Bibliographic record
Abstract
Recent attention has been placed on interactive segmentation for specialized tasks where specialist input is required to further amplify the segmentation performance. In this work, we propose a novel interactive segmentation architecture as well as loss function, where user clicks are dynamically altered in size based upon the current segmentation mask. A weight map is formed based upon the users selected regions and is later passed into a deep neural network as a novel weighted loss function. An interactive U-Net (IU-Net) model which applies both foreground and background user clicks as the main method of interaction is employed to evaluate our loss function. As an addition to the IU-Net, we propose the use of a two-stream fusion interactive U-Net (TSFIU-Net) which applies multimodal fusion properties to allow for the propagation of image feature information throughout the architecture. This model is also tested with the same loss function and dynamically changing click sizes to determine the increase in accuracy. We experiment on spleen and colon cancer CT data from the MSD dataset held at MICCAI 2018 and improve the overall segmentation accuracy in comparison to the standard U-Net using our weighted loss function. Dynamic user click sizes enhances accuracy by 8.88% and 2.16% respectively by utilizing only a single user interaction on the IU-Net and by 13.9% and 3.92% on the TSFIU-Net.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".