LPD-Net: A Lightweight and Efficient Deep Learning Model for Accurate Colorectal Polyp Segmentation
Bibliographic record
Abstract
Accurate colorectal polyp segmentation is crucial for the early detection and prevention of colorectal cancer, one of the leading causes of cancer-related deaths worldwide. While colonoscopy remains the most reliable screening method, it is time-consuming, resource-intensive, and highly dependent on the operator, which can lead to variability in diagnosis and potential delays. Deep learning models have shown great potential in automating polyp detection, but their large size and high computational demands make them impractical for real-time clinical use. To overcome these challenges, we introduce LPD-Net, a lightweight and efficient alternative to DUCK-Net that reduces computational complexity while maintaining high segmentation accuracy. This is achieved by optimizing the network architecture, reducing the number of residual blocks, and leveraging depthwise and pointwise convolutions. Our model strikes a balance between performance and computational efficiency. With robust preprocessing and test-time augmentation, LPD-Net achieves state-of-the-art segmentation on CVC-ClinicDB and Kvasir-SEG while remaining lightweight.Clinical RelevanceEarly and precise polyp segmentation is essential for effective colorectal cancer screening and treatment. LPD-Net ensures high segmentation accuracy while significantly reducing parameters, enabling real-time analysis of colonoscopy images. Its lightweight design lowers computational costs, making it suitable for resource-limited settings. By enhancing segmentation efficiency and robustness, LPD-Net supports faster and more reliable polyp assessment, aiding timely medical intervention and improved patient outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".