Opponenter: Alma Lund och Felix Ericsson
Examinator: Ida Häggström
Abstract:
For
medical disease diagnosis, studies have shown that artificial
intelligence-based models can be trained to perform just as effectively
or even outperform humans. Such models are typically trained in a
supervised manner that requires large amounts of data with ground truth
labels. Hence, a major bottleneck is the laborious labeling/annotation
that is required. A proposed way to overcome this bottleneck is weak
supervision. Weak supervision uses limited or noisy data as training
data in a supervised learning setting.
In this project, the
performance of an image classification model trained in both a
classically supervised setting and a weakly supervised setting was
measured and compared. A convolutional neural network model was used as
an image classifier. Images of skin lesions were classified as malignant
or benign melanoma. The noisy data was programmatically annotated by
using heuristics and rules. So-called labeling functions were used to
implement these rules which together with statistical models generate
probabilistic labels for the images. The labeling functions were
formulated for metadata, such as age, gender and character traits of the
mole. 2000 images were used as training and validation data and 600
images were set aside for test data. The weakly annotated data receives
an averaged area under the curve score at 0.82, and for the ground truth
data, 0.86 on the test data. The weakly supervised model shows
promising results, scoring an accuracy of 76%. The result shows that
weak supervision can create good predictive image classifiers. Hence,
these models have the potential to be used to generate new training data that can be used for a image classifier.
Welcome!
Linn, Sara and Ida
Category
Student project presentation
Location:
EDIT-rummet 3364 (EDIT-huset)
Starts:
02 June, 2022, 09:00
Ends:
02 June, 2022, 10:00