Improving Interactive Segmentation using a Novel Weighted Loss Function with an Adaptive Click Size and Two-Stream Fusion
Bibliographic record
Abstract
Interactive segmentation has recently attracted at-tention for specialized segmentation tasks where expert input is required to further enhance the segmentation performance. In this work, we propose a novel interactive segmentation framework, where user clicks are dynamically adapted in size based on the current segmentation mask. The clicked regions form a weight map and are fed to a deep neural network together with the image, where the network learns to discriminate important regions through a novel weighted loss function. To evaluate our loss function, a state-of-the-art interactive V-Net (IV-Net) model which utilizes both foreground and background user clicks as the main method of interaction is employed. To further improve on the IV-Net, we propose the use of a two-stream fusion interactive If-Net (TSFIV-Net) which applies multimodal fusion to allow for the propagation of image feature information throughout the architecture. We train and validate both the models on the BCV dataset, while testing on both seen and unseen structures from the MSD dataset to determine the models generalization and segmentation abilities in comparison to the standard IV-Net. Applying adaptive user click sizes increases the overall dice score by 4.86 % and 8.59 % for seen and unseen structures respectively by utilizing only a single user interaction on the IV-Net compared to the original version, and 9.88% and 10.35% on the TSFIV-Net.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".