Detection of Hepatocellular Carcinoma in CT Images Using Deep Learning
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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".