FETR: A Weakly Self-Supervised Approach for Fetal Ultrasound Anatomical Detection
Bibliographic record
Abstract
Weakly supervised object detection (WSOD) is a cutting-edge research field within computer vision that aims to detect objects in images with minimal or incomplete annotations. In this regard, we propose a novel WSOD architecture optimized for fetal ultrasound imaging. The model is designed to leverage the inherent capabilities of the convolutional neural network and the transformers to localize and classify fetal anatomical structures within ultrasound images without requiring extensive annotated datasets. Employing a class-agnostic Fetal Transformer (FETR) for generating high-quality object proposals, our approach integrates a Multiple Instance Learning (MIL) framework to enhance detection sensitivity. We conduct thorough experiments on the FPUS23 dataset, incorporating strategic data augmentation techniques to ensure model robustness while maintaining the diagnostic integrity of the images. The efficacy of our method is demonstrated through extensive evaluations, where it achieves superior performance against state-of-the-art models, not only on the FPUS23 dataset but also on the Fetal Plane DB dataset, showcasing its adaptability to various imaging conditions. The results from our ablation studies further validate the significance of each architectural component, with qualitative results emphasizing the model's precision and reliability. Our work sets the stage for advanced prenatal diagnostics, promising to elevate the standards of fetal health monitoring and care.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".