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Claim this profileWorks43 from public data
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- Deep adversarial domain adaptation for breast cancer screening from mammograms43
The proposed method adopts an adversarial learning technique to perform domain adaptation using the two domains of source and target domains to improve the detection performance on the target dataset.
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- Contrastive domain adaptation with consistency match for automated pneumonia diagnosis31
This work proposes a novel method called Contrastive Domain Adaptation with Consistency Match (CDACM), which outperforms several state-of-the-art unsupervised domain adaptation approaches, and verifies the effectiveness of CDACM for automated pneumonia diagnosis using chest X-ray imaging.
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- A multi-stage multi-modal learning algorithm with adaptive multimodal fusion for improving multi-label skin lesion classification22
A novel uncertainty-based hybrid fusion strategy for a multi-modal learning algorithm aimed at skin cancer diagnosis that combines three different modalities: clinical images, dermoscopy images, and metadata, to make the final classification.
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- A feature-wise attention module based on the difference with surrounding features for convolutional neural networks14
A feature-wise attention module is proposed, which can give each feature of the input feature map an attention weight and is based on the well-known surround suppression in the discipline of neuroscience.
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- GapMatch: Bridging Instance and Model Perturbations for Enhanced Semi-Supervised Medical Image Segmentation11
A unified perturbation framework named GapMatch is proposed, which bridges instance and model perturbations to broaden the perturbation space and employs dual perturbation to impose consistency regularization on the model.
- Breast cancer X-ray image staging: based on efficient net with multi-scale fusion and cbam attention9
Compared with other existing image classification algorithms, the proposed Efficientnet model based on the cbam attention mechanism has the highest accuracy, thus the researchers conclude that EfficientNet with CBAM and multi-scale fusion will improve the classification performance.
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- Performance Test of a Well-Trained Model for Meningioma Segmentation in Health Care Centers: Secondary Analysis Based on Four Retrospective Multicenter Data Sets7
Deploying the trained CNN model in different health care institutions may show significant performance degradation due to the domain shift of MRIs, and the use of unsupervised domain adaptation or supervised retraining should be considered.
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- Class-Center-Based Self-Knowledge Distillation: A Simple Method to Reduce Intra-Class Variance5
This work proposes a simple improved algorithm, the center self-distillation, which achieves a better effect with almost no additional computational cost and can not only reduce intra-class variance but also greatly improve the generalization ability of modern convolutional neural networks.
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- Deep Neural Network Augments Performance of Junior Residents in Diagnosing COVID-19 Pneumonia on Chest Radiographs5
While the AI model improved junior residents’ performance, a decline in performance was observed on the external test compared to the internal test set, suggesting a domain shift between the patient dataset and the external dataset, highlighting the need for future research on test-time training domain adaptation to address this issue.
Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar; position from the scholar’s ORCID record, last synced 2026-10-11. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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