Improving Medical Image Classification in Noisy Labels Using only Self-supervised Pretraining

Khanal, Bidur; Bhattarai, Binod; Khanal, Bishesh; Linte, Cristian A.

doi:10.1007/978-3-031-44992-5_8

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article2

Book1

Other1

Relationship

Self Cite0

Independent4

Authors

Journals

Cited by 4 publications

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Active Label Refinement for Robust Training of Imbalanced Medical Image Classification Tasks in the Presence of High Label Noise

Khanal,

Dai,

Bhattarai

et al. 2024

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Active Label Refinement for Robust Training of Imbalanced Medical Image Classification Tasks in the Presence of High Label Noise

Khanal,

Dai,

Bhattarai

et al. 2024

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Performance changes due to differences among annotating radiologists for training data in computerized lesion detection

Nomura,

Hanaoka,

Hayashi

et al. 2024

Int J CARS

View full text Add to dashboard Cite

Purpose The quality and bias of annotations by annotators (e.g., radiologists) affect the performance changes in computer-aided detection (CAD) software using machine learning. We hypothesized that the difference in the years of experience in image interpretation among radiologists contributes to annotation variability. In this study, we focused on how the performance of CAD software changes with retraining by incorporating cases annotated by radiologists with varying experience. Methods We used two types of CAD software for lung nodule detection in chest computed tomography images and cerebral aneurysm detection in magnetic resonance angiography images. Twelve radiologists with different years of experience independently annotated the lesions, and the performance changes were investigated by repeating the retraining of the CAD software twice, with the addition of cases annotated by each radiologist. Additionally, we investigated the effects of retraining using integrated annotations from multiple radiologists. Results The performance of the CAD software after retraining differed among annotating radiologists. In some cases, the performance was degraded compared to that of the initial software. Retraining using integrated annotations showed different performance trends depending on the target CAD software, notably in cerebral aneurysm detection, where the performance decreased compared to using annotations from a single radiologist. Conclusions Although the performance of the CAD software after retraining varied among the annotating radiologists, no direct correlation with their experience was found. The performance trends differed according to the type of CAD software used when integrated annotations from multiple radiologists were used.

show abstract

Tracing the genealogy origin of geographic populations based on genomic variation and deep learning

Yang,

Zhou,

Liu

2024

Molecular Phylogenetics and Evolution

View full text Add to dashboard Cite

Improving Medical Image Classification in Noisy Labels Using only Self-supervised Pretraining

Cited by 4 publications

References 25 publications

Active Label Refinement for Robust Training of Imbalanced Medical Image Classification Tasks in the Presence of High Label Noise

Active Label Refinement for Robust Training of Imbalanced Medical Image Classification Tasks in the Presence of High Label Noise

Performance changes due to differences among annotating radiologists for training data in computerized lesion detection

Tracing the genealogy origin of geographic populations based on genomic variation and deep learning

Contact Info

Product

Resources

About