Reconstrução de um modelo 3D a partir de imagens térmicas 2D de uma mama via câmera infravermelha
Bibliographic record
Abstract
With the development of image processing algorithms and the arrival of thermal cameras, a new concept of temperature analysis emerged. Besides industrial processes, thermal images also started to play an important role in the evaluation of the temperature of human beings, being used for the diagnosis of diseases and other anomalies in which the body undergoes a change in its considered normal temperature. Having a metabolism different from the cells of the human body, the tumor cells cause the temperature of the region where they are to be, also, different from that found in non-tumor cells. This work aims to develop three-dimensional models with temperature information from two-dimensional thermal. With the three-dimensional thermal maps, we can have a closer view of the real geometries found in the images and, with this, new information for the application of the bio-heat transfer equations in numeric simulations also involving geometric parameters of the breast. Using the stereoscopic view, which is based on epipolar geometry, it was possible to develop a method of generating representative dense points clouds from thermal images, as well as verifying the developed method particularities and limitations. The developed three-dimensional maps showed well-defined and visible characteristics, although the input images had low spatial resolution, which limited the application of the method.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".