Deep Orthogonal Multi-Frequency Fusion for Tomogram-Free Diagnosis in Diffuse Optical Imaging
Bibliographic record
Abstract
Identifying breast cancer lesions with a portable diffuse optical tomography (DOT) device can improve early detection while avoiding otherwise unnecessarily invasive, ionizing, and more expensive modalities such as CT, as well as enabling pre-screening efficiency. Critical to this capability is not just the identification of lesions but rather the complex problem of discriminating between malignant and benign lesions. To accurately capture the highly heterogeneous tissue of a cancer lesion embedded in healthy breast tissue with non-invasive DOT, multiple frequencies can be combined to optimize signal penetration and reduce sensitivity to noise. However, these frequency responses can overlap, capture common information, and correlate, potentially confounding reconstruction and downstream end tasks. We show that an orthogonal fusion loss of multi-frequency DOT can improve reconstruction. More importantly, the orthogonal fusion leads to more accurate end-to-end identification of malignant versus benign lesions, illustrating its regularization properties in the multi-frequency input space. While the deployment of portable DOT probes requires a severely constrained computational budget, we show that our raw-to-task model, for direct prediction of the end task from signal, significantly reduces computational complexity without sacrificing accuracy, enabling a high real-time throughput, desiredin medical settings. Furthermore, our results indicate that image reconstruction is not necessary for unbiased classiication of lesions with a balanced accuracy of 77% and 66% on the synthetic dataset and clinical dataset, espectively, using the raw-to-task model. Code is available at https: //github.com/sfu-mial/FuseNet
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".