Training and validation loss per epoch for different pretext task combinations.

<p>Subplot (a) shows Scenario 1, where training loss decreases rapidly and stabilizes, with test loss following a similar trend. Subplot (b) depicts Scenario 2, where both training and test loss decrease rapidly initially and then gradually, with training loss generally lower than test loss. S...

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Bibliographic Details
Main Author:	Shima Ayyoubi Nezhad (21276429) (author)
Other Authors:	Golnaz Tajeddin (21276432) (author), Toktam Khatibi (7356137) (author), Masoudreza Sohrabi (9333500) (author)
Published:	2025
Subjects:	Medicine Biotechnology Cancer Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified unlabeled endoscopic images jigsaw puzzle solving highlighting critical decision enhance feature extraction endoscopic video frames demonstrating substantial improvements anatomical landmark identification model extracts rich model &# 8217 enhancing model performance combined model achieves enhance feature learning world clinical applications cam improves explainability incorporate attention mechanisms time diagnostic integration large annotated datasets supervised learning framework supervised learning diverse datasets clinical deployment cam visualization annotated data attention mechanism attention layers model interpretability work introduces score compared roc curves results underscore reducing reliance pylorus regions proposed framework patch prediction meaningful representations making regions limited availability learn discriminative interpretable methodology high accuracy generalizable features gastrointestinal diseases future research explore validation enhancements make efficient classification early diagnosis downstream classification confusion matrices comprehensive dataset classification accuracy based architectures 98 %,
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Training and validation loss per epoch for different pretext task combinations.

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