GNN-ViTCap: GNN-Enhanced Multiple Instance Learning with Vision Transformers for Whole Slide Image Classification and Captioning
Bibliographic record
Abstract
Microscopic assessment of histopathology images is vital for accurate cancer diagnosis and treatment. Whole Slide Image (WSI) classification and captioning have become crucial tasks in computer-aided pathology. However, microscopic WSIs face challenges such as redundant patches and unknown patch positions due to subjective pathologist captures. Moreover, generating automatic pathology captions remains a significant challenge. To address these challenges, a novel GNN-ViTCap framework is introduced for classification and caption generation from histopathological microscopic images. A visual feature extractor is used to extract feature embeddings. The redundant patches are then removed by dynamically clustering images using deep embedded clustering and extracting representative images through a scalar dot attention mechanism. The graph is formed by constructing edges from the similarity matrix, connecting each node to its nearest neighbors. Therefore, a graph neural network is utilized to extract and represent contextual information from both local and global areas. The aggregated image embeddings are then projected into the language model’s input space using a linear layer and combined with input caption tokens to fine-tune the large language models for caption generation. Our proposed method is validated using the BreakHis and PatchGastric microscopic datasets. The GNN-ViTCap method achieves an F1-Score of 0.934 and AUC of 0.963 for classification, along with BLEU@4 = 0.811 and METEOR = 0.569 for captioning. Experimental analysis demonstrates that the GNN-ViTCap architecture outper-forms state-of-the-art (SOTA) approaches, providing a reliable and efficient approach for patient diagnosis using microscopy images.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".