Depth Invariant 3D-CU-Net Model with Completely Connected Dense Skip Networks for MRI Kidney Tumor Segmentation
Bibliographic record
Abstract
Due to the impact and importance of the kidney objects in human body, the kidney tumor analysis from three dimensional CT and MRI medical images becomes a pivotal research topic, which helps in diagnosing the kidney diseases like kidney stones, polycystic and kidney tumors etc.In deep learning, U-Net became a prominent and reliable solution for kidney image analysis and objects segmentation process.Although several research works were focused on kidney object detection and tumor segmentation from medical images, they are suffering from some intrinsic limitations due to: variance in network depths, enforced feature fusion, segmentation errors and inaccuracy.In order to address these limitations in kidney tumor segmentation process, in this paper we proposed the 3D-CU-Net model for kidney tumor segmentation, which is a custom variant of the U-Net.In 3D-CU-Net, the encoder-decoder network model is unified to tolerate the depth invariance issues, while training various input images with the same model.Completely connected dense skip connections are designed at each layer of 3D-CU-Net, to control the enforced feature fusion and to extract the crucial features.An integrated loss function is designed with Binary Cross Entropy (BCE) and Soft-Dice Coefficient (SDC) to mitigate the segmentation errors and inaccuracy.Experiments on TCGA-KIRC dataset with 3D-CU-NET recorded the high accuracy in kidney tumor segmentation with mIoU (91.21%) and mDSC (92.69%).
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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 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".