Unifying Biomedical Vision-Language Expertise: Towards a Generalist Foundation Model via Multi-CLIP Knowledge Distillation | Paper Citation Record

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This paper cites Clip and complementary methods.

Unifying Biomedical Vision-Language Expertise: Towards a Generalist Foundation Model via Multi-CLIP Knowledge Distillation Clip and complementary methods

Reference 1

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

Unifying Biomedical Vision-Language Expertise: Towards a Generalist Foundation Model via Multi-CLIP Knowledge Distillation Learning transferable visual models from natural language supervision

Reference 2

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This paper cites Improved baselines with visual instruction tuning.

Unifying Biomedical Vision-Language Expertise: Towards a Generalist Foundation Model via Multi-CLIP Knowledge Distillation Improved baselines with visual instruction tuning

Reference 3

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This paper cites Foundation models for generalist medical artificial intelligence.

Unifying Biomedical Vision-Language Expertise: Towards a Generalist Foundation Model via Multi-CLIP Knowledge Distillation Foundation models for generalist medical artificial intelligence

Reference 4

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

Unifying Biomedical Vision-Language Expertise: Towards a Generalist Foundation Model via Multi-CLIP Knowledge Distillation Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation

Reference 5

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This paper cites Cxr-clip: Toward large scale chest x-ray language-image pre-training.

Unifying Biomedical Vision-Language Expertise: Towards a Generalist Foundation Model via Multi-CLIP Knowledge Distillation Cxr-clip: Toward large scale chest x-ray language-image pre-training

Reference 6

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This paper cites Merlin: A vision language foundation model for 3d computed tomography.

Unifying Biomedical Vision-Language Expertise: Towards a Generalist Foundation Model via Multi-CLIP Knowledge Distillation Merlin: A vision language foundation model for 3d computed tomography

Reference 7

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This paper cites Artificial intelligence for multimodal data integration in oncology.

Unifying Biomedical Vision-Language Expertise: Towards a Generalist Foundation Model via Multi-CLIP Knowledge Distillation Artificial intelligence for multimodal data integration in oncology

Reference 8

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This paper cites Triad: Vision Foundation Model for 3D Magnetic Resonance Imaging.

Unifying Biomedical Vision-Language Expertise: Towards a Generalist Foundation Model via Multi-CLIP Knowledge Distillation Triad: Vision Foundation Model for 3D Magnetic Resonance Imaging

Reference 9

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This paper cites A whole-slide foundation model for digital pathology from real-world data.

Unifying Biomedical Vision-Language Expertise: Towards a Generalist Foundation Model via Multi-CLIP Knowledge Distillation A whole-slide foundation model for digital pathology from real-world data

Reference 10

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This paper cites A visual–language foundation model for pathology image analysis using medical twitter.

Unifying Biomedical Vision-Language Expertise: Towards a Generalist Foundation Model via Multi-CLIP Knowledge Distillation A visual–language foundation model for pathology image analysis using medical twitter

Reference 11

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This paper cites A foundation model for generalizable disease detection from retinal images.

Unifying Biomedical Vision-Language Expertise: Towards a Generalist Foundation Model via Multi-CLIP Knowledge Distillation A foundation model for generalizable disease detection from retinal images

Reference 12

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This paper cites OphCLIP: Hierarchical Retrieval-Augmented Learning for Ophthalmic Surgical Video-Language Pretraining.

Unifying Biomedical Vision-Language Expertise: Towards a Generalist Foundation Model via Multi-CLIP Knowledge Distillation OphCLIP: Hierarchical Retrieval-Augmented Learning for Ophthalmic Surgical Video-Language Pretraining

Reference 13

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This paper cites Transparent medical image ai via an image–text foundation model grounded in medical literature.

Unifying Biomedical Vision-Language Expertise: Towards a Generalist Foundation Model via Multi-CLIP Knowledge Distillation Transparent medical image ai via an image–text foundation model grounded in medical literature

Reference 14

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This paper cites A generalist vision–language foundation model for diverse biomedical tasks.

Unifying Biomedical Vision-Language Expertise: Towards a Generalist Foundation Model via Multi-CLIP Knowledge Distillation A generalist vision–language foundation model for diverse biomedical tasks

Reference 15

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This paper cites Quantifying the Reasoning Abilities of LLMs on Real-world Clinical Cases.

Unifying Biomedical Vision-Language Expertise: Towards a Generalist Foundation Model via Multi-CLIP Knowledge Distillation Quantifying the Reasoning Abilities of LLMs on Real-world Clinical Cases

Reference 16

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This paper cites Unsupervised mri motion artifact disentanglement: introducing maudgan.

Unifying Biomedical Vision-Language Expertise: Towards a Generalist Foundation Model via Multi-CLIP Knowledge Distillation Unsupervised mri motion artifact disentanglement: introducing maudgan

Reference 17

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This paper cites Mri super-resolution reconstruction using efficient diffusion probabilistic model with residual shifting.

Unifying Biomedical Vision-Language Expertise: Towards a Generalist Foundation Model via Multi-CLIP Knowledge Distillation Mri super-resolution reconstruction using efficient diffusion probabilistic model with residual shifting

Reference 18

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This paper cites Multi-Modal Explainable Medical AI Assistant for Trustworthy Human-AI Collaboration.

Unifying Biomedical Vision-Language Expertise: Towards a Generalist Foundation Model via Multi-CLIP Knowledge Distillation Multi-Modal Explainable Medical AI Assistant for Trustworthy Human-AI Collaboration

Reference 19

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This paper cites Deep learning based multimodal biomedical data fusion: An overview and comparative review.

Unifying Biomedical Vision-Language Expertise: Towards a Generalist Foundation Model via Multi-CLIP Knowledge Distillation Deep learning based multimodal biomedical data fusion: An overview and comparative review

Reference 20

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This paper cites The future of multimodal artificial intelligence models for integrating imaging and clinical metadata: a narrative review.

Unifying Biomedical Vision-Language Expertise: Towards a Generalist Foundation Model via Multi-CLIP Knowledge Distillation The future of multimodal artificial intelligence models for integrating imaging and clinical metadata: a narrative review

Reference 21

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Unifying Biomedical Vision-Language Expertise: Towards a Generalist Foundation Model via Multi-CLIP Knowledge Distillation Pmc open access subset

Reference 22

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This paper cites Pmc-clip: Contrastive language-image pre-training using biomedical documents.

Unifying Biomedical Vision-Language Expertise: Towards a Generalist Foundation Model via Multi-CLIP Knowledge Distillation Pmc-clip: Contrastive language-image pre-training using biomedical documents

Reference 23

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This paper cites BIOMEDICA: An Open Biomedical Image-Caption Archive, Dataset, and Vision-Language Models Derived from Scientific Literature.

Unifying Biomedical Vision-Language Expertise: Towards a Generalist Foundation Model via Multi-CLIP Knowledge Distillation BIOMEDICA: An Open Biomedical Image-Caption Archive, Dataset, and Vision-Language Models Derived from Scientific Literature

Reference 24

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This paper cites An Explainable Biomedical Foundation Model via Large-Scale Concept-Enhanced Vision-Language Pre-training.

Unifying Biomedical Vision-Language Expertise: Towards a Generalist Foundation Model via Multi-CLIP Knowledge Distillation An Explainable Biomedical Foundation Model via Large-Scale Concept-Enhanced Vision-Language Pre-training

Reference 25

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Unifying Biomedical Vision-Language Expertise: Towards a Generalist Foundation Model via Multi-CLIP Knowledge Distillation Laion-5b: An open large-scale dataset for training next generation image-text models

Reference 26

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Unifying Biomedical Vision-Language Expertise: Towards a Generalist Foundation Model via Multi-CLIP Knowledge Distillation Clip-kd: An empirical study of clip model distillation

Reference 27

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Unifying Biomedical Vision-Language Expertise: Towards a Generalist Foundation Model via Multi-CLIP Knowledge Distillation Enabling Multimodal Generation on CLIP via Vision-Language Knowledge Distillation

Reference 28

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Unifying Biomedical Vision-Language Expertise: Towards a Generalist Foundation Model via Multi-CLIP Knowledge Distillation Medicalnarratives: Connecting medical vision and language with localized narratives

Reference 29

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Unifying Biomedical Vision-Language Expertise: Towards a Generalist Foundation Model via Multi-CLIP Knowledge Distillation Medclip: Contrastive learning from unpaired medical images and text

Reference 30

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This paper cites A multimodal biomedical foundation model trained from fifteen million image–text pairs.

Unifying Biomedical Vision-Language Expertise: Towards a Generalist Foundation Model via Multi-CLIP Knowledge Distillation A multimodal biomedical foundation model trained from fifteen million image–text pairs

Reference 31

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This paper cites UniMed-CLIP: Towards a Unified Image-Text Pretraining Paradigm for Diverse Medical Imaging Modalities.

Unifying Biomedical Vision-Language Expertise: Towards a Generalist Foundation Model via Multi-CLIP Knowledge Distillation UniMed-CLIP: Towards a Unified Image-Text Pretraining Paradigm for Diverse Medical Imaging Modalities

Reference 32

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Unifying Biomedical Vision-Language Expertise: Towards a Generalist Foundation Model via Multi-CLIP Knowledge Distillation Unresolved cited work

Reference 33

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Unifying Biomedical Vision-Language Expertise: Towards a Generalist Foundation Model via Multi-CLIP Knowledge Distillation Quilt-1m: One million image-text pairs for histopathology

Reference 34

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Unifying Biomedical Vision-Language Expertise: Towards a Generalist Foundation Model via Multi-CLIP Knowledge Distillation MedTrinity-25M: A Large-scale Multimodal Dataset with Multigranular Annotations for Medicine

Reference 35

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This paper cites Multiple instance captioning: Learning representations from histopathol- ogy textbooks and articles.

Unifying Biomedical Vision-Language Expertise: Towards a Generalist Foundation Model via Multi-CLIP Knowledge Distillation Multiple instance captioning: Learning representations from histopathol- ogy textbooks and articles

Reference 36

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Unifying Biomedical Vision-Language Expertise: Towards a Generalist Foundation Model via Multi-CLIP Knowledge Distillation Slake: A semantically-labeled knowledge-enhanced dataset for medical visual question answering

Reference 37

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Unifying Biomedical Vision-Language Expertise: Towards a Generalist Foundation Model via Multi-CLIP Knowledge Distillation A dataset of clinically generated visual questions and answers about radiology images

Reference 38

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Unifying Biomedical Vision-Language Expertise: Towards a Generalist Foundation Model via Multi-CLIP Knowledge Distillation Komura, A

Reference 39

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Unifying Biomedical Vision-Language Expertise: Towards a Generalist Foundation Model via Multi-CLIP Knowledge Distillation Bracs: A dataset for breast carcinoma subtyping in h&e histology images

Reference 40

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Unifying Biomedical Vision-Language Expertise: Towards a Generalist Foundation Model via Multi-CLIP Knowledge Distillation The cancer genome atlas pan-cancer analysis project

Reference 41

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Unifying Biomedical Vision-Language Expertise: Towards a Generalist Foundation Model via Multi-CLIP Knowledge Distillation Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer

Reference 42

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Unifying Biomedical Vision-Language Expertise: Towards a Generalist Foundation Model via Multi-CLIP Knowledge Distillation Demystifying CLIP Data

Reference 43

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Unifying Biomedical Vision-Language Expertise: Towards a Generalist Foundation Model via Multi-CLIP Knowledge Distillation Biomedbert: A pre-trained biomedical language model for qa and ir

Reference 44

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Unifying Biomedical Vision-Language Expertise: Towards a Generalist Foundation Model via Multi-CLIP Knowledge Distillation Representation Learning with Contrastive Predictive Coding

Reference 45

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Unifying Biomedical Vision-Language Expertise: Towards a Generalist Foundation Model via Multi-CLIP Knowledge Distillation Attention-based deep multiple instance learning

Reference 46

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This paper cites A vision–language foundation model for precision oncology.

Unifying Biomedical Vision-Language Expertise: Towards a Generalist Foundation Model via Multi-CLIP Knowledge Distillation A vision–language foundation model for precision oncology

Reference 47

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This paper cites Tcga-reports: A machine-readable pathology report resource for benchmarking text-based ai models.

Unifying Biomedical Vision-Language Expertise: Towards a Generalist Foundation Model via Multi-CLIP Knowledge Distillation Tcga-reports: A machine-readable pathology report resource for benchmarking text-based ai models

Reference 48

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This paper cites Covid-net: A tailored deep convolutional neural network design for detection of covid-19 cases from chest x-ray images.

Unifying Biomedical Vision-Language Expertise: Towards a Generalist Foundation Model via Multi-CLIP Knowledge Distillation Covid-net: A tailored deep convolutional neural network design for detection of covid-19 cases from chest x-ray images

Reference 49

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This paper cites Philip Kegelmeyer.

Unifying Biomedical Vision-Language Expertise: Towards a Generalist Foundation Model via Multi-CLIP Knowledge Distillation Philip Kegelmeyer

Reference 50

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This paper cites Two public chest x-ray datasets for computer-aided screening of pulmonary diseases.

Unifying Biomedical Vision-Language Expertise: Towards a Generalist Foundation Model via Multi-CLIP Knowledge Distillation Two public chest x-ray datasets for computer-aided screening of pulmonary diseases

Reference 51

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Observation aa30a820-7b26-4770-b9ef-90e8fd7baa49 · outbound

This paper cites Siim-acr pneumothorax segmentation.

Unifying Biomedical Vision-Language Expertise: Towards a Generalist Foundation Model via Multi-CLIP Knowledge Distillation Siim-acr pneumothorax segmentation

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:09:21.082885Z

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Observation c0a280d7-d35e-4bdb-9c8f-538d9310df59 · outbound

This paper cites Augmenting the national institutes of health chest radiograph dataset with expert annotations of possible pneumonia.

Unifying Biomedical Vision-Language Expertise: Towards a Generalist Foundation Model via Multi-CLIP Knowledge Distillation Augmenting the national institutes of health chest radiograph dataset with expert annotations of possible pneumonia

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:09:20.923983Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 42c27fdf-9b20-413f-b607-899e86b31a1b · outbound

This paper cites Brain Tumor Multimodal Image (CT & MRI).

Unifying Biomedical Vision-Language Expertise: Towards a Generalist Foundation Model via Multi-CLIP Knowledge Distillation Brain Tumor Multimodal Image (CT & MRI)

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:09:20.723024Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 6b1aab37-2bd3-480c-a932-cf8c8d58d882 · outbound

This paper cites A comprehensive and easy-to-use multi-domain multi-task medical imaging meta-dataset (medimeta).

Unifying Biomedical Vision-Language Expertise: Towards a Generalist Foundation Model via Multi-CLIP Knowledge Distillation A comprehensive and easy-to-use multi-domain multi-task medical imaging meta-dataset (medimeta)

Reference 55

Resolution: verified exact; raw_fallback, observed 2026-08-06T22:09:16.178185Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f333df6c-05b5-4940-ac79-5f8651913ba4 · outbound

This paper cites Covid-net ct-2: Enhanced deep neural networks for detection of covid-19 from chest ct images through bigger, more diverse learning.

Unifying Biomedical Vision-Language Expertise: Towards a Generalist Foundation Model via Multi-CLIP Knowledge Distillation Covid-net ct-2: Enhanced deep neural networks for detection of covid-19 from chest ct images through bigger, more diverse learning

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:09:20.567384Z

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Observation db636dfa-4579-407c-966b-0135d5977f7e · outbound

This paper cites Medmnist v2-a large-scale lightweight benchmark for 2d and 3d biomedical image classification.

Unifying Biomedical Vision-Language Expertise: Towards a Generalist Foundation Model via Multi-CLIP Knowledge Distillation Medmnist v2-a large-scale lightweight benchmark for 2d and 3d biomedical image classification

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-06T22:09:12.918917Z

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Observation 28ccdc36-53ca-45ff-b104-e78844f16c9c · outbound

This paper cites Brain tumor mri dataset, 2021.

Unifying Biomedical Vision-Language Expertise: Towards a Generalist Foundation Model via Multi-CLIP Knowledge Distillation Brain tumor mri dataset, 2021

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:09:20.439724Z

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Observation 7d62a276-cf29-4906-a593-de73f042ff96 · outbound

This paper cites Deep-learning-assisted diagnosis for knee magnetic resonance imaging: development and retrospective validation of mrnet.

Unifying Biomedical Vision-Language Expertise: Towards a Generalist Foundation Model via Multi-CLIP Knowledge Distillation Deep-learning-assisted diagnosis for knee magnetic resonance imaging: development and retrospective validation of mrnet

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:09:20.273737Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e27df61b-a9eb-42e2-a5b8-a661b5a6da12 · outbound

This paper cites Role of inter-and extra-lesion tissue, transfer learning, and fine-tuning in the robust classification of breast lesions.

Unifying Biomedical Vision-Language Expertise: Towards a Generalist Foundation Model via Multi-CLIP Knowledge Distillation Role of inter-and extra-lesion tissue, transfer learning, and fine-tuning in the robust classification of breast lesions

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:09:20.106799Z

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Observation ed6b850e-401a-48b6-81bd-6a727ce0a25b · outbound

This paper cites Dataset of breast ultrasound images.

Unifying Biomedical Vision-Language Expertise: Towards a Generalist Foundation Model via Multi-CLIP Knowledge Distillation Dataset of breast ultrasound images

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-06T22:09:13.350526Z

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This paper cites Diabetic retinopathy detection.

Unifying Biomedical Vision-Language Expertise: Towards a Generalist Foundation Model via Multi-CLIP Knowledge Distillation Diabetic retinopathy detection

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:09:19.923891Z

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Observation 7d0803e5-9687-4782-8563-9e3b1d51a1ea · outbound

This paper cites Automatic detection of 39 fundus diseases and conditions in retinal photographs using deep neural networks.

Unifying Biomedical Vision-Language Expertise: Towards a Generalist Foundation Model via Multi-CLIP Knowledge Distillation Automatic detection of 39 fundus diseases and conditions in retinal photographs using deep neural networks

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:09:19.714171Z

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Observation d4a918d0-b4f0-43d7-a307-f5464308caa9 · outbound

This paper cites Fives: A fundus image dataset for artificial intelligence based vessel segmentation.

Unifying Biomedical Vision-Language Expertise: Towards a Generalist Foundation Model via Multi-CLIP Knowledge Distillation Fives: A fundus image dataset for artificial intelligence based vessel segmentation

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:09:19.502437Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 43b95e6c-1b23-493d-8b1d-9d39ddab78ac · outbound

This paper cites Retinal oct image classification - c8 [data set].

Unifying Biomedical Vision-Language Expertise: Towards a Generalist Foundation Model via Multi-CLIP Knowledge Distillation Retinal oct image classification - c8 [data set]

Reference 65

Resolution: malformed identifier; doi_truncated, observed 2026-08-06T22:09:15.930294Z

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Observation b9fa17bf-d65e-4589-b522-91aaf1ce44dc · outbound

This paper cites Bach: Grand challenge on breast cancer histology images.

Unifying Biomedical Vision-Language Expertise: Towards a Generalist Foundation Model via Multi-CLIP Knowledge Distillation Bach: Grand challenge on breast cancer histology images

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:09:19.296259Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 742742e3-5d6f-4aca-b3cb-1e3791f4fcff · outbound

This paper cites Lung and Colon Cancer Histopathological Image Dataset (LC25000).

Unifying Biomedical Vision-Language Expertise: Towards a Generalist Foundation Model via Multi-CLIP Knowledge Distillation Lung and Colon Cancer Histopathological Image Dataset (LC25000)

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-06T22:09:13.976650Z

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Observation f16b75cb-d657-42c5-88d4-61497ba19e22 · outbound

This paper cites 100,000 histological images of human colorectal cancer and healthy tissue.

Unifying Biomedical Vision-Language Expertise: Towards a Generalist Foundation Model via Multi-CLIP Knowledge Distillation 100,000 histological images of human colorectal cancer and healthy tissue

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:09:19.131819Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation b8c13085-7bfc-4541-9d6c-99e7edbf6ce6 · outbound

This paper cites Viable and necrotic tumor assessment from whole slide images of osteosarcoma using machine-learning and deep-learning models.

Unifying Biomedical Vision-Language Expertise: Towards a Generalist Foundation Model via Multi-CLIP Knowledge Distillation Viable and necrotic tumor assessment from whole slide images of osteosarcoma using machine-learning and deep-learning models

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:09:18.956386Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ad4742ad-5884-4179-8694-304c34a110c1 · outbound

This paper cites Large-scale pretraining on pathological images for fine-tuning of small pathological benchmarks.

Unifying Biomedical Vision-Language Expertise: Towards a Generalist Foundation Model via Multi-CLIP Knowledge Distillation Large-scale pretraining on pathological images for fine-tuning of small pathological benchmarks

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:09:18.749417Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 06741a13-d982-466e-b7b7-a89ab22eda96 · outbound

This paper cites Multi-class texture analysis in colorectal cancer histology.

Unifying Biomedical Vision-Language Expertise: Towards a Generalist Foundation Model via Multi-CLIP Knowledge Distillation Multi-class texture analysis in colorectal cancer histology

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:09:18.542159Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 5b87e978-db3b-4cba-a33e-ccc502280678 · outbound

This paper cites Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study.

Unifying Biomedical Vision-Language Expertise: Towards a Generalist Foundation Model via Multi-CLIP Knowledge Distillation Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:09:18.258083Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 172c08c5-e908-43ef-b367-a56b614a7271 · outbound

This paper cites Deep learning for the detection of anatomical tissue structures and neoplasms of the skin on scanned histopathological tissue sections.

Unifying Biomedical Vision-Language Expertise: Towards a Generalist Foundation Model via Multi-CLIP Knowledge Distillation Deep learning for the detection of anatomical tissue structures and neoplasms of the skin on scanned histopathological tissue sections

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:09:18.006071Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d2bdfe87-dd28-4bc8-ae8e-6844d35ef26a · outbound

This paper cites Interpretable classification of alzheimer’s disease pathologies with a convolutional neural network pipeline.

Unifying Biomedical Vision-Language Expertise: Towards a Generalist Foundation Model via Multi-CLIP Knowledge Distillation Interpretable classification of alzheimer’s disease pathologies with a convolutional neural network pipeline

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:09:17.805711Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d93fc9a5-b44a-4e3d-9dbd-589944d017a7 · outbound

This paper cites Deep learning from multiple experts improves identification of amyloid neuropathologies.

Unifying Biomedical Vision-Language Expertise: Towards a Generalist Foundation Model via Multi-CLIP Knowledge Distillation Deep learning from multiple experts improves identification of amyloid neuropathologies

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:09:17.527224Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e0dee7a3-f05a-4ae9-9ea9-5dc87caf7a55 · outbound

This paper cites Kvasir: A multi-class image dataset for computer aided gastrointestinal disease detection.

Unifying Biomedical Vision-Language Expertise: Towards a Generalist Foundation Model via Multi-CLIP Knowledge Distillation Kvasir: A multi-class image dataset for computer aided gastrointestinal disease detection

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:09:17.273052Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation bacdd138-296e-421a-9888-657aa1946bc9 · outbound

This paper cites Montalbo.

Unifying Biomedical Vision-Language Expertise: Towards a Generalist Foundation Model via Multi-CLIP Knowledge Distillation Montalbo

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:09:17.013849Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 7d4321d6-722b-4c8c-933d-b9f1d0a72b40 · outbound

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

Unifying Biomedical Vision-Language Expertise: Towards a Generalist Foundation Model via Multi-CLIP Knowledge Distillation The ham10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-06T22:09:15.369691Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 88a57ed3-838c-4a2a-b67d-abc046207477 · outbound

This paper cites Pad-ufes-20: A skin lesion dataset composed of patient data and clinical images collected from smartphones.

Unifying Biomedical Vision-Language Expertise: Towards a Generalist Foundation Model via Multi-CLIP Knowledge Distillation Pad-ufes-20: A skin lesion dataset composed of patient data and clinical images collected from smartphones

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:09:16.789470Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 4577c7e9-fe2c-46b2-8df2-ad9f69fd8699 · outbound

This paper cites Decoupled Weight Decay Regularization.

Unifying Biomedical Vision-Language Expertise: Towards a Generalist Foundation Model via Multi-CLIP Knowledge Distillation Decoupled Weight Decay Regularization

Reference 80

Resolution: malformed identifier; no resolver link, observed 2026-08-06T22:09:15.626675Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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