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#5728. MBNM: Multi-branch network based on memory features for long-tailed medical image recognition
| August 2026 | publication date |
| Proposal available till | 29-05-2025 |
| 4 total number of authors per manuscript | 0 $ |
| The title of the journal is available only for the authors who have already paid for |
| Journal’s subject area: |
| Health Informatics; Computer Science Applications; Software; |
| place 1 | place 2 | place 3 | place 4 |
| Free | Free | Free | Free |
| 2350 $ | 1200 $ | 1050 $ | 900 $ |
Contract №5728.1   | Contract №5728.2 | Contract №5728.3 | Contract №5728.4 |
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Abstract:
Background and objectives: Deep learning algorithms show revolutionary potential in computer-aided diagnosis. These computer-aided diagnosis techniques often rely on large-scale, balanced standard datasets. However, there are many rare diseases in real clinical scenarios, which makes the medical datasets present a highly imbalanced long-tailed distribution, leading to the poor generalization ability of deep learning models. Currently, most algorithms to solve this problem involve more complex modules and loss functions. But for complicated tasks in the medical domain, usually suffer from issues such as increased inference time and unstable performance. Therefore, it is a great challenge to develop a computer-aided diagnosis algorithm for long-tailed medical data. Methods: We proposed the Multi-Branch Network based on Memory Features (MBNM) for Long-Tailed Medical Image Recognition. MBNM includes three branches, where each branch focuses on a different learning task: 1) the regular learning branch learns the generalizable feature representations; 2) the tail learning branch gains extra intra-class diversity for the tail classes through the feature memory module and the improved reverse sampler to improve the classification performance of the tail classes; 3) the fusion balance branch integrates various decision-making advantages and introduces an adaptive loss function to re-balance the classification performance of easy and difficult samples.
Keywords:
Deep learning; Fusion model; Imbalanced medical image; Memory features
Contacts :
2 place - free (for sale)
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4 place - free (for sale)
Abstract:
Background and objectives: Deep learning algorithms show revolutionary potential in computer-aided diagnosis. These computer-aided diagnosis techniques often rely on large-scale, balanced standard datasets. However, there are many rare diseases in real clinical scenarios, which makes the medical datasets present a highly imbalanced long-tailed distribution, leading to the poor generalization ability of deep learning models. Currently, most algorithms to solve this problem involve more complex modules and loss functions. But for complicated tasks in the medical domain, usually suffer from issues such as increased inference time and unstable performance. Therefore, it is a great challenge to develop a computer-aided diagnosis algorithm for long-tailed medical data. Methods: We proposed the Multi-Branch Network based on Memory Features (MBNM) for Long-Tailed Medical Image Recognition. MBNM includes three branches, where each branch focuses on a different learning task: 1) the regular learning branch learns the generalizable feature representations; 2) the tail learning branch gains extra intra-class diversity for the tail classes through the feature memory module and the improved reverse sampler to improve the classification performance of the tail classes; 3) the fusion balance branch integrates various decision-making advantages and introduces an adaptive loss function to re-balance the classification performance of easy and difficult samples.
Keywords:
Deep learning; Fusion model; Imbalanced medical image; Memory features
Contacts :
