An Efficient Hybrid Model for Kidney Tumor Segmentation in CT Images
Bibliographic record
Abstract
Kidney tumor segmentation from CT-volumes is essential for lesion diagnosis. Considering excessive GPU memory requirements for 3D medical images, slices and patches are exploited for training and inference in conventional U-Net variant architectures' which inevitably hampers contextual learning. In this paper, we propose a novel effective hybrid model for kidney tumor segmentation in CT images with two parts: 1) Foreground Segmentation Network; 2) Sparse PointCloud Segmentation Network. Specifically, Foreground Segmentation Network firstly segments the foreground, i.e., kidneys with tumors, from background in voxel grid using classical V-Net. Secondly, we represent the obtained foreground regions as point clouds and feed them into the Sparse PointCloud Segmentation Networks to conduct fine-grained segmentation for kidney and tumor. The critical module embedded in the second part is an efficient Submanifold Sparse Convolutional Networks (SSCNs). By exploiting SSCNs, our proposed model can take all foreground as input for better context learning in a memory-efficient manner, and consider the anisotropy of CT images as well. Experiments show that our model can achieve state-of-the-art tumor segmentation while reducing GPU resource demand significantly.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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 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".