Paper Citation Record · LEDGER

Boosting Vision Semantic Density with Anatomy Normality Modeling for Medical Vision-language Pre-training

As of 7 August 2026, this Paper Citation Record lists 51 of 51 outbound references and 2 inbound Pith citation observations for arXiv:2508.03742.

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pith.paper-citation-record.v1
2508.03742 v1

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Outbound references

Observation da735143-cebb-42a5-bd59-791f0507e4a5 · outbound

This paper cites Medical image segmentation review: The suc- cess of u-net.

Boosting Vision Semantic Density with Anatomy Normality Modeling for Medical Vision-language Pre-training Medical image segmentation review: The suc- cess of u-net

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This paper cites Qwen Technical Report.

Boosting Vision Semantic Density with Anatomy Normality Modeling for Medical Vision-language Pre-training Qwen Technical Report

Reference 2

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This paper cites Merlin: A vision language foun- dation model for 3d computed tomography.

Boosting Vision Semantic Density with Anatomy Normality Modeling for Medical Vision-language Pre-training Merlin: A vision language foun- dation model for 3d computed tomography

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This paper cites A review of the appli- cation of deep learning in medical image classification and segmentation.

Boosting Vision Semantic Density with Anatomy Normality Modeling for Medical Vision-language Pre-training A review of the appli- cation of deep learning in medical image classification and segmentation

Reference 4

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This paper cites Bootstrapping chest ct image understanding by distilling knowledge from x-ray expert models.

Boosting Vision Semantic Density with Anatomy Normality Modeling for Medical Vision-language Pre-training Bootstrapping chest ct image understanding by distilling knowledge from x-ray expert models

Reference 5

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This paper cites A simple framework for contrastive learning of visual representations.

Boosting Vision Semantic Density with Anatomy Normality Modeling for Medical Vision-language Pre-training A simple framework for contrastive learning of visual representations

Reference 6

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This paper cites Uniter: Universal image-text representation learning.

Boosting Vision Semantic Density with Anatomy Normality Modeling for Medical Vision-language Pre-training Uniter: Universal image-text representation learning

Reference 7

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This paper cites Align, rea- son and learn: Enhancing medical vision-and-language pre- training with knowledge.

Boosting Vision Semantic Density with Anatomy Normality Modeling for Medical Vision-language Pre-training Align, rea- son and learn: Enhancing medical vision-and-language pre- training with knowledge

Reference 8

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This paper cites Masked image modeling advances 3d medical image analysis.

Boosting Vision Semantic Density with Anatomy Normality Modeling for Medical Vision-language Pre-training Masked image modeling advances 3d medical image analysis

Reference 9

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This paper cites Prior: Prototype representation joint learning from medical images and reports.

Boosting Vision Semantic Density with Anatomy Normality Modeling for Medical Vision-language Pre-training Prior: Prototype representation joint learning from medical images and reports

Reference 10

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This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Boosting Vision Semantic Density with Anatomy Normality Modeling for Medical Vision-language Pre-training BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

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This paper cites Machine-learning-based multiple abnor- mality prediction with large-scale chest computed tomogra- phy volumes.

Boosting Vision Semantic Density with Anatomy Normality Modeling for Medical Vision-language Pre-training Machine-learning-based multiple abnor- mality prediction with large-scale chest computed tomogra- phy volumes

Reference 12

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This paper cites Cmt: Convolutional neural networks meet vision transformers.

Boosting Vision Semantic Density with Anatomy Normality Modeling for Medical Vision-language Pre-training Cmt: Convolutional neural networks meet vision transformers

Reference 13

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This paper cites A foundation model utiliz- ing chest ct volumes and radiology reports for supervised- level zero-shot detection of abnormalities.

Boosting Vision Semantic Density with Anatomy Normality Modeling for Medical Vision-language Pre-training A foundation model utiliz- ing chest ct volumes and radiology reports for supervised- level zero-shot detection of abnormalities

Reference 14

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This paper cites Deep residual learning for image recognition.

Boosting Vision Semantic Density with Anatomy Normality Modeling for Medical Vision-language Pre-training Deep residual learning for image recognition

Reference 15

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This paper cites Momentum contrast for unsupervised visual rep- resentation learning.

Boosting Vision Semantic Density with Anatomy Normality Modeling for Medical Vision-language Pre-training Momentum contrast for unsupervised visual rep- resentation learning

Reference 16

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This paper cites Masked autoencoders are scalable vision learners.

Boosting Vision Semantic Density with Anatomy Normality Modeling for Medical Vision-language Pre-training Masked autoencoders are scalable vision learners

Reference 17

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This paper cites Gloria: A multimodal global-local represen- tation learning framework for label-efficient medical image recognition.

Boosting Vision Semantic Density with Anatomy Normality Modeling for Medical Vision-language Pre-training Gloria: A multimodal global-local represen- tation learning framework for label-efficient medical image recognition

Reference 18

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This paper cites nnu-net: a self-configuring method for deep learning-based biomedical image segmen- tation.

Boosting Vision Semantic Density with Anatomy Normality Modeling for Medical Vision-language Pre-training nnu-net: a self-configuring method for deep learning-based biomedical image segmen- tation

Reference 19

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This paper cites Align before fuse: Vision and language representation learn- ing with momentum distillation.

Boosting Vision Semantic Density with Anatomy Normality Modeling for Medical Vision-language Pre-training Align before fuse: Vision and language representation learn- ing with momentum distillation

Reference 20

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This paper cites Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation.

Boosting Vision Semantic Density with Anatomy Normality Modeling for Medical Vision-language Pre-training Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation

Reference 21

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Boosting Vision Semantic Density with Anatomy Normality Modeling for Medical Vision-language Pre-training Grounded language-image pre-training

Reference 22

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Boosting Vision Semantic Density with Anatomy Normality Modeling for Medical Vision-language Pre-training Dynamic graph enhanced contrastive learning for chest x-ray report generation

Reference 23

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This paper cites Anatomical Structure-Guided Medical Vision-Language Pre-training.

Boosting Vision Semantic Density with Anatomy Normality Modeling for Medical Vision-language Pre-training Anatomical Structure-Guided Medical Vision-Language Pre-training

Reference 24

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This paper cites An organ-aware diagnosis framework for radiology report generation.

Boosting Vision Semantic Density with Anatomy Normality Modeling for Medical Vision-language Pre-training An organ-aware diagnosis framework for radiology report generation

Reference 25

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This paper cites Scaling language-image pre-training via masking.

Boosting Vision Semantic Density with Anatomy Normality Modeling for Medical Vision-language Pre-training Scaling language-image pre-training via masking

Reference 26

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This paper cites Ct-glip: 3d grounded language- image pretraining with ct scans and radiology reports for full-body scenarios.

Boosting Vision Semantic Density with Anatomy Normality Modeling for Medical Vision-language Pre-training Ct-glip: 3d grounded language- image pretraining with ct scans and radiology reports for full-body scenarios

Reference 27

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This paper cites IMITATE: Clinical Prior Guided Hierarchical Vision-Language Pre-training.

Boosting Vision Semantic Density with Anatomy Normality Modeling for Medical Vision-language Pre-training IMITATE: Clinical Prior Guided Hierarchical Vision-Language Pre-training

Reference 28

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Boosting Vision Semantic Density with Anatomy Normality Modeling for Medical Vision-language Pre-training Bootstrapping large language models for radiology report generation

Reference 29

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This paper cites Joint learning of localized representations from medical images and reports.

Boosting Vision Semantic Density with Anatomy Normality Modeling for Medical Vision-language Pre-training Joint learning of localized representations from medical images and reports

Reference 30

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Boosting Vision Semantic Density with Anatomy Normality Modeling for Medical Vision-language Pre-training Representation Learning with Contrastive Predictive Coding

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This paper cites GREEN: Generative Radiology Report Evaluation and Error Notation.

Boosting Vision Semantic Density with Anatomy Normality Modeling for Medical Vision-language Pre-training GREEN: Generative Radiology Report Evaluation and Error Notation

Reference 32

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This paper cites Deep learning for anomaly detection: A review.

Boosting Vision Semantic Density with Anatomy Normality Modeling for Medical Vision-language Pre-training Deep learning for anomaly detection: A review

Reference 33

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This paper cites Learn- ing transferable visual models from natural language super- vision.

Boosting Vision Semantic Density with Anatomy Normality Modeling for Medical Vision-language Pre-training Learn- ing transferable visual models from natural language super- vision

Reference 34

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This paper cites Large-scale and fine-grained vision-language pre-training for enhanced ct image under- standing.

Boosting Vision Semantic Density with Anatomy Normality Modeling for Medical Vision-language Pre-training Large-scale and fine-grained vision-language pre-training for enhanced ct image under- standing

Reference 35

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This paper cites Expert-level detection of pathologies from unannotated chest x-ray images via self- supervised learning.

Boosting Vision Semantic Density with Anatomy Normality Modeling for Medical Vision-language Pre-training Expert-level detection of pathologies from unannotated chest x-ray images via self- supervised learning

Reference 36

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Boosting Vision Semantic Density with Anatomy Normality Modeling for Medical Vision-language Pre-training Neural discrete representation learning

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Boosting Vision Semantic Density with Anatomy Normality Modeling for Medical Vision-language Pre-training Neural discrete representation learning

Reference 38

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This paper cites Attention is all you need.

Boosting Vision Semantic Density with Anatomy Normality Modeling for Medical Vision-language Pre-training Attention is all you need

Reference 39

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This paper cites Multi-granularity cross-modal align- ment for generalized medical visual representation learning.

Boosting Vision Semantic Density with Anatomy Normality Modeling for Medical Vision-language Pre-training Multi-granularity cross-modal align- ment for generalized medical visual representation learning

Reference 40

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This paper cites To- talsegmentator: robust segmentation of 104 anatomic struc- tures in ct images.

Boosting Vision Semantic Density with Anatomy Normality Modeling for Medical Vision-language Pre-training To- talsegmentator: robust segmentation of 104 anatomic struc- tures in ct images

Reference 41

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This paper cites Medklip: Medical knowledge enhanced language-image pre-training for x-ray diagnosis.

Boosting Vision Semantic Density with Anatomy Normality Modeling for Medical Vision-language Pre-training Medklip: Medical knowledge enhanced language-image pre-training for x-ray diagnosis

Reference 42

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This paper cites Multimodal ChatGPT for Medical Applications: an Experimental Study of GPT-4V.

Boosting Vision Semantic Density with Anatomy Normality Modeling for Medical Vision-language Pre-training Multimodal ChatGPT for Medical Applications: an Experimental Study of GPT-4V

Reference 43

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This paper cites Knowledge-enhanced visual-language pre- training on chest radiology images.Nature Communications, 14(1):4542, 2023.

Boosting Vision Semantic Density with Anatomy Normality Modeling for Medical Vision-language Pre-training Knowledge-enhanced visual-language pre- training on chest radiology images.Nature Communications, 14(1):4542, 2023

Reference 44

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This paper cites When radiology report genera- tion meets knowledge graph.

Boosting Vision Semantic Density with Anatomy Normality Modeling for Medical Vision-language Pre-training When radiology report genera- tion meets knowledge graph

Reference 45

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This paper cites Advancing Radiograph Representation Learning with Masked Record Modeling.

Boosting Vision Semantic Density with Anatomy Normality Modeling for Medical Vision-language Pre-training Advancing Radiograph Representation Learning with Masked Record Modeling

Reference 46

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This paper cites Multimodal Large Language Model driven Radiology Report Generation with Clinical Knowledge Enhancement.

Boosting Vision Semantic Density with Anatomy Normality Modeling for Medical Vision-language Pre-training Multimodal Large Language Model driven Radiology Report Generation with Clinical Knowledge Enhancement

Reference 47

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This paper cites Variety in visual encoder selection In the 3D CT VLP task, we discover that the CNN visual encoder outperforms the ViT.

Boosting Vision Semantic Density with Anatomy Normality Modeling for Medical Vision-language Pre-training Variety in visual encoder selection In the 3D CT VLP task, we discover that the CNN visual encoder outperforms the ViT

Reference 48

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Boosting Vision Semantic Density with Anatomy Normality Modeling for Medical Vision-language Pre-training Unresolved cited work

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Cheap Probes Predict Expensive Training in 3D-CT Vision--Language Models cites this paper.

Cheap Probes Predict Expensive Training in 3D-CT Vision--Language Models Boosting Vision Semantic Density with Anatomy Normality Modeling for Medical Vision-language Pre-training

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ORCA: ORgan-Centroid Aggregation for Training-Free 3D CT Visual Token Compression cites this paper.

ORCA: ORgan-Centroid Aggregation for Training-Free 3D CT Visual Token Compression Boosting Vision Semantic Density with Anatomy Normality Modeling for Medical Vision-language Pre-training

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