03-13-2024

https://arxiv.org/pdf/2403.07434.pdf

Глибші Запити

How can crowdsourcing be integrated into the labeling process to further reduce resources?

Crowdsourcing can be integrated into the labeling process by leveraging a large number of individuals to annotate medical images. This approach involves distributing small tasks to a crowd of online workers, enabling rapid and cost-effective data annotation. By breaking down the labeling task into smaller units, multiple contributors can work simultaneously on different parts of an image dataset. Crowdsourcing platforms allow for easy management of annotations, quality control mechanisms, and scalability in handling large volumes of data.

What are the implications of using domain adaptation techniques in other medical imaging applications?

The use of domain adaptation techniques in other medical imaging applications has significant implications for improving model performance and generalization across different scenarios. By correcting sampling biases introduced by sparse annotations or variations in imaging setups, domain adaptation ensures that classifiers trained on one set of data perform well on another set with different characteristics. This adaptability enhances the robustness and reliability of automated segmentation algorithms in various clinical settings, leading to more accurate diagnoses and treatment planning.

How might advancements in automated tumor segmentation impact clinical workflows beyond efficiency gains?

Advancements in automated tumor segmentation have far-reaching impacts on clinical workflows beyond efficiency gains:

Improved Accuracy: Automated segmentation reduces human error and variability associated with manual delineation, leading to more precise tumor identification.
Enhanced Treatment Planning: Accurate tumor segmentation provides clinicians with detailed information about tumor size, location, and boundaries essential for treatment planning.
Faster Diagnoses: Automated segmentation speeds up the diagnostic process by quickly analyzing large amounts of imaging data.
Personalized Medicine: Precise tumor delineation enables personalized treatment strategies tailored to individual patient needs.
Research Advancements: Automated segmentation facilitates research by providing standardized quantitative measurements for studying disease progression and treatment outcomes.

These advancements not only streamline clinical processes but also contribute to better patient care outcomes through optimized decision-making based on reliable imaging analysis results.