<title>3D foveated visualization on the Web</title>
Bibliographic record
Abstract
Recent developments in Internet technology, combined with the computerization of hospital radiology departments, allow the remote viewing of medical data images. Generally, however, medical images are data intensive and the transmission of such images over a network can consumer large amounts of network resources. Previous work by Liptay et al, presented an interactive, progressive program (implemented in JAVA and requiring a web browser) that allowed the transmission of multi-resolution JPEG image data using various ROI (Region of Interest) strategies in order to minimize Internet bandwidth requirements. This work handles both 2D and 3D image data, but 3D data was treated as a sequence of 2D images, where each 2D image had to be individually requested by the system. The work described in this paper replaces the representation of 3D data as a 2D JPEG image sequence with a single block of lossy 3D image data compressed using wavelets. In a similar fashion, 2D image data is wavelet compressed. Wavelet decomposition has been shown to have consistently better image quality at high compression ratios than other lossy compression methods. We use wavelet compression in a JAVA application program on the server side to construct a lossy low resolution version of the data. As well, high resolution difference sub-blocks of data are also created by the JAVA application; a difference sub-block and the corresponding low resolution lossless data. Transmitting the low resolution image and difference sub-blocks (as requested) only requires a small fraction of the network bandwidth compared to that which would otherwise be needed to transmit the entire lossless data set. The user, via a JAVA applet on the client side, is provided with a number of methods to choose a trajectory (sequence) of regions of interest in the low resolution image. Once the region(s) of interest are chosen, the sub-blocks of image data in the various trajectories are then retrieved and integrated into the low resolution display to provide lossless reconstruction in the regions of interest. Our program significantly reduces download time since extraneous information is not transmitted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.487 | 0.230 |
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".