Impact of train- and test-time Hounsfield unit window variation on CT segmentation of liver lesions
Bibliographic record
Abstract
Data preprocessing is an important step in a deep learning pipeline. Within the context of computed tomography image segmentation, preprocessing includes a Hounsfield unit (HU) windowing step. HU windowing ensures that the contrast of the region of interest is maximized to highlight the important features for the given task. HU windowing is defined by the window level and width. There are general guidelines for optimal window level and width for a given tissue, however, there’s a certain degree of subjectivity involved within the setting. Moreover, the CT imaging protocol and the type of scan (contrast-enhanced vs. none) has an impact on the HU window. In this paper, we evaluate the impact of varying the HU window level and window width at both training and test time to assess whether liver lesion segmentation models can generalize to different contrasts in input data, as well as a method for estimating uncertainty within the task. The experiments show that HU windowing can have a significant impact on model performance at train and test times. Moreover, we show that HU variation at test time is a computationally cheap alternative to test-time augmentation through spatial transformation for estimating uncertainty.
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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 | 0.000 |
| 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".