An Attentive Multi-Modal CNN for Brain Tumor Radiogenomic Classification
Why this work is in the frame
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Bibliographic record
Abstract
Medical images of brain tumors are critical for characterizing the pathology of tumors and early diagnosis. There are multiple modalities for medical images of brain tumors. Fusing the unique features of each modality of the magnetic resonance imaging (MRI) scans can accurately determine the nature of brain tumors. The current genetic analysis approach is time-consuming and requires surgical extraction of brain tissue samples. Accurate classification of multi-modal brain tumor images can speed up the detection process and alleviate patient suffering. Medical image fusion refers to effectively merging the significant information of multiple source images of the same tissue into one image, which will carry abundant information for diagnosis. This paper proposes a novel attentive deep-learning-based classification model that integrates multi-modal feature aggregation, lite attention mechanism, separable embedding, and modal-wise shortcuts for performance improvement. We evaluate our model on the RSNA-MICCAI dataset, a scenario-specific medical image dataset, and demonstrate that the proposed method outperforms the state-of-the-art (SOTA) by around 3%.
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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 | 0.001 | 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