AN INVESTIGATION OF AUTOMATED MODELS FOR TUMOR SEGMENTATION
Bibliographic record
Abstract
In this work, we focus on the segmentation of tumors on PET/CT [Positron Emission Tomography used with Computed Tomography], which is crucial in routine clinical oncology. Based on the advances in recent deep learning-based methodologies, we studied the relative performances of three different frameworks: (a) nnU-Net [Convolutional Neural Network (CNN)-based], (b) nnU-Net with prompting a large Vision-Transformer (ViT) model called Segment Anything Model (SAM) (Hybrid), and (c) Swin-Unet (U-Net-like pure transformer) in a publicly available dataset of PET/CT images including normal patients and patients with lung cancer, lymphoma, and melanoma. Our study includes a holistic performance analysis for three cancer types and normal cases, which is typically avoided in the literature. The image volumes with cancer typically include more than one lesion (primary tumor and potential metastases). Therefore, we conducted two types of analyses. Our first analysis is conducted at an image volume level, considering all lesions together as foreground, and the rest as background. For the second analysis, we executed connected-component labelling to algorithmically label different parts of the tumor and assessed at lesion component level. At image volume level, nnU-Net performed best for lung cancer (Dice score: 73.25%) compared to melanoma (63%) and lymphoma (72.6%) among the three methods. The median largest lesion component-wise Dice score for nnU-Net, SAM with nnU-Net prompts, and Swin-Unet on three cancer types combined are 85%, 67%, and 72%, respectively. Both nnU-Net and SAM with nnU-Net approaches missed 2, 4, and 4 image volumes of lung cancer, lymphoma, and melanoma patients, resp., whereas Swin-Unet did not miss a single volume. Out of 513 normal volumes, 201 were successfully identified by nnU-Net and SAM, whereas Swin-Unet only identified 7 of them. In conclusion, the performance of models varied across the cancer types. nnU-Net proved to be the most reliable and precise algorithm evaluated in this study by showing the best performance for identifying normal patients and in delineating the largest lesions.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".