Curriculum Fine-tuning of Vision Foundation Model for Medical Image Classification Under Label Noise | Paper Citation Record

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This paper cites Deep residual learning for image recognition.

Curriculum Fine-tuning of Vision Foundation Model for Medical Image Classification Under Label Noise Deep residual learning for image recognition

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Curriculum Fine-tuning of Vision Foundation Model for Medical Image Classification Under Label Noise Fast r-cnn

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This paper cites Mask r-cnn.

Curriculum Fine-tuning of Vision Foundation Model for Medical Image Classification Under Label Noise Mask r-cnn

Reference 3

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This paper cites Tokens-to-token vit: Training vision transformers from scratch on imagenet.

Curriculum Fine-tuning of Vision Foundation Model for Medical Image Classification Under Label Noise Tokens-to-token vit: Training vision transformers from scratch on imagenet

Reference 4

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This paper cites Learning from noisy labels with deep neural networks: A survey.

Curriculum Fine-tuning of Vision Foundation Model for Medical Image Classification Under Label Noise Learning from noisy labels with deep neural networks: A survey

Reference 5

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This paper cites The unreasonable effectiveness of noisy data for fine-grained recognition.

Curriculum Fine-tuning of Vision Foundation Model for Medical Image Classification Under Label Noise The unreasonable effectiveness of noisy data for fine-grained recognition

Reference 6

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This paper cites A closer look at memorization in deep networks.

Curriculum Fine-tuning of Vision Foundation Model for Medical Image Classification Under Label Noise A closer look at memorization in deep networks

Reference 7

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This paper cites Robust medical image classification from noisy labeled data with global and local representation guided co-training.

Curriculum Fine-tuning of Vision Foundation Model for Medical Image Classification Under Label Noise Robust medical image classification from noisy labeled data with global and local representation guided co-training

Reference 8

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Observation 5498ea21-8233-4a0c-bdc9-6a98a419b41b · outbound

This paper cites Improving medical images classification with label noise using dual-uncertainty estimation.

Curriculum Fine-tuning of Vision Foundation Model for Medical Image Classification Under Label Noise Improving medical images classification with label noise using dual-uncertainty estimation

Reference 9

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Observation 8d6959a3-eafc-42ec-9d37-c2291fff3009 · outbound

This paper cites Mentornet: Learning data-driven curriculum for very deep neural networks on corrupted labels.

Curriculum Fine-tuning of Vision Foundation Model for Medical Image Classification Under Label Noise Mentornet: Learning data-driven curriculum for very deep neural networks on corrupted labels

Reference 10

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This paper cites Co-teaching: Robust training of deep neural networks with extremely noisy labels.

Curriculum Fine-tuning of Vision Foundation Model for Medical Image Classification Under Label Noise Co-teaching: Robust training of deep neural networks with extremely noisy labels

Reference 11

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Observation ba7d6938-e1eb-4be6-a361-ed28cde77dce · outbound

This paper cites How does disagreement help generalization against label corruption? In International conference on machine learning, pages 7164–7173.

Curriculum Fine-tuning of Vision Foundation Model for Medical Image Classification Under Label Noise How does disagreement help generalization against label corruption? In International conference on machine learning, pages 7164–7173

Reference 12

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This paper cites Combating noisy labels by agreement: A joint training method with co-regularization.

Curriculum Fine-tuning of Vision Foundation Model for Medical Image Classification Under Label Noise Combating noisy labels by agreement: A joint training method with co-regularization

Reference 13

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This paper cites Combating noisy labels with sample selection by mining high-discrepancy examples.

Curriculum Fine-tuning of Vision Foundation Model for Medical Image Classification Under Label Noise Combating noisy labels with sample selection by mining high-discrepancy examples

Reference 14

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This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

Curriculum Fine-tuning of Vision Foundation Model for Medical Image Classification Under Label Noise An image is worth 16x16 words: Transformers for image recognition at scale

Reference 15

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This paper cites Learning transferable visual models from natural language supervision.

Curriculum Fine-tuning of Vision Foundation Model for Medical Image Classification Under Label Noise Learning transferable visual models from natural language supervision

Reference 16

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This paper cites Masked autoencoders are scalable vision learners.

Curriculum Fine-tuning of Vision Foundation Model for Medical Image Classification Under Label Noise Masked autoencoders are scalable vision learners

Reference 17

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This paper cites Segment anything.

Curriculum Fine-tuning of Vision Foundation Model for Medical Image Classification Under Label Noise Segment anything

Reference 18

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This paper cites DINOv2: Learning Robust Visual Features without Supervision.

Curriculum Fine-tuning of Vision Foundation Model for Medical Image Classification Under Label Noise DINOv2: Learning Robust Visual Features without Supervision

Reference 19

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This paper cites AnyDoor: Zero-shot Object-level Image Customization.

Curriculum Fine-tuning of Vision Foundation Model for Medical Image Classification Under Label Noise AnyDoor: Zero-shot Object-level Image Customization

Reference 20

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This paper cites Stronger, Fewer, & Superior: Harnessing Vision Foundation Models for Domain Generalized Semantic Segmentation.

Curriculum Fine-tuning of Vision Foundation Model for Medical Image Classification Under Label Noise Stronger, Fewer, & Superior: Harnessing Vision Foundation Models for Domain Generalized Semantic Segmentation

Reference 21

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Curriculum Fine-tuning of Vision Foundation Model for Medical Image Classification Under Label Noise Robustness of SAM: Segment Anything Under Corruptions and Beyond

Reference 22

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This paper cites Finding Dino: A Plug-and-Play Framework for Zero-Shot Detection of Out-of-Distribution Objects Using Prototypes.

Curriculum Fine-tuning of Vision Foundation Model for Medical Image Classification Under Label Noise Finding Dino: A Plug-and-Play Framework for Zero-Shot Detection of Out-of-Distribution Objects Using Prototypes

Reference 23

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This paper cites LoRA: Low-rank adaptation of large language models.

Curriculum Fine-tuning of Vision Foundation Model for Medical Image Classification Under Label Noise LoRA: Low-rank adaptation of large language models

Reference 24

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This paper cites Visual prompt tuning.

Curriculum Fine-tuning of Vision Foundation Model for Medical Image Classification Under Label Noise Visual prompt tuning

Reference 25

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This paper cites Adapt- former: Adapting vision transformers for scalable visual recognition.

Curriculum Fine-tuning of Vision Foundation Model for Medical Image Classification Under Label Noise Adapt- former: Adapting vision transformers for scalable visual recognition

Reference 26

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This paper cites Swin transformer: Hierarchical vision transformer using shifted windows.

Curriculum Fine-tuning of Vision Foundation Model for Medical Image Classification Under Label Noise Swin transformer: Hierarchical vision transformer using shifted windows

Reference 27

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This paper cites Training data-efficient image transformers & distillation through attention.

Curriculum Fine-tuning of Vision Foundation Model for Medical Image Classification Under Label Noise Training data-efficient image transformers & distillation through attention

Reference 28

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This paper cites Emerging properties in self-supervised vision transformers.

Curriculum Fine-tuning of Vision Foundation Model for Medical Image Classification Under Label Noise Emerging properties in self-supervised vision transformers

Reference 29

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This paper cites Parameter- efficient fine-tuning for medical image analysis: The missed opportunity.

Curriculum Fine-tuning of Vision Foundation Model for Medical Image Classification Under Label Noise Parameter- efficient fine-tuning for medical image analysis: The missed opportunity

Reference 30

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Curriculum Fine-tuning of Vision Foundation Model for Medical Image Classification Under Label Noise Robust loss functions under label noise for deep neural networks

Reference 31

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Curriculum Fine-tuning of Vision Foundation Model for Medical Image Classification Under Label Noise Early-learning reg- ularization prevents memorization of noisy labels

Reference 32

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Curriculum Fine-tuning of Vision Foundation Model for Medical Image Classification Under Label Noise Symmetric cross entropy for robust learning with noisy labels

Reference 33

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Curriculum Fine-tuning of Vision Foundation Model for Medical Image Classification Under Label Noise Generalized cross entropy loss for training deep neural networks with noisy labels

Reference 34

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Curriculum Fine-tuning of Vision Foundation Model for Medical Image Classification Under Label Noise Fine samples for learning with noisy labels

Reference 35

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This paper cites DivideMix: Learning with Noisy Labels as Semi-supervised Learning.

Curriculum Fine-tuning of Vision Foundation Model for Medical Image Classification Under Label Noise DivideMix: Learning with Noisy Labels as Semi-supervised Learning

Reference 36

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This paper cites Learning from noisy data with robust representation learning.

Curriculum Fine-tuning of Vision Foundation Model for Medical Image Classification Under Label Noise Learning from noisy data with robust representation learning

Reference 37

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Curriculum Fine-tuning of Vision Foundation Model for Medical Image Classification Under Label Noise when to update

Reference 38

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Curriculum Fine-tuning of Vision Foundation Model for Medical Image Classification Under Label Noise Using pre-training can improve model robustness and uncertainty

Reference 39

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This paper cites Why is prompt tuning for vision-language models robust to noisy labels? In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 15488–15497, 2023.

Curriculum Fine-tuning of Vision Foundation Model for Medical Image Classification Under Label Noise Why is prompt tuning for vision-language models robust to noisy labels? In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 15488–15497, 2023

Reference 40

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Curriculum Fine-tuning of Vision Foundation Model for Medical Image Classification Under Label Noise Layer Normalization

Reference 41

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This paper cites The ham10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions.

Curriculum Fine-tuning of Vision Foundation Model for Medical Image Classification Under Label Noise The ham10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions

Reference 42

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Curriculum Fine-tuning of Vision Foundation Model for Medical Image Classification Under Label Noise Deep learning based method for computer aided diagnosis of diabetic retinopathy

Reference 43

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This paper cites A dataset of microscopic peripheral blood cell images for development of automatic recognition systems.

Curriculum Fine-tuning of Vision Foundation Model for Medical Image Classification Under Label Noise A dataset of microscopic peripheral blood cell images for development of automatic recognition systems

Reference 44

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Curriculum Fine-tuning of Vision Foundation Model for Medical Image Classification Under Label Noise Efficient multiple organ localization in ct image using 3d region proposal network

Reference 45

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This paper cites https://www.kaggle.com/c/ diabetic-retinopathy-detection.

Curriculum Fine-tuning of Vision Foundation Model for Medical Image Classification Under Label Noise https://www.kaggle.com/c/ diabetic-retinopathy-detection

Reference 46

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This paper cites https://challenge.isic-archive.com/landing/2018/.

Curriculum Fine-tuning of Vision Foundation Model for Medical Image Classification Under Label Noise https://challenge.isic-archive.com/landing/2018/

Reference 47

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Curriculum Fine-tuning of Vision Foundation Model for Medical Image Classification Under Label Noise Retinal abnormalities recognition using regional multitask learning

Reference 48

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This paper cites A benchmark for studying diabetic retinopathy: segmentation, grading, and transferability.

Curriculum Fine-tuning of Vision Foundation Model for Medical Image Classification Under Label Noise A benchmark for studying diabetic retinopathy: segmentation, grading, and transferability

Reference 49

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This paper cites Gradient and feature conformity-steered medical image classification with noisy labels.

Curriculum Fine-tuning of Vision Foundation Model for Medical Image Classification Under Label Noise Gradient and feature conformity-steered medical image classification with noisy labels

Reference 50

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This paper cites Combating medical label noise via robust semi-supervised contrastive learning.

Curriculum Fine-tuning of Vision Foundation Model for Medical Image Classification Under Label Noise Combating medical label noise via robust semi-supervised contrastive learning

Reference 51

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This paper cites PyTorch 2: Faster Machine Learning Through Dynamic Python Bytecode Transformation and Graph Compilation.

Curriculum Fine-tuning of Vision Foundation Model for Medical Image Classification Under Label Noise PyTorch 2: Faster Machine Learning Through Dynamic Python Bytecode Transformation and Graph Compilation

Reference 52

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This paper cites Medmnist classification decathlon: A lightweight automl benchmark for medical image analysis.

Curriculum Fine-tuning of Vision Foundation Model for Medical Image Classification Under Label Noise Medmnist classification decathlon: A lightweight automl benchmark for medical image analysis

Reference 53

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Curriculum Fine-tuning of Vision Foundation Model for Medical Image Classification Under Label Noise Medmnist v2-a large-scale lightweight benchmark for 2d and 3d biomedical image classification

Reference 54

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This paper cites Adam: A Method for Stochastic Optimization.

Curriculum Fine-tuning of Vision Foundation Model for Medical Image Classification Under Label Noise Adam: A Method for Stochastic Optimization

Reference 55

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This paper cites Classification with noisy labels by importance reweighting.

Curriculum Fine-tuning of Vision Foundation Model for Medical Image Classification Under Label Noise Classification with noisy labels by importance reweighting

Reference 56

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This paper cites Large-scale domain-specific pretraining for biomedical vision-language processing, 2023.

Curriculum Fine-tuning of Vision Foundation Model for Medical Image Classification Under Label Noise Large-scale domain-specific pretraining for biomedical vision-language processing, 2023

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:13:16.712628Z

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Observation 68589d5d-5dab-4e6f-a37c-beb8f2ed23d7 · outbound

This paper cites Imagenet: A large-scale hierarchical image database.

Curriculum Fine-tuning of Vision Foundation Model for Medical Image Classification Under Label Noise Imagenet: A large-scale hierarchical image database

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-12T10:13:16.512632Z

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This paper cites SELFIE: Refurbishing unclean samples for robust deep learning.

Curriculum Fine-tuning of Vision Foundation Model for Medical Image Classification Under Label Noise SELFIE: Refurbishing unclean samples for robust deep learning

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:13:16.688845Z

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Observation 626d5e22-c6b0-4ae5-96c8-306d8585159b · outbound

This paper cites Learning multiple layers of features from tiny images.

Curriculum Fine-tuning of Vision Foundation Model for Medical Image Classification Under Label Noise Learning multiple layers of features from tiny images

Reference 60

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