Enhancement of clustering techniques by coupling clustering tree and neural network: Application to brain tumour segmentation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Currently, no classical clustering algorithm is efficient on its own. The predefined number of clusters required for their operation does not consistently produce satisfactory segmentation results. They exhibit cluster instability, are vulnerable to the local optimum trap, and are sensitive to noise and imaging artefacts. Most contributions designed to overcome these drawbacks incorporate prior knowledge such as cluster label information and statistic measures that demand minimal labelled training data. Although these approaches improve the segmentation accuracy, they tend to diminish the advantages of clustering algorithms over the supervised learning methods. This study proposes a shift from the use of a predefined number of clusters to a clustering tree‐based method for performance enhancement of classical clustering algorithms. The proposed method is a three‐stage algorithm. It begins with the extraction of low‐level features from a clustering tree. Clustering trees are sets of labelled clusters of an image at multiple clustering resolutions. The second stage extracts high‐level features by coupling the clustering tree to a single‐layer feedforward neural network. The third stage is the classification stage, where the basic model of a neural network extracts the tumour from a high‐level feature map. Because neither of the neural networks requires training, the proposed method is both fully unsupervised and fully automated and retains all its advantages over supervised methods. A performance evaluation using FLAIR MRI images of brain tumour patients from the BRATS2015 and BRATS2020 databases demonstrates significant performance enhancement over four classical clustering algorithms and two of the four proposed techniques were comparable to deep learning methods.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it