Enhancing Semantic Segmentation in Medicine with Low-Resolution Inputs

Główne pojęcia

The author proposes an architecture that leverages high-resolution ground truths to improve prediction quality while using low-resolution inputs efficiently.

Streszczenie

The content discusses the challenges of deploying neural networks in medical devices due to hardware limitations. It introduces a novel architecture that enhances prediction quality by utilizing high-resolution ground truths with low-resolution inputs. The proposed model shows significant improvements in semantic segmentation for cancer detection in MRI images, outperforming existing state-of-the-art frameworks. Extensive experiments on different datasets validate the effectiveness of the approach, showcasing better prediction quality and reduced computational complexity.

Statystyki

Using lower-resolution input leads to a significant reduction in computing and memory requirements.
The proposed model improves prediction quality by 5.5% with less than 200 additional parameters.
Our architecture reaches higher prediction scores with less model complexity compared to standard U-Net and ELU-Net.
The number of GMAC operations increases as input resolution rises, affecting prediction scores.
Our architecture maintains higher prediction quality on lower input resolutions compared to other networks.

Cytaty

"Our architecture dramatically improves the prediction quality of the baseline while not adding any significant computations."
"Our experiments showed that our architecture can maintain higher prediction quality on lower input resolutions than other networks."
"Our architecture allows us to overcome limitations imposed by output resolution."

Kluczowe wnioski z

Embedded Deployment of Semantic Segmentation in Medicine through Low-Resolution Inputs

by Erik Ostrows... o arxiv.org 03-11-2024

https://arxiv.org/pdf/2403.05340.pdf

Głębsze pytania

How can the proposed architecture impact real-world applications beyond medical imaging

The proposed architecture's impact extends beyond medical imaging into various real-world applications. One significant area is in autonomous vehicles, where low-resolution inputs from sensors like cameras can be enhanced using high-resolution ground truths. This enhancement can improve object detection and segmentation accuracy, leading to safer and more efficient self-driving systems. Additionally, in agriculture, the architecture could aid in crop monitoring by enhancing low-quality satellite images with high-resolution ground truth data. This would enable better identification of plant health issues or pest infestations. Moreover, in environmental monitoring, the architecture could enhance low-resolution sensor data with detailed information from high-resolution sources to improve analysis of climate patterns or natural disaster prediction.

What potential drawbacks or limitations might arise from relying on high-resolution ground truths for low-resolution inputs

Relying on high-resolution ground truths for low-resolution inputs may introduce certain drawbacks and limitations. One potential limitation is the increased computational complexity required to process and store the higher resolution ground truth data alongside lower resolution input images. This could lead to higher memory requirements and longer processing times, especially when dealing with large datasets or real-time applications. Another drawback is the dependency on accurate high-resolution ground truths; any inaccuracies or noise in this data could negatively impact model performance when upscaling predictions based on it for lower resolution inputs.

How could advancements in hardware technology influence the scalability and efficiency of this approach

Advancements in hardware technology play a crucial role in influencing the scalability and efficiency of this approach. As hardware capabilities continue to evolve towards more powerful processors and GPUs, models utilizing the proposed architecture can benefit from faster processing speeds and reduced inference times even when handling complex tasks such as upscaling low-resolution inputs using high-quality ground truths. Improved hardware efficiency also enables easier deployment of these models on edge devices or resource-constrained environments without compromising performance quality significantly.