Segmentation based encryption method for medical images
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The increasing need for telemedicine in healthcare industry created a great necessity to secure the transmitted data among medical centers. Medical image encryption (MIE) is an important technique to achieve security for medical images. Many researchers use advanced encryption standard (AES) to ensure the security of medical images. Applying AES encryption method for medical images directly leads to a long processing time; also it results in obvious background regions, which are considered flaws. In this paper we apply information theory (IT) to identify the two regions of a medical image: the region of interest (ROI) and the region of background (ROB). In order to reduce the processing time needed to protect a medical image using AES with a higher level of security, we propose a hybrid encryption, where AES is applied for ROI and a coding method such as Gold code (GC) is applied for the ROB after improvement. The proposed method has a shorter processing time than applying AES for the whole medical image. In addition, it has better security as seen in the related entropy and correlation calculations.
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.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it