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Clip in medical imaging: A comprehensive survey
Contrastive Language-Image Pre-training (CLIP), a simple yet effective pre-training
paradigm, successfully introduces text supervision to vision models. It has shown promising …
paradigm, successfully introduces text supervision to vision models. It has shown promising …
Three-way multi-label classification: A review, a framework, and new challenges
The multi-label classification task is more challenging than the degenerated case of single-
label classification due to diversified uncertainty. Uncertainty in multi-label classification not …
label classification due to diversified uncertainty. Uncertainty in multi-label classification not …
Fine-Grained Self-Supervised Learning with Jigsaw puzzles for medical image classification
Classifying fine-grained lesions is challenging due to minor and subtle differences in
medical images. This is because learning features of fine-grained lesions with highly minor …
medical images. This is because learning features of fine-grained lesions with highly minor …
[HTML][HTML] Denoising diffusion probabilistic models for addressing data limitations in chest X-ray classification
Deep learning plays a crucial role in medical imaging analysis, particularly in tasks such as
image classification and segmentation. However, learning from medical imaging datasets …
image classification and segmentation. However, learning from medical imaging datasets …
Evolution-aware VAriance (EVA) Coreset Selection for Medical Image Classification
In the medical field, managing high-dimensional massive medical imaging data and
performing reliable medical analysis from it is a critical challenge, especially in resource …
performing reliable medical analysis from it is a critical challenge, especially in resource …
Improving Generalization and Personalization in Long-Tailed Federated Learning via Classifier Retraining
Extensive research has been dedicated to studying the substantial challenge posed by non-
IID data, which hinders the performance of federated learning (FL), a popular distributed …
IID data, which hinders the performance of federated learning (FL), a popular distributed …
Ensemble of ConvNeXt V2 and MaxViT for Long-Tailed CXR Classification with View-Based Aggregation
In this work, we present our solution for the MICCAI 2024 CXR-LT challenge, achieving 4th
place in Subtask 2 and 5th in Subtask 1. We leveraged an ensemble of ConvNeXt V2 and …
place in Subtask 2 and 5th in Subtask 1. We leveraged an ensemble of ConvNeXt V2 and …
ReDebias: Exploring Residual energy based Debias learning
R Peng, Z Wang, X Chen, X Lan - openreview.net
In real-world applications, ensuring that model decisions are independent of the training
data distribution is crucial for safely deploying models. To address the long-tailed problem …
data distribution is crucial for safely deploying models. To address the long-tailed problem …
MONICA: BENCHMARKING ON LONG-TAILED MEDI-CAL IMAGE CLASSIFICATION
CB Loss - openreview.net
Long-tailed learning is considered to be an extremely challenging problem in data
imbalance learning. It aims to train well-generalized models from a large number of images …
imbalance learning. It aims to train well-generalized models from a large number of images …
[فهرست منابع][C] Editorial for Special Issue on Foundation Models for Medical Image Analysis
Editorial for Special Issue on Foundation Models for Medical Image Analysis Editorial for
Special Issue on Foundation Models for Medical Image Analysis Med Image Anal. 2024 Nov …
Special Issue on Foundation Models for Medical Image Analysis Med Image Anal. 2024 Nov …