Simple net: Convolutional neural network to perform differential diagnosis of ampullary tumors
Bibliographic record
Abstract
Diagnosing different stages of cancer has only been performed by doctors due to the complexity of the task. However recent advancements made in the field of deep learning has pushed the capabilities of what an algorithm can achieve. In this study, we have trained a convolutional neural network to perform differential diagnosis of Ampullary tumors. Our proposed network is only made out of seven layers. However, when compared with other state of the art classification networks such as VGG 16, VGG 19, Res Net, and Dense Net our model not only had the best performance but also shortest training time. All of the networks were trained for 150 epochs with step wise learning rate with Adam optimizer to converge as quick as possible. Our model was able to reach average of 78.14 percent accuracy with average training time of 50.60 seconds on Asus Zephyrus, with Nvidia 1080 GPU and Max Q technology.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".