Unlocking the Potential of Weakly Labeled Data: A Co-Evolutionary Learning Framework for Abnormality Detection and Report Generation | Paper Citation Record

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This paper cites Computer-aided detection in chest radiography based on artificial intelligence: A survey,.

Unlocking the Potential of Weakly Labeled Data: A Co-Evolutionary Learning Framework for Abnormality Detection and Report Generation Computer-aided detection in chest radiography based on artificial intelligence: A survey,

Reference 1

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This paper cites Deep learning in generating radiology reports: A survey,.

Unlocking the Potential of Weakly Labeled Data: A Co-Evolutionary Learning Framework for Abnormality Detection and Report Generation Deep learning in generating radiology reports: A survey,

Reference 2

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This paper cites Deep learning at chest radiography: Au- tomated classification of pulmonary tuberculosis by using convolutional neural networks,.

Unlocking the Potential of Weakly Labeled Data: A Co-Evolutionary Learning Framework for Abnormality Detection and Report Generation Deep learning at chest radiography: Au- tomated classification of pulmonary tuberculosis by using convolutional neural networks,

Reference 3

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This paper cites Lung nodule detection in X-ray images: a new feature set,.

Unlocking the Potential of Weakly Labeled Data: A Co-Evolutionary Learning Framework for Abnormality Detection and Report Generation Lung nodule detection in X-ray images: a new feature set,

Reference 4

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This paper cites CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning.

Unlocking the Potential of Weakly Labeled Data: A Co-Evolutionary Learning Framework for Abnormality Detection and Report Generation CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning

Reference 5

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This paper cites Exploring and distilling posterior and prior knowledge for radiology report generation,.

Unlocking the Potential of Weakly Labeled Data: A Co-Evolutionary Learning Framework for Abnormality Detection and Report Generation Exploring and distilling posterior and prior knowledge for radiology report generation,

Reference 6

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This paper cites Generating radiology reports via memory-driven transformer,.

Unlocking the Potential of Weakly Labeled Data: A Co-Evolutionary Learning Framework for Abnormality Detection and Report Generation Generating radiology reports via memory-driven transformer,

Reference 7

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This paper cites Label matching semi-supervised object detection,.

Unlocking the Potential of Weakly Labeled Data: A Co-Evolutionary Learning Framework for Abnormality Detection and Report Generation Label matching semi-supervised object detection,

Reference 8

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This paper cites A Simple Semi-Supervised Learning Framework for Object Detection.

Unlocking the Potential of Weakly Labeled Data: A Co-Evolutionary Learning Framework for Abnormality Detection and Report Generation A Simple Semi-Supervised Learning Framework for Object Detection

Reference 9

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This paper cites End-to-end semi-supervised object detection with soft teacher,.

Unlocking the Potential of Weakly Labeled Data: A Co-Evolutionary Learning Framework for Abnormality Detection and Report Generation End-to-end semi-supervised object detection with soft teacher,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:03:46.190181Z

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This paper cites Distilling the Knowledge in a Neural Network.

Unlocking the Potential of Weakly Labeled Data: A Co-Evolutionary Learning Framework for Abnormality Detection and Report Generation Distilling the Knowledge in a Neural Network

Reference 11

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This paper cites AlignTransformer: Hierarchical alignment of visual regions and disease tags for medical report generation,.

Unlocking the Potential of Weakly Labeled Data: A Co-Evolutionary Learning Framework for Abnormality Detection and Report Generation AlignTransformer: Hierarchical alignment of visual regions and disease tags for medical report generation,

Reference 12

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This paper cites UniTAB: Unifying Text and Box Outputs for Grounded Vision-Language Modeling.

Unlocking the Potential of Weakly Labeled Data: A Co-Evolutionary Learning Framework for Abnormality Detection and Report Generation UniTAB: Unifying Text and Box Outputs for Grounded Vision-Language Modeling

Reference 13

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This paper cites Unifying vision-and-language tasks via text generation,.

Unlocking the Potential of Weakly Labeled Data: A Co-Evolutionary Learning Framework for Abnormality Detection and Report Generation Unifying vision-and-language tasks via text generation,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:03:46.125981Z

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This paper cites Towards general purpose vision systems: An end-to-end task-agnostic vision-language architecture,.

Unlocking the Potential of Weakly Labeled Data: A Co-Evolutionary Learning Framework for Abnormality Detection and Report Generation Towards general purpose vision systems: An end-to-end task-agnostic vision-language architecture,

Reference 15

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This paper cites UniT: Multimodal multitask learning with a unified Transformer,.

Unlocking the Potential of Weakly Labeled Data: A Co-Evolutionary Learning Framework for Abnormality Detection and Report Generation UniT: Multimodal multitask learning with a unified Transformer,

Reference 16

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This paper cites Rich feature hierarchies for accurate object detection and semantic segmentation,.

Unlocking the Potential of Weakly Labeled Data: A Co-Evolutionary Learning Framework for Abnormality Detection and Report Generation Rich feature hierarchies for accurate object detection and semantic segmentation,

Reference 17

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This paper cites Making the most of text semantics to improve biomedical vision–language processing,.

Unlocking the Potential of Weakly Labeled Data: A Co-Evolutionary Learning Framework for Abnormality Detection and Report Generation Making the most of text semantics to improve biomedical vision–language processing,

Reference 18

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This paper cites Weakly supervised one-stage vision and language disease detection using large scale pneumonia and pneumothorax studies,.

Unlocking the Potential of Weakly Labeled Data: A Co-Evolutionary Learning Framework for Abnormality Detection and Report Generation Weakly supervised one-stage vision and language disease detection using large scale pneumonia and pneumothorax studies,

Reference 19

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This paper cites MIMIC-CXR-JPG-chest radiographs with structured labels,.

Unlocking the Potential of Weakly Labeled Data: A Co-Evolutionary Learning Framework for Abnormality Detection and Report Generation MIMIC-CXR-JPG-chest radiographs with structured labels,

Reference 20

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This paper cites You’ve got two teachers: Co-evolutionary image and report distillation for semi-supervised anatomical abnormality detection in chest X-ray,.

Unlocking the Potential of Weakly Labeled Data: A Co-Evolutionary Learning Framework for Abnormality Detection and Report Generation You’ve got two teachers: Co-evolutionary image and report distillation for semi-supervised anatomical abnormality detection in chest X-ray,

Reference 21

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This paper cites You only look once: Unified, real-time object detection,.

Unlocking the Potential of Weakly Labeled Data: A Co-Evolutionary Learning Framework for Abnormality Detection and Report Generation You only look once: Unified, real-time object detection,

Reference 22

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This paper cites Focal loss for dense object detection,.

Unlocking the Potential of Weakly Labeled Data: A Co-Evolutionary Learning Framework for Abnormality Detection and Report Generation Focal loss for dense object detection,

Reference 23

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This paper cites Fast R-CNN,.

Unlocking the Potential of Weakly Labeled Data: A Co-Evolutionary Learning Framework for Abnormality Detection and Report Generation Fast R-CNN,

Reference 24

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Unlocking the Potential of Weakly Labeled Data: A Co-Evolutionary Learning Framework for Abnormality Detection and Report Generation Mask R-CNN,

Reference 25

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This paper cites 3rd place solution for the 2018 RSNA Pneumonia Detection Challenge,.

Unlocking the Potential of Weakly Labeled Data: A Co-Evolutionary Learning Framework for Abnormality Detection and Report Generation 3rd place solution for the 2018 RSNA Pneumonia Detection Challenge,

Reference 26

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This paper cites Self-EMD: Self-Supervised Object Detection without ImageNet.

Unlocking the Potential of Weakly Labeled Data: A Co-Evolutionary Learning Framework for Abnormality Detection and Report Generation Self-EMD: Self-Supervised Object Detection without ImageNet

Reference 27

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This paper cites Weakly supervised object localization and detection: A survey,.

Unlocking the Potential of Weakly Labeled Data: A Co-Evolutionary Learning Framework for Abnormality Detection and Report Generation Weakly supervised object localization and detection: A survey,

Reference 28

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This paper cites Comprehensive atten- tion self-distillation for weakly-supervised object detection,.

Unlocking the Potential of Weakly Labeled Data: A Co-Evolutionary Learning Framework for Abnormality Detection and Report Generation Comprehensive atten- tion self-distillation for weakly-supervised object detection,

Reference 29

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This paper cites High-quality proposals for weakly supervised object detection,.

Unlocking the Potential of Weakly Labeled Data: A Co-Evolutionary Learning Framework for Abnormality Detection and Report Generation High-quality proposals for weakly supervised object detection,

Reference 30

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This paper cites Learning deep features for discriminative localization,.

Unlocking the Potential of Weakly Labeled Data: A Co-Evolutionary Learning Framework for Abnormality Detection and Report Generation Learning deep features for discriminative localization,

Reference 31

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This paper cites Improving pneumonia localization via cross- attention on medical images and reports,.

Unlocking the Potential of Weakly Labeled Data: A Co-Evolutionary Learning Framework for Abnormality Detection and Report Generation Improving pneumonia localization via cross- attention on medical images and reports,

Reference 32

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This paper cites Anatomy- guided weakly-supervised abnormality localization in chest X-rays,.

Unlocking the Potential of Weakly Labeled Data: A Co-Evolutionary Learning Framework for Abnormality Detection and Report Generation Anatomy- guided weakly-supervised abnormality localization in chest X-rays,

Reference 33

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This paper cites What’s the point: Semantic segmentation with point supervision,.

Unlocking the Potential of Weakly Labeled Data: A Co-Evolutionary Learning Framework for Abnormality Detection and Report Generation What’s the point: Semantic segmentation with point supervision,

Reference 34

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This paper cites Point beyond class: A benchmark for weakly semi- supervised abnormality localization in chest X-rays,.

Unlocking the Potential of Weakly Labeled Data: A Co-Evolutionary Learning Framework for Abnormality Detection and Report Generation Point beyond class: A benchmark for weakly semi- supervised abnormality localization in chest X-rays,

Reference 35

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This paper cites Consistency-based semi- supervised learning for object detection,.

Unlocking the Potential of Weakly Labeled Data: A Co-Evolutionary Learning Framework for Abnormality Detection and Report Generation Consistency-based semi- supervised learning for object detection,

Reference 36

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Unlocking the Potential of Weakly Labeled Data: A Co-Evolutionary Learning Framework for Abnormality Detection and Report Generation Proposal learning for semi-supervised object detection,

Reference 37

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This paper cites On the Automatic Generation of Medical Imaging Reports.

Unlocking the Potential of Weakly Labeled Data: A Co-Evolutionary Learning Framework for Abnormality Detection and Report Generation On the Automatic Generation of Medical Imaging Reports

Reference 38

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This paper cites Multimodal recurrent model with attention for automated radiology report generation,.

Unlocking the Potential of Weakly Labeled Data: A Co-Evolutionary Learning Framework for Abnormality Detection and Report Generation Multimodal recurrent model with attention for automated radiology report generation,

Reference 39

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This paper cites Automatic radiology report generation based on multi-view image fusion and medical concept enrichment,.

Unlocking the Potential of Weakly Labeled Data: A Co-Evolutionary Learning Framework for Abnormality Detection and Report Generation Automatic radiology report generation based on multi-view image fusion and medical concept enrichment,

Reference 40

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This paper cites PromptMRG: Diagnosis-driven prompts for medical report generation,.

Unlocking the Potential of Weakly Labeled Data: A Co-Evolutionary Learning Framework for Abnormality Detection and Report Generation PromptMRG: Diagnosis-driven prompts for medical report generation,

Reference 41

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

Unlocking the Potential of Weakly Labeled Data: A Co-Evolutionary Learning Framework for Abnormality Detection and Report Generation Learning transferable visual models from natural language supervision,

Reference 42

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This paper cites BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs.

Unlocking the Potential of Weakly Labeled Data: A Co-Evolutionary Learning Framework for Abnormality Detection and Report Generation BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs

Reference 43

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Unlocking the Potential of Weakly Labeled Data: A Co-Evolutionary Learning Framework for Abnormality Detection and Report Generation GPT-4 Technical Report

Reference 44

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This paper cites LLaMA: Open and Efficient Foundation Language Models.

Unlocking the Potential of Weakly Labeled Data: A Co-Evolutionary Learning Framework for Abnormality Detection and Report Generation LLaMA: Open and Efficient Foundation Language Models

Reference 45

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This paper cites Domain-specific language model pretraining for biomed- ical natural language processing,.

Unlocking the Potential of Weakly Labeled Data: A Co-Evolutionary Learning Framework for Abnormality Detection and Report Generation Domain-specific language model pretraining for biomed- ical natural language processing,

Reference 46

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Observation 81eb7d6c-20e8-4b23-b1c4-8530ae6ba845 · outbound

This paper cites BioBERT: a pre-trained biomedical language represen- tation model for biomedical text mining,.

Unlocking the Potential of Weakly Labeled Data: A Co-Evolutionary Learning Framework for Abnormality Detection and Report Generation BioBERT: a pre-trained biomedical language represen- tation model for biomedical text mining,

Reference 47

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Observation 5c67d236-c312-4d0c-b2b0-edf752a49752 · outbound

This paper cites Flamingo: a visual language model for few-shot learning,.

Unlocking the Potential of Weakly Labeled Data: A Co-Evolutionary Learning Framework for Abnormality Detection and Report Generation Flamingo: a visual language model for few-shot learning,

Reference 48

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Observation bfe81834-379f-4856-a0b2-c8471692022d · outbound

This paper cites CogVLM: Visual Expert for Pretrained Language Models.

Unlocking the Potential of Weakly Labeled Data: A Co-Evolutionary Learning Framework for Abnormality Detection and Report Generation CogVLM: Visual Expert for Pretrained Language Models

Reference 49

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Observation ff10bf83-a93c-4759-af76-1b69ac596e25 · outbound

This paper cites Visual instruction tuning,.

Unlocking the Potential of Weakly Labeled Data: A Co-Evolutionary Learning Framework for Abnormality Detection and Report Generation Visual instruction tuning,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:03:45.135221Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation a81cb653-6f7a-4fcd-b428-6c5987a1cab6 · outbound

This paper cites LLaV A-Med: Training a large language-and-vision assistant for biomedicine in one day,.

Unlocking the Potential of Weakly Labeled Data: A Co-Evolutionary Learning Framework for Abnormality Detection and Report Generation LLaV A-Med: Training a large language-and-vision assistant for biomedicine in one day,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:03:45.112477Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 8506123e-10f1-45c7-abd9-ca55855b581c · outbound

This paper cites XrayGPT: Chest Radiographs Summarization using Medical Vision-Language Models.

Unlocking the Potential of Weakly Labeled Data: A Co-Evolutionary Learning Framework for Abnormality Detection and Report Generation XrayGPT: Chest Radiographs Summarization using Medical Vision-Language Models

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-11T13:03:42.385339Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 09363e10-1b78-4d70-bcb5-0ffec1faf549 · outbound

This paper cites CheXagent: Towards a foundation model for chest x-ray interpretation,.

Unlocking the Potential of Weakly Labeled Data: A Co-Evolutionary Learning Framework for Abnormality Detection and Report Generation CheXagent: Towards a foundation model for chest x-ray interpretation,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:03:45.075224Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation b2de17b6-a825-4a8b-9bdd-6652abc80d41 · outbound

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

Unlocking the Potential of Weakly Labeled Data: A Co-Evolutionary Learning Framework for Abnormality Detection and Report Generation An image is worth 16x16 words: Transformers for image recognition at scale,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:03:45.035074Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 5dce3699-f6e9-4614-8343-a10261d1ce82 · outbound

This paper cites Cross-modal Memory Networks for Radiology Report Generation.

Unlocking the Potential of Weakly Labeled Data: A Co-Evolutionary Learning Framework for Abnormality Detection and Report Generation Cross-modal Memory Networks for Radiology Report Generation

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-11T13:03:42.408694Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8433d86a-110e-438a-a461-82ae302bd714 · outbound

This paper cites Born again neural networks,.

Unlocking the Potential of Weakly Labeled Data: A Co-Evolutionary Learning Framework for Abnormality Detection and Report Generation Born again neural networks,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:03:44.992424Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation dabc4527-6d75-431a-b927-8daa0fa9f5ef · outbound

This paper cites MIMIC-CXR-JPG, a large publicly available database of labeled chest radiographs.

Unlocking the Potential of Weakly Labeled Data: A Co-Evolutionary Learning Framework for Abnormality Detection and Report Generation MIMIC-CXR-JPG, a large publicly available database of labeled chest radiographs

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-11T13:03:42.425908Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c07c0888-2cbe-4d5b-abe9-57fd11e52229 · outbound

This paper cites The PASCAL visual object classes (VOC) challenge,.

Unlocking the Potential of Weakly Labeled Data: A Co-Evolutionary Learning Framework for Abnormality Detection and Report Generation The PASCAL visual object classes (VOC) challenge,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:03:44.953528Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 2738f84a-e663-491c-9eae-20b4a2d7c1db · outbound

This paper cites BLEU: A method for automatic evaluation of machine translation,.

Unlocking the Potential of Weakly Labeled Data: A Co-Evolutionary Learning Framework for Abnormality Detection and Report Generation BLEU: A method for automatic evaluation of machine translation,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:03:44.916930Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 2d759960-dad3-4842-ae10-47cf956aaa41 · outbound

This paper cites METEOR: An automatic metric for MT evaluation with improved correlation with human judgments,.

Unlocking the Potential of Weakly Labeled Data: A Co-Evolutionary Learning Framework for Abnormality Detection and Report Generation METEOR: An automatic metric for MT evaluation with improved correlation with human judgments,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:03:44.628976Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 64dce0c4-6a60-4cd6-8905-cc2bbf722de0 · outbound

This paper cites ROUGE: A package for automatic evaluation of summaries,.

Unlocking the Potential of Weakly Labeled Data: A Co-Evolutionary Learning Framework for Abnormality Detection and Report Generation ROUGE: A package for automatic evaluation of summaries,

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-11T13:03:42.654917Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1a73ce24-761b-49a1-aaea-3b1b9f33ba26 · outbound

This paper cites CheXbert: Combining Automatic Labelers and Expert Annotations for Accurate Radiology Report Labeling Using BERT.

Unlocking the Potential of Weakly Labeled Data: A Co-Evolutionary Learning Framework for Abnormality Detection and Report Generation CheXbert: Combining Automatic Labelers and Expert Annotations for Accurate Radiology Report Labeling Using BERT

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-11T13:03:42.741865Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5679de9c-73a5-4bdd-bb6d-428335735ed4 · outbound

This paper cites PyTorch: An imperative style, high-performance deep learning library,.

Unlocking the Potential of Weakly Labeled Data: A Co-Evolutionary Learning Framework for Abnormality Detection and Report Generation PyTorch: An imperative style, high-performance deep learning library,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:03:44.584653Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation a50aa243-909d-4823-ac1a-cc98c91b5b52 · outbound

This paper cites Deep residual learning for image recognition,.

Unlocking the Potential of Weakly Labeled Data: A Co-Evolutionary Learning Framework for Abnormality Detection and Report Generation Deep residual learning for image recognition,

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-11T13:03:42.846580Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5eed8481-4056-488b-a1cb-a64f184f5f00 · outbound

This paper cites ImageNet classification with deep convolutional neural networks,.

Unlocking the Potential of Weakly Labeled Data: A Co-Evolutionary Learning Framework for Abnormality Detection and Report Generation ImageNet classification with deep convolutional neural networks,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:03:44.528070Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 3a1dc7e8-5810-40e3-81d1-01efb225d538 · outbound

This paper cites Feature pyramid networks for object detection,.

Unlocking the Potential of Weakly Labeled Data: A Co-Evolutionary Learning Framework for Abnormality Detection and Report Generation Feature pyramid networks for object detection,

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-11T13:03:42.982560Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9fe51a08-d0ab-4f1a-82ee-65331675bdc4 · outbound

This paper cites GLIPv2: Unifying localization and vision-language understanding,.

Unlocking the Potential of Weakly Labeled Data: A Co-Evolutionary Learning Framework for Abnormality Detection and Report Generation GLIPv2: Unifying localization and vision-language understanding,

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:03:44.460867Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 58dbb1e2-38b7-47cc-a751-54bef9112f22 · outbound

This paper cites ORGAN: Observation- guided radiology report generation via tree reasoning,.

Unlocking the Potential of Weakly Labeled Data: A Co-Evolutionary Learning Framework for Abnormality Detection and Report Generation ORGAN: Observation- guided radiology report generation via tree reasoning,

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:03:44.433319Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation bbb92786-5a8f-493d-8349-bb640cbb2af5 · outbound

This paper cites EVA-CLIP: Improved Training Techniques for CLIP at Scale.

Unlocking the Potential of Weakly Labeled Data: A Co-Evolutionary Learning Framework for Abnormality Detection and Report Generation EVA-CLIP: Improved Training Techniques for CLIP at Scale

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-11T13:03:43.145169Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 39452b6b-94de-4b8e-ba96-898d698273d6 · outbound

This paper cites MedCLIP: Contrastive learning from unpaired medical images and text,.

Unlocking the Potential of Weakly Labeled Data: A Co-Evolutionary Learning Framework for Abnormality Detection and Report Generation MedCLIP: Contrastive learning from unpaired medical images and text,

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:03:44.389182Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 872eaf13-4b85-4b4a-b624-2febe25b8572 · outbound

This paper cites Automatic bounding box annotation of chest X-ray data for localization of abnormalities,.

Unlocking the Potential of Weakly Labeled Data: A Co-Evolutionary Learning Framework for Abnormality Detection and Report Generation Automatic bounding box annotation of chest X-ray data for localization of abnormalities,

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:03:44.355132Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 9d21f743-9293-42d7-81dd-b07a869b4871 · outbound

This paper cites METransformer: Radiology report generation by Transformer with multiple learnable expert tokens,.

Unlocking the Potential of Weakly Labeled Data: A Co-Evolutionary Learning Framework for Abnormality Detection and Report Generation METransformer: Radiology report generation by Transformer with multiple learnable expert tokens,

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:03:44.311236Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 0ca707b8-3923-4f96-b903-c818d6adbc4a · outbound

This paper cites Towards open world object detection,.

Unlocking the Potential of Weakly Labeled Data: A Co-Evolutionary Learning Framework for Abnormality Detection and Report Generation Towards open world object detection,

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:03:44.280483Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation b5714f74-e32a-4dec-b380-8f0a4c99f0cf · outbound

This paper cites Towards open- set object detection and discovery,.

Unlocking the Potential of Weakly Labeled Data: A Co-Evolutionary Learning Framework for Abnormality Detection and Report Generation Towards open- set object detection and discovery,

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:03:44.245942Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers