Augmented Image Dataset Using Image-to-Image Translation to Enhance the Non-Hodgkin Lymphoma Diagnosis
Bibliographic record
Abstract
Digital pathology involves the conversion of histology slides into digital format to generate high-resolution images.Tissue classification, particularly for identifying common types of non-Hodgkin lymphomas like mantle cell lymphoma, follicular lymphoma, and chronic lymphocytic leukemia, is a crucial application of this technology.Due to the complexity of these lymphomas, pathologists often encounter challenges in their diagnosis.However, the limited availability of image data in the dataset poses a significant challenge.To solve this issue, we present a new technique for augmenting the dataset and enabling more efficient model training.In this article, we propose a technique capable of approaching lymphoma images.Our idea is to generate lymphoma images by adding a segmented image.The reference images are taken from the available dataset.We use two re neural networks.The first neural network is image-to-image translation, a technique used to transform the appearance or style of images, particularly adversarial neural networks, facilitates this process by using inputs from the source domain to produce images that closely match the target domain.The second neural network, we use an improved convolutional neural network (CNN) algorithm for classifying non-Hodgkin lymphomas.Trained on this augmented dataset, the proposed model achieves a classification accuracy of 99.91%.This precision is higher than that reported by Khelil et al. in their study.Moreover, we acknowledge the ethical implications of generating synthetic medical images and propose guidelines for ensuring the ethical conduct of our proposed technique.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".