Multimodal, Multianatomical and Multidimensional Medical Image Retrieval System
Bibliographic record
Abstract
Recently, there is a rapid use of digital imaging information in healthcare enterprises. Hence, it becomes laborious to manage and query in such large databases which need effi- cient medical image retrieval systems. Also, the multi-modal and multi-dimensional aspects of medical images make this a much more demanding task. The imaging data such as the CT, and MRI from the scanners is in the form of 3D images which consist of several slices stacked upon each other. While medical images such as the X- rays are in the 2D format. This imbalance in the medical image databases leads to de- velop an integrated 2D and 3D medical image retrieval sys- tem using Deep Learning architectures. In this context, an integrated framework with hybrid architectures consisting of convolutional neural networks and autoencoder is proposed. A heterogeneous database comprises of 2D and 3D images produced from different sources of modalities to train the proposed networks is used. The learned features are used to retrieve the medical images. Five unsupervised CNN mod- els namely LeNetCoder, VGGCoder, Noisy VGGCoder, LSTM VGGCoder, and ResCoder were trained and tested for both the 2D and 3D images. Finally, the performances of all the models are compared with the metrics like Precision, Recall, and F-Score. Among them, ResCoder has the highest mean average precision (MAP) of 0.96 for 2D and 0.92 for 3D im- ages in this framework.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".