LMFLOSS: A Hybrid Loss For Imbalanced Medical Image Classification

Sadi, Abu Adnan; Chowdhury, Labib; Jahan, Nursrat; Rafi, Mohammad Newaz Sharif; Chowdhury, Radeya; Khan, Faisal Ahamed; Mohammed, Nabeel

doi:10.48550/arxiv.2212.12741

Cited by 1 publication

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, new loss functions are supposed to train the network. For example, we will use LMFLOSS [ 40 ] function to dynamically consider hard samples and class distribution and alleviate the poor performance caused by data class imbalance.…”

Section: Limitations and Future Directionsmentioning

confidence: 99%

Multi-Branch Attention Learning for Bone Age Assessment with Ambiguous Label

Zhang

et al. 2023

Sensors

View full text Add to dashboard Cite

Bone age assessment (BAA) is a typical clinical technique for diagnosing endocrine and metabolic diseases in children’s development. Existing deep learning-based automatic BAA models are trained on the Radiological Society of North America dataset (RSNA) from Western populations. However, due to the difference in developmental process and BAA standards between Eastern and Western children, these models cannot be applied to bone age prediction in Eastern populations. To address this issue, this paper collects a bone age dataset based on the East Asian populations for model training. Nevertheless, it is laborious and difficult to obtain enough X-ray images with accurate labels. In this paper, we employ ambiguous labels from radiology reports and transform them into Gaussian distribution labels of different amplitudes. Furthermore, we propose multi-branch attention learning with ambiguous labels network (MAAL-Net). MAAL-Net consists of a hand object location module and an attention part extraction module to discover the informative regions of interest (ROIs) based only on image-level labels. Extensive experiments on both the RSNA dataset and the China Bone Age (CNBA) dataset demonstrate that our method achieves competitive results with the state-of-the-arts, and performs on par with experienced physicians in children’s BAA tasks.

show abstract

Section: Limitations and Future Directionsmentioning

confidence: 99%

Multi-Branch Attention Learning for Bone Age Assessment with Ambiguous Label

Zhang

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

LMFLOSS: A Hybrid Loss For Imbalanced Medical Image Classification

Cited by 1 publication

References 0 publications

Multi-Branch Attention Learning for Bone Age Assessment with Ambiguous Label

Multi-Branch Attention Learning for Bone Age Assessment with Ambiguous Label

Contact Info

Product

Resources

About