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Paper Citation Record · LEDGER

PRIME: Prototype-Driven Multimodal Pretraining for Cancer Prognosis with Missing Modalities

As of 4 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2604.04999.

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pith.paper-citation-record.v1
2604.04999 v1

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

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measured 36 of 36 standing notices

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

36 of 36 outbound references displayed

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Outbound references

Observation 0aa3a483-609f-4705-8b5b-1631d68f16f2 · outbound

This paper cites Machine learning and AI in cancer prognosis, prediction, and treatment selection: A critical approach,.

PRIME: Prototype-Driven Multimodal Pretraining for Cancer Prognosis with Missing Modalities Machine learning and AI in cancer prognosis, prediction, and treatment selection: A critical approach,

Reference 1

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Observation c5866dae-25d0-453f-b840-e0ec8531e9d9 · outbound

This paper cites Multivariable prognostic models: issues in developing models, evaluating assumptions and ade- quacy, and measuring and reducing errors,.

PRIME: Prototype-Driven Multimodal Pretraining for Cancer Prognosis with Missing Modalities Multivariable prognostic models: issues in developing models, evaluating assumptions and ade- quacy, and measuring and reducing errors,

Reference 2

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Observation 3f8f5c65-d9cd-4fe5-a798-b9f239c482b2 · outbound

This paper cites Harnessing multimodal data integration to advance precision oncology,.

PRIME: Prototype-Driven Multimodal Pretraining for Cancer Prognosis with Missing Modalities Harnessing multimodal data integration to advance precision oncology,

Reference 3

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Observation cb7a688a-fd63-4885-a438-184afeed9f2d · outbound

This paper cites Review the cancer genome atlas (TCGA): an immeasurable source of knowledge,.

PRIME: Prototype-Driven Multimodal Pretraining for Cancer Prognosis with Missing Modalities Review the cancer genome atlas (TCGA): an immeasurable source of knowledge,

Reference 4

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Observation 6cf55c99-f874-4a8d-82f9-60ec4adc523b · outbound

This paper cites A multimodal knowledge-enhanced whole-slide pathology foundation model,.

PRIME: Prototype-Driven Multimodal Pretraining for Cancer Prognosis with Missing Modalities A multimodal knowledge-enhanced whole-slide pathology foundation model,

Reference 5

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Observation b4e350c5-7b1a-402d-ab6f-f749119ccc40 · outbound

This paper cites Cross-modal translation and alignment for survival analysis,.

PRIME: Prototype-Driven Multimodal Pretraining for Cancer Prognosis with Missing Modalities Cross-modal translation and alignment for survival analysis,

Reference 6

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Observation 7667d4a2-b706-4c5d-ae37-845e176a58c4 · outbound

This paper cites PS3: A multimodal transformer integrating pathology reports with histology images and biological pathways for cancer survival prediction,.

PRIME: Prototype-Driven Multimodal Pretraining for Cancer Prognosis with Missing Modalities PS3: A multimodal transformer integrating pathology reports with histology images and biological pathways for cancer survival prediction,

Reference 7

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Observation 464c97d9-6a22-4ca0-8398-cae3f7e6979e · outbound

This paper cites Handling missing modalities in multimodal survival prediction for non-small cell lung cancer,.

PRIME: Prototype-Driven Multimodal Pretraining for Cancer Prognosis with Missing Modalities Handling missing modalities in multimodal survival prediction for non-small cell lung cancer,

Reference 8

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Observation 878342b3-86a6-4be2-8d6d-30f243dcad35 · outbound

This paper cites Memory- augmented incomplete multimodal survival prediction via cross-slide and gene-attentive hypergraph learning,.

PRIME: Prototype-Driven Multimodal Pretraining for Cancer Prognosis with Missing Modalities Memory- augmented incomplete multimodal survival prediction via cross-slide and gene-attentive hypergraph learning,

Reference 9

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Observation 082c23ea-3250-445f-9826-72cc9e043bf6 · outbound

This paper cites A machine learning ap- proach for multimodal data fusion for survival prediction in cancer patients,.

PRIME: Prototype-Driven Multimodal Pretraining for Cancer Prognosis with Missing Modalities A machine learning ap- proach for multimodal data fusion for survival prediction in cancer patients,

Reference 10

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Observation 60f48d61-1418-43d0-90e8-4c7e0ce1b534 · outbound

This paper cites Multimodal deep learning to predict prognosis in adult and pediatric brain tumors,.

PRIME: Prototype-Driven Multimodal Pretraining for Cancer Prognosis with Missing Modalities Multimodal deep learning to predict prognosis in adult and pediatric brain tumors,

Reference 11

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Observation bcd4b459-7974-4090-9e2b-4e78b5dcce4d · outbound

This paper cites Foundation model-enabled multimodal deep learning for prognostic prediction in colorectal cancer with incomplete modalities: A multi- institutional retrospective study,.

PRIME: Prototype-Driven Multimodal Pretraining for Cancer Prognosis with Missing Modalities Foundation model-enabled multimodal deep learning for prognostic prediction in colorectal cancer with incomplete modalities: A multi- institutional retrospective study,

Reference 12

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Observation b0c3b85a-6d96-407f-a23c-9014b0180b0a · outbound

This paper cites Long-term cancer survival prediction using multimodal deep learning,.

PRIME: Prototype-Driven Multimodal Pretraining for Cancer Prognosis with Missing Modalities Long-term cancer survival prediction using multimodal deep learning,

Reference 13

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Observation 2956f9b0-bbac-4a93-af2b-2b5e22185245 · outbound

This paper cites Multimodal data fusion: An overview of methods, challenges, and prospects,.

PRIME: Prototype-Driven Multimodal Pretraining for Cancer Prognosis with Missing Modalities Multimodal data fusion: An overview of methods, challenges, and prospects,

Reference 14

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Observation 61a86d4e-1e9f-4c74-a4f3-f9255ec25e3d · outbound

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

PRIME: Prototype-Driven Multimodal Pretraining for Cancer Prognosis with Missing Modalities Artificial intelligence for multimodal data integration in oncology,

Reference 15

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Observation 7422ff76-1178-4ae2-a374-abd8d97d5cf1 · outbound

This paper cites Tensor fusion network for multimodal sentiment analysis,.

PRIME: Prototype-Driven Multimodal Pretraining for Cancer Prognosis with Missing Modalities Tensor fusion network for multimodal sentiment analysis,

Reference 16

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Observation a156defe-43c2-4164-9edd-d3d32ec977d3 · outbound

This paper cites Integrating multimodal information in large pretrained transformers,.

PRIME: Prototype-Driven Multimodal Pretraining for Cancer Prognosis with Missing Modalities Integrating multimodal information in large pretrained transformers,

Reference 17

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Observation 09fc84e1-c4ea-4afe-80dd-e27f4bc60d1a · outbound

This paper cites Multimodal transformer for unaligned multimodal language sequences,.

PRIME: Prototype-Driven Multimodal Pretraining for Cancer Prognosis with Missing Modalities Multimodal transformer for unaligned multimodal language sequences,

Reference 18

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Observation a683141d-4f2d-4eee-ad39-44f9f22d3e71 · outbound

This paper cites Multimodal co-attention trans- former for survival prediction in gigapixel whole slide images,.

PRIME: Prototype-Driven Multimodal Pretraining for Cancer Prognosis with Missing Modalities Multimodal co-attention trans- former for survival prediction in gigapixel whole slide images,

Reference 19

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Observation d243a3b9-53f3-44ab-82ca-a2752348b322 · outbound

This paper cites Pan-cancer integrative histology-genomic analysis via multimodal deep learning,.

PRIME: Prototype-Driven Multimodal Pretraining for Cancer Prognosis with Missing Modalities Pan-cancer integrative histology-genomic analysis via multimodal deep learning,

Reference 20

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Observation 6487d3d2-4111-4a3f-a9ec-ebb3059a18d9 · outbound

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

PRIME: Prototype-Driven Multimodal Pretraining for Cancer Prognosis with Missing Modalities Pathology-and- genomics multimodal transformer for survival outcome prediction,

Reference 21

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Observation 7a22b112-57e4-42bc-b316-f926742561bc · outbound

This paper cites Mul- timodal cancer modeling in the age of foundation model embeddings,.

PRIME: Prototype-Driven Multimodal Pretraining for Cancer Prognosis with Missing Modalities Mul- timodal cancer modeling in the age of foundation model embeddings,

Reference 22

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Observation 05acb375-b81d-4893-a8b0-780c64809749 · outbound

This paper cites Towards a general- purpose foundation model for computational pathology,.

PRIME: Prototype-Driven Multimodal Pretraining for Cancer Prognosis with Missing Modalities Towards a general- purpose foundation model for computational pathology,

Reference 23

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Observation 431f814f-0974-427c-8d14-49a36c0bee20 · outbound

This paper cites A visual-language foundation model for computational pathology,.

PRIME: Prototype-Driven Multimodal Pretraining for Cancer Prognosis with Missing Modalities A visual-language foundation model for computational pathology,

Reference 24

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Observation 5980847e-8d25-4a14-8471-d0cbffd5ec7f · outbound

This paper cites A multimodal foundation model to enhance generalizability and data efficiency for pan-cancer prognosis prediction,.

PRIME: Prototype-Driven Multimodal Pretraining for Cancer Prognosis with Missing Modalities A multimodal foundation model to enhance generalizability and data efficiency for pan-cancer prognosis prediction,

Reference 25

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Observation cd9d919e-2690-475d-b334-64bec2cc9748 · outbound

This paper cites POMP: Pathology- omics multimodal pre-training framework for cancer survival predic- tion,.

PRIME: Prototype-Driven Multimodal Pretraining for Cancer Prognosis with Missing Modalities POMP: Pathology- omics multimodal pre-training framework for cancer survival predic- tion,

Reference 26

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Observation 550a32ed-2ea1-41b2-85e7-95e716b3b221 · outbound

This paper cites Robust multimodal survival prediction with conditional latent differentiation variational AutoEncoder,.

PRIME: Prototype-Driven Multimodal Pretraining for Cancer Prognosis with Missing Modalities Robust multimodal survival prediction with conditional latent differentiation variational AutoEncoder,

Reference 27

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Observation 5e6b6eb4-fa00-497b-a1f3-1126d8bbb7b1 · outbound

This paper cites M3AE: Multimodal representation learning for brain tumor segmentation with missing modalities,.

PRIME: Prototype-Driven Multimodal Pretraining for Cancer Prognosis with Missing Modalities M3AE: Multimodal representation learning for brain tumor segmentation with missing modalities,

Reference 28

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Observation b8ee6611-66a6-48fc-b8b2-c624a4bc06fb · outbound

This paper cites Multimodal masked autoencoder pre-training for 3D MRI-based brain tumor analysis with missing modalities,.

PRIME: Prototype-Driven Multimodal Pretraining for Cancer Prognosis with Missing Modalities Multimodal masked autoencoder pre-training for 3D MRI-based brain tumor analysis with missing modalities,

Reference 29

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Observation 30397821-9d3e-472f-abaa-bcb48983badf · outbound

This paper cites Closing the gap in the clinical adoption of computational pathology: a standardized, open-source framework to integrate deep-learning models into the laboratory information system,.

PRIME: Prototype-Driven Multimodal Pretraining for Cancer Prognosis with Missing Modalities Closing the gap in the clinical adoption of computational pathology: a standardized, open-source framework to integrate deep-learning models into the laboratory information system,

Reference 30

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Observation 2b1f6231-b9b1-46cf-97f5-949deabeecb6 · outbound

This paper cites Self-supervised attention-based deep learning for pan-cancer mutation prediction from histopathology,.

PRIME: Prototype-Driven Multimodal Pretraining for Cancer Prognosis with Missing Modalities Self-supervised attention-based deep learning for pan-cancer mutation prediction from histopathology,

Reference 31

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Observation 1008d26f-d8c2-404d-8299-d8194fd64c90 · outbound

This paper cites BulkRNABert: Cancer prognosis from bulk RNA-seq based language models,.

PRIME: Prototype-Driven Multimodal Pretraining for Cancer Prognosis with Missing Modalities BulkRNABert: Cancer prognosis from bulk RNA-seq based language models,

Reference 32

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Observation dd742c68-2baf-4b19-aaa4-badfc8aa4aa0 · outbound

This paper cites Publicly available clinical BERT embeddings,.

PRIME: Prototype-Driven Multimodal Pretraining for Cancer Prognosis with Missing Modalities Publicly available clinical BERT embeddings,

Reference 33

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Observation 79e1c782-37ec-42a2-9be7-099a26ddc725 · outbound

This paper cites Attention-based deep multiple instance learning,.

PRIME: Prototype-Driven Multimodal Pretraining for Cancer Prognosis with Missing Modalities Attention-based deep multiple instance learning,

Reference 34

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Observation dcf4f787-1acd-429b-9196-a5c84026946c · outbound

This paper cites Self- normalizing neural networks,.

PRIME: Prototype-Driven Multimodal Pretraining for Cancer Prognosis with Missing Modalities Self- normalizing neural networks,

Reference 35

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Observation 3b981156-d3c3-4613-81a7-00c80ae5f6dc · outbound

This paper cites Decoupled weight decay regularization,.

PRIME: Prototype-Driven Multimodal Pretraining for Cancer Prognosis with Missing Modalities Decoupled weight decay regularization,

Reference 36

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source=pdf_text observed=2026-07-13T10:21:01.818157Z digest=sha256:0e91484e392d24094043050133bebbcc2cb1377d729c3042651964c212f3433b

Pith citing papers

No inbound Pith citation observations are available.