Feature-Guided CNN for Denoising Images From Portable Ultrasound Devices
Bibliographic record
Abstract
As a non-invasive medical imaging scanning device, ultrasound has greatly increased the efficiency and accuracy of medical diagnosis. In recent years, portable ultrasound is being more widely used for its convenience and lower cost. Patients and physicians can receive the scanned images on their mobile phones at any time via a wireless network with low latency. However, it is difficult for portable ultrasound devices to capture images with the same quality as standard hospital ultrasound image acquisition systems. Usually, the images captured by portable ultrasound equipment have considerable noise. This noise undoubtedly affects the diagnosis of the physician. It is imperative to develop methods to remove the noise while preserving important information in the image. For this reason, we propose a novel denoising neural network model, called Feature-guided Denoising Convolutional Neural Network (FDCNN), to remove noise while retaining important feature information. In order to achieve high-quality denoising results, we employ a hierarchical denoising framework driven by a feature masking layer for medical images. Furthermore, we propose a feature extraction algorithm based on Explainable Artificial Intelligence (XAI) for medical images. Experimental results show that our medical image feature extraction method outperforms previous methods. Combined with the new denoising neural network architecture, portable ultrasound devices can now achieve better diagnostic performance.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".