Continually Evolved Multimodal Foundation Models for Cancer Prognosis | Paper Citation Record

Observation 5df1b587-fbf9-4638-b50c-4a0771f7b11a · outbound

This paper cites The application of deep learning in cancer prognosis prediction.

Continually Evolved Multimodal Foundation Models for Cancer Prognosis The application of deep learning in cancer prognosis prediction

Reference 1

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Observation aafec957-a1c8-4fc9-b5d0-703ef3058649 · outbound

This paper cites Global cancer statistics 2020: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries.

Continually Evolved Multimodal Foundation Models for Cancer Prognosis Global cancer statistics 2020: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries

Reference 2

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This paper cites Trends in cancer prognosis in a population-based cohort survey: can recent advances in cancer therapy affect the prognosis? Cancer Epidemiology, 39(1):97–103, 2015.

Continually Evolved Multimodal Foundation Models for Cancer Prognosis Trends in cancer prognosis in a population-based cohort survey: can recent advances in cancer therapy affect the prognosis? Cancer Epidemiology, 39(1):97–103, 2015

Reference 3

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Observation 543f4d6d-468c-4d3b-8fcb-f28ea7ba8fbf · outbound

This paper cites Multimodal adversarial representation learning for breast cancer prognosis prediction.

Continually Evolved Multimodal Foundation Models for Cancer Prognosis Multimodal adversarial representation learning for breast cancer prognosis prediction

Reference 4

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Observation 984218a1-7f62-421f-a5f1-a834e5e86adc · outbound

This paper cites Mbfusion: Multi- modal balanced fusion and multi-task learning for cancer diagnosis and prognosis.

Continually Evolved Multimodal Foundation Models for Cancer Prognosis Mbfusion: Multi- modal balanced fusion and multi-task learning for cancer diagnosis and prognosis

Reference 5

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Observation fa0199c2-6485-4086-a945-7f92d5166c03 · outbound

This paper cites Machine learning applications in cancer prognosis and prediction.

Continually Evolved Multimodal Foundation Models for Cancer Prognosis Machine learning applications in cancer prognosis and prediction

Reference 6

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Observation f72a0458-55b2-48cd-bd28-c952dd6a6bdf · outbound

This paper cites Pathology-and-genomics multimodal transformer for survival outcome prediction.

Continually Evolved Multimodal Foundation Models for Cancer Prognosis Pathology-and-genomics multimodal transformer for survival outcome prediction

Reference 7

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Observation c4197869-b632-482c-8bff-1aa6824c7578 · outbound

This paper cites A pathology foundation model for cancer diagnosis and prognosis prediction.

Continually Evolved Multimodal Foundation Models for Cancer Prognosis A pathology foundation model for cancer diagnosis and prognosis prediction

Reference 8

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Observation f3c09a7a-f2fe-4968-95be-9cf51e1ac1e8 · outbound

This paper cites Llm-guided multi-modal multiple instance learning for 5-year overall survival prediction of lung cancer.

Continually Evolved Multimodal Foundation Models for Cancer Prognosis Llm-guided multi-modal multiple instance learning for 5-year overall survival prediction of lung cancer

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T00:29:02.599139Z

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Observation 70d570c0-77ac-46be-aadd-e6185c1a3029 · outbound

This paper cites Multimodal Whole Slide Foundation Model for Pathology.

Continually Evolved Multimodal Foundation Models for Cancer Prognosis Multimodal Whole Slide Foundation Model for Pathology

Reference 10

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Observation 604322be-5d64-4fa9-872b-11b5811433ed · outbound

This paper cites Clinical applications of continual learning machine learning.

Continually Evolved Multimodal Foundation Models for Cancer Prognosis Clinical applications of continual learning machine learning

Reference 11

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Observation 91185445-8d17-4ffb-b75f-55a1199982be · outbound

This paper cites Integrating multimodal information in large pretrained transformers.

Continually Evolved Multimodal Foundation Models for Cancer Prognosis Integrating multimodal information in large pretrained transformers

Reference 12

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Observation f3614ec9-4bd9-4f65-b4a7-0b95f4c26c7b · outbound

This paper cites Multimodal transformer for unaligned multimodal language sequences.

Continually Evolved Multimodal Foundation Models for Cancer Prognosis Multimodal transformer for unaligned multimodal language sequences

Reference 13

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Observation 83623341-d02b-4194-a3f2-957cd0cdc968 · outbound

This paper cites 3 4 5 Brat Daniel J.

Continually Evolved Multimodal Foundation Models for Cancer Prognosis 3 4 5 Brat Daniel J

Reference 14

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Observation 898b48ed-d4f6-4877-b971-8f611f8d4439 · outbound

This paper cites Attention is all you need.

Continually Evolved Multimodal Foundation Models for Cancer Prognosis Attention is all you need

Reference 15

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Observation 2a4b23e4-c963-4110-aed6-6230c1570699 · outbound

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

Continually Evolved Multimodal Foundation Models for Cancer Prognosis Learning transferable visual models from natural language supervision

Reference 16

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Observation 635511b8-051f-457f-bb17-417960570457 · outbound

This paper cites Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and- language tasks.

Continually Evolved Multimodal Foundation Models for Cancer Prognosis Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and- language tasks

Reference 17

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Observation 078eb2e0-9d36-4eaa-a50d-391b15789054 · outbound

This paper cites Lxmert: Learning cross-modality encoder representations from transformers.

Continually Evolved Multimodal Foundation Models for Cancer Prognosis Lxmert: Learning cross-modality encoder representations from transformers

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T00:29:02.521469Z

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Observation 0826b44e-4c8e-4980-aac1-e3b5a3e295dc · outbound

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

Continually Evolved Multimodal Foundation Models for Cancer Prognosis Flamingo: a visual language model for few-shot learning

Reference 19

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Observation 0143d2da-28b0-4d20-93dd-aa6cf645dd8f · outbound

This paper cites Gpt-4v(ision) system card.

Continually Evolved Multimodal Foundation Models for Cancer Prognosis Gpt-4v(ision) system card

Reference 20

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Observation 71a71252-d819-494e-b72e-1d40ff97cc65 · outbound

This paper cites Large Language Models for Disease Diagnosis: A Scoping Review.

Continually Evolved Multimodal Foundation Models for Cancer Prognosis Large Language Models for Disease Diagnosis: A Scoping Review

Reference 21

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This paper cites Multi-modal medical image diagnosis.

Continually Evolved Multimodal Foundation Models for Cancer Prognosis Multi-modal medical image diagnosis

Reference 22

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Observation 2a197725-cb20-4dfb-8efa-c94d7b9d4e32 · outbound

This paper cites Modality-Aware Integration with Large Language Models for Knowledge-based Visual Question Answering.

Continually Evolved Multimodal Foundation Models for Cancer Prognosis Modality-Aware Integration with Large Language Models for Knowledge-based Visual Question Answering

Reference 23

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Observation 5224d3b9-3afe-4bf3-a6ab-21684a0ce2fe · outbound

This paper cites Advances in multimodal human-computer interaction.

Continually Evolved Multimodal Foundation Models for Cancer Prognosis Advances in multimodal human-computer interaction

Reference 24

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Observation 88b6fa33-f8bf-4cb8-aba8-63663206a508 · outbound

This paper cites On the opportunities and risks of foundation models.

Continually Evolved Multimodal Foundation Models for Cancer Prognosis On the opportunities and risks of foundation models

Reference 25

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This paper cites Bert: Pre-training of deep bidirectional transformers.

Continually Evolved Multimodal Foundation Models for Cancer Prognosis Bert: Pre-training of deep bidirectional transformers

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T00:29:02.474051Z

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This paper cites Language models are unsupervised multitask learners.

Continually Evolved Multimodal Foundation Models for Cancer Prognosis Language models are unsupervised multitask learners

Reference 27

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This paper cites Language models are few-shot learners.

Continually Evolved Multimodal Foundation Models for Cancer Prognosis Language models are few-shot learners

Reference 28

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Observation 1681b07f-889e-4f3e-9759-09c4df8560ed · outbound

This paper cites Scaling laws for neural language models.

Continually Evolved Multimodal Foundation Models for Cancer Prognosis Scaling laws for neural language models

Reference 29

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Observation 40955e78-f20a-4f5d-88cc-b0299c67fc73 · outbound

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

Continually Evolved Multimodal Foundation Models for Cancer Prognosis Learning transferable visual models from natural language supervision

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T00:29:02.436893Z

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This paper cites Zero-shot text-to-image generation.

Continually Evolved Multimodal Foundation Models for Cancer Prognosis Zero-shot text-to-image generation

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T00:29:02.427693Z

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Observation 9ba3ee24-2e80-42e3-bab6-43fbca5b1091 · outbound

This paper cites High-resolution image synthesis with latent diffusion models.

Continually Evolved Multimodal Foundation Models for Cancer Prognosis High-resolution image synthesis with latent diffusion models

Reference 32

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This paper cites Gpt-4 technical report.

Continually Evolved Multimodal Foundation Models for Cancer Prognosis Gpt-4 technical report

Reference 33

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This paper cites Highly accurate protein structure prediction with alphafold.

Continually Evolved Multimodal Foundation Models for Cancer Prognosis Highly accurate protein structure prediction with alphafold

Reference 34

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This paper cites Palm: Scaling language modeling with pathways.

Continually Evolved Multimodal Foundation Models for Cancer Prognosis Palm: Scaling language modeling with pathways

Reference 35

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This paper cites Rt-1: Robotics transformer for real-world control.

Continually Evolved Multimodal Foundation Models for Cancer Prognosis Rt-1: Robotics transformer for real-world control

Reference 36

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This paper cites On the dangers of stochastic parrots.

Continually Evolved Multimodal Foundation Models for Cancer Prognosis On the dangers of stochastic parrots

Reference 37

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This paper cites Ethical and social risks of harm from language models.

Continually Evolved Multimodal Foundation Models for Cancer Prognosis Ethical and social risks of harm from language models

Reference 38

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Observation 59e3db01-6a16-4b85-a7ca-555d8c295126 · outbound

This paper cites Crema: Generalizable and efficient video- language reasoning via multimodal modular fusion.

Continually Evolved Multimodal Foundation Models for Cancer Prognosis Crema: Generalizable and efficient video- language reasoning via multimodal modular fusion

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T00:29:02.353206Z

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Observation 2ed75b0b-adda-4726-b4c6-167f3e91a0fa · outbound

This paper cites Pathformer: a biological pathway informed transformer for disease diagnosis and prognosis using multi-omics data.

Continually Evolved Multimodal Foundation Models for Cancer Prognosis Pathformer: a biological pathway informed transformer for disease diagnosis and prognosis using multi-omics data

Reference 40

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This paper cites Multimodal Data Integration for Precision Oncology: Challenges and Future Directions.

Continually Evolved Multimodal Foundation Models for Cancer Prognosis Multimodal Data Integration for Precision Oncology: Challenges and Future Directions

Reference 41

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This paper cites Multimodal prototyping for cancer survival prediction.

Continually Evolved Multimodal Foundation Models for Cancer Prognosis Multimodal prototyping for cancer survival prediction

Reference 42

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Observation 532c41f3-70ce-4a69-804f-a6b211606d6a · outbound

This paper cites Samms: Multi-modality deep learning with the foundation model for the prediction of cancer patient survival.

Continually Evolved Multimodal Foundation Models for Cancer Prognosis Samms: Multi-modality deep learning with the foundation model for the prediction of cancer patient survival

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T00:29:02.324005Z

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Observation efc7d69d-77d1-4446-bce9-140bf8196919 · outbound

This paper cites A Multimodal Knowledge-enhanced Whole-slide Pathology Foundation Model.

Continually Evolved Multimodal Foundation Models for Cancer Prognosis A Multimodal Knowledge-enhanced Whole-slide Pathology Foundation Model

Reference 44

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This paper cites A continual learning survey: Defying forgetting in classification tasks.

Continually Evolved Multimodal Foundation Models for Cancer Prognosis A continual learning survey: Defying forgetting in classification tasks

Reference 45

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This paper cites Overcoming catastrophic forgetting in incremental few-shot learning by finding flat minima.

Continually Evolved Multimodal Foundation Models for Cancer Prognosis Overcoming catastrophic forgetting in incremental few-shot learning by finding flat minima

Reference 46

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Observation 29c85e94-7486-43a6-9a96-636e5eb4db9b · outbound

This paper cites A comprehensive survey of continual learning: theory, method and application.

Continually Evolved Multimodal Foundation Models for Cancer Prognosis A comprehensive survey of continual learning: theory, method and application

Reference 47

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This paper cites Recent advances of foundation language models-based continual learning: A survey.

Continually Evolved Multimodal Foundation Models for Cancer Prognosis Recent advances of foundation language models-based continual learning: A survey

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T00:29:02.291083Z

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Observation d6b7f3a3-1376-4f5c-9496-6f7e337d1309 · outbound

This paper cites Continual Learning of Large Language Models: A Comprehensive Survey.

Continually Evolved Multimodal Foundation Models for Cancer Prognosis Continual Learning of Large Language Models: A Comprehensive Survey

Reference 49

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This paper cites Continual Learning for Large Language Models: A Survey.

Continually Evolved Multimodal Foundation Models for Cancer Prognosis Continual Learning for Large Language Models: A Survey

Reference 50

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Observation f2adaacd-5bea-4c20-87c7-7b84c694fa28 · outbound

This paper cites Efficient continual pre-training for building domain specific large language models.

Continually Evolved Multimodal Foundation Models for Cancer Prognosis Efficient continual pre-training for building domain specific large language models

Reference 51

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Observation 06b9bcb6-c71e-47e4-9832-b0bc90d2ee2e · outbound

This paper cites Large- scale lifelong learning of in-context instructions and how to tackle it.

Continually Evolved Multimodal Foundation Models for Cancer Prognosis Large- scale lifelong learning of in-context instructions and how to tackle it

Reference 52

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This paper cites COPR: Continual Learning Human Preference through Optimal Policy Regularization.

Continually Evolved Multimodal Foundation Models for Cancer Prognosis COPR: Continual Learning Human Preference through Optimal Policy Regularization

Reference 53

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Observation 4657aa21-c849-4fbe-a867-7953a48e2a0f · outbound

This paper cites Modality-Inconsistent Continual Learning of Multimodal Large Language Models.

Continually Evolved Multimodal Foundation Models for Cancer Prognosis Modality-Inconsistent Continual Learning of Multimodal Large Language Models

Reference 54

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Observation 03bdeb5d-f625-4810-8f9e-5c5162c09399 · outbound

This paper cites ModalPrompt: Towards Efficient Multimodal Continual Instruction Tuning with Dual-Modality Guided Prompt.

Continually Evolved Multimodal Foundation Models for Cancer Prognosis ModalPrompt: Towards Efficient Multimodal Continual Instruction Tuning with Dual-Modality Guided Prompt

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-10T00:29:02.023372Z

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Observation 85baab97-ab37-4fb8-a0be-6c7e13fa04b0 · outbound

This paper cites LLM-Assisted Multi-Teacher Continual Learning for Visual Question Answering in Robotic Surgery.

Continually Evolved Multimodal Foundation Models for Cancer Prognosis LLM-Assisted Multi-Teacher Continual Learning for Visual Question Answering in Robotic Surgery

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-10T00:29:02.026335Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 14d5dba6-f46f-4b60-8b6d-81d8892dcbbd · outbound

This paper cites BLIP-2: Bootstrapping language- image pre-training with frozen image encoders and large language models.

Continually Evolved Multimodal Foundation Models for Cancer Prognosis BLIP-2: Bootstrapping language- image pre-training with frozen image encoders and large language models

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T00:29:02.274996Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation baeb0250-89f7-4292-bb67-7cf43de2dab3 · outbound

This paper cites marugoto: Machine learning for medical images, 2024.

Continually Evolved Multimodal Foundation Models for Cancer Prognosis marugoto: Machine learning for medical images, 2024

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T00:29:02.264696Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 8b203217-5ea5-4d8c-a2b4-b07a4dd0ffa8 · outbound

This paper cites Bulkrnabert: Cancer prognosis from bulk rna-seq based language models.

Continually Evolved Multimodal Foundation Models for Cancer Prognosis Bulkrnabert: Cancer prognosis from bulk rna-seq based language models

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T00:29:02.254556Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers