Fully automated cascaded approach for renal mass detection on T2 weighted MRI images
Bibliographic record
Abstract
Due to the superior soft tissue contrast in magnetic resonance imaging (MRI), MRI may be well suited for renal mass characterization (e.g., benign vs. malignant). Though renal mass detection and characterization using deeplearning (DL) methods have been extensively studied for CT images, those same tasks are yet to be investigated on MR images. Existing algorithms for renal mass characterization require manual segmentation, therefore development of algorithms to localize and detect renal masses is important fully automatically. In this study, we developed a DL-based fully automated renal mass detection model on T2- weighted (T2W) images. In a cascaded approach, we initially segmented kidneys as a region-of-interest (ROI) using 2D U-Net model, then renal masses were detected on segmented kidneys using 2D U-Net convolutional neural network (CNN) model. We trained our model on randomly selected 80% of dataset using 5-fold cross-validation technique and evaluated on remaining 20% test cases for renal mass detection. Our T2W MRI dataset contained 108 patients with malignant (renal cell carcinoma- clear cell, papillary and chromophobe) and benign (fat poor angiomyolipoma-fpAML, oncocytomas) renal masses. The U-Net model for renal mass detection generated Dice similarity coefficient (DSC) of 90.00 ± 6.00 % (mean ± standard deviation). When localized kidneys evaluated on U-Net renal mass detection model yielded a sensitivity/recall, and specificity of 76.49% and 86.55%, respectively. Thus, our proposed fully automated cascaded approach has potential to be used as the first step in renal mass characterization study on T2W MRI images.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".