Files and links (1)
Preprint (Author's original)arXiv.org - Non-exclusive license to distribute, Open
Preprint
The Impact of an XAI-Augmented Approach on Binary Classification with Scarce Data
ArXiv.org
01 Jul 2024
Abstract
Point-of-Care Ultrasound (POCUS) is the practice of clinicians conducting and
interpreting ultrasound scans right at the patient's bedside. However, the
expertise needed to interpret these images is considerable and may not always
be present in emergency situations. This reality makes algorithms such as
machine learning classifiers extremely valuable to augment human decisions.
POCUS devices are becoming available at a reasonable cost in the size of a
mobile phone. The challenge of turning POCUS devices into life-saving tools is
that interpretation of ultrasound images requires specialist training and
experience. Unfortunately, the difficulty to obtain positive training images
represents an important obstacle to building efficient and accurate
classifiers. Hence, the problem we try to investigate is how to explore
strategies to increase accuracy of classifiers trained with scarce data. We
hypothesize that training with a few data instances may not suffice for
classifiers to generalize causing them to overfit. Our approach uses an
Explainable AI-Augmented approach to help the algorithm learn more from less
and potentially help the classifier better generalize.
Metrics
27 Record Views
Details
- Title
- The Impact of an XAI-Augmented Approach on Binary Classification with Scarce Data
- Creators
- Ximing WenRosina O WeberAnik SenDarryl HannanSteven C NesbitVincent ChanAlberto GoffiMichael MorrisJohn C HunninghakeNicholas E VillalobosEdward KimChristopher J MacLellan
- Publication Details
- ArXiv.org
- Resource Type
- Preprint
- Language
- English
- Academic Unit
- Information Science (Informatics); Computer Science (Computing); College of Computing and Informatics
- Other Identifier
- 991021893715404721