Detection of Hepatocellular Carcinoma in CT Images Using Deep Learning
Why this work is in the frame
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Bibliographic record
Abstract
The purpose of this paper is to develop a method to detect hepatocellular carcinoma, namely liver cancer in CT (Computerized Tomography) images using the deep learning that is a kind of AI (Artificial Intelligence). Firstly, the learning and recognition programs were developed using Python as a programming language and TensorFlow provided by Google that is a machine learning library. The CT images of 30 clinical subjects were selected from the DICOM format data provided by Graduate School of Medicine of Ehime University. Then 150 sets of CT images were selected where one set consists of two CT images for early and late phases in the cases with hepatocellular carcinoma. In addition, 150 sets of CT images were also selected in the cases without hepatocellular carcinoma. The 450 sets of CT images to each the 150 sets, namely 900 sets in total were created by rotating each original CT image. Consequently, 1,200 sets of CT images (2,400 CT images) in total were used for the learning. Then validity and usefulness of the learning and recognition programs were proved by examining the calculated results. This time, the hepatocellular carcinoma could be detected with relatively high sensitivity of 92.2% even with a relatively small number of learning data, namely 1,200 sets of CT images.
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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 | 0.001 |
| 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