Boosting Semi-supervised Image Segmentation with Global and Local Mutual\n Information Regularization
Bibliographic record
Abstract
The scarcity of labeled data often impedes the application of deep learning\nto the segmentation of medical images. Semi-supervised learning seeks to\novercome this limitation by exploiting unlabeled examples in the learning\nprocess. In this paper, we present a novel semi-supervised segmentation method\nthat leverages mutual information (MI) on categorical distributions to achieve\nboth global representation invariance and local smoothness. In this method, we\nmaximize the MI for intermediate feature embeddings that are taken from both\nthe encoder and decoder of a segmentation network. We first propose a global MI\nloss constraining the encoder to learn an image representation that is\ninvariant to geometric transformations. Instead of resorting to\ncomputationally-expensive techniques for estimating the MI on continuous\nfeature embeddings, we use projection heads to map them to a discrete cluster\nassignment where MI can be computed efficiently. Our method also includes a\nlocal MI loss to promote spatial consistency in the feature maps of the decoder\nand provide a smoother segmentation. Since mutual information does not require\na strict ordering of clusters in two different assignments, we incorporate a\nfinal consistency regularization loss on the output which helps align the\ncluster labels throughout the network. We evaluate the method on four\nchallenging publicly-available datasets for medical image segmentation.\nExperimental results show our method to outperform recently-proposed approaches\nfor semi-supervised segmentation and provide an accuracy near to full\nsupervision while training with very few annotated images.\n
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".