# name
Active Learning strategies for WCE images classification

# metadata.dc.description[].value
Treballs finals del Màster de Fonaments de Ciència de Dades, Facultat de matemàtiques, Universitat de Barcelona. Curs: 2023-2024. Tutor: Santi Seguí Mesquida i Pere Gilabert Roca

# metadata.dc.description.abstract[].value
[en] Medical imaging, particularly through Wireless Capsule Endoscopy (WCE), has revolutionized gastrointestinal health by capturing intricate details of the digestive tract, with a focus on identifying potential precursors like polyps. However, labeling vast and continuous WCE videos for machine learning poses significant challenges due
to its resource-intensive nature. This research explores the realm of active learning (AL) to optimize WCE image classification, aiming to enhance model performance with minimal labeled data. Utilizing WCE videos, we established an AL framework that at every cycles selects a video to query for labels. Our study implemented
various sampling strategies, categorized into uncertainty-based and diversity-based approaches. Initial outcomes with uncertainty-based methods aligned closely with random sampling, prompting a shift towards diversity-based strategies. Notably, the cover strategy, especially with its autoencoder variant, and the clustering strategies, both diversity-based, exhibited promising results. Despite these advancements, discerning the superior strategy between cover with autoencoder and clustering necessitates further exploration. This study shows the potential of AL in WCE image classification while highlighting areas for future investigation.

# metadata.dc.rights[].value
cc-by-nc-nd (c) Sara Bardají Serra, 2024

# metadata.dc.source[].value
Màster Oficial - Fonaments de la Ciència de Dades

# metadata.dc.title[].value
Active Learning strategies for WCE images classification