Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining | Paper Citation Record

Observation 865d0c3a-a6e4-4ca4-ab26-0f4604fb84c1 · outbound

This paper cites Towards A Generalizable Pathology Foundation Model via Unified Knowledge Distillation.

Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining Towards A Generalizable Pathology Foundation Model via Unified Knowledge Distillation

Reference 1

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Observation dfd807c9-2156-4cb7-a110-0efcb91c986a · outbound

This paper cites Chen, Drew F.K.

Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining Chen, Drew F.K

Reference 2

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Observation d4f93978-e00f-4d77-bbeb-8acfe6417ab5 · outbound

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

Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining A Multimodal Knowledge-enhanced Whole-slide Pathology Foundation Model

Reference 3

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Observation ed8a7bfe-a2be-4a9c-b053-af8da7a4b97f · outbound

This paper cites Chen, Drew F.K.

Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining Chen, Drew F.K

Reference 4

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Observation d85681c5-1433-43d7-9f05-e09e9dcdcc11 · outbound

This paper cites Chen, Ming Y.

Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining Chen, Ming Y

Reference 5

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Observation a20caaca-2bcf-4ed9-8c85-193e13e795b7 · outbound

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

Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining Towards a general-purpose foundation model for computational pathology

Reference 6

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Observation 9e6e5ece-54bf-4151-9d63-6fa0b46a4378 · outbound

This paper cites HEALNet: Multimodal Fusion for Heterogeneous Biomedical Data.

Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining HEALNet: Multimodal Fusion for Heterogeneous Biomedical Data

Reference 7

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Observation 19497df7-17cc-4950-b90c-7b0fce3c5f46 · outbound

This paper cites Computing receptive fields of convolutional neural networks.

Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining Computing receptive fields of convolutional neural networks

Reference 8

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Observation 8abcc0ae-bfe2-48e8-a401-ef24abca040f · outbound

This paper cites Deep learning in histopathology: the path to the clinic.

Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining Deep learning in histopathology: the path to the clinic

Reference 9

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Observation 01174f86-82bd-4f49-97ee-eec1a286aab7 · outbound

This paper cites Attention-based deep multiple instance learning.

Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining Attention-based deep multiple instance learning

Reference 10

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Observation 07bde1be-8257-46f3-bb26-92d9f57b7e1f · outbound

This paper cites Clinical-grade computational pathology using weakly supervised deep learning on whole slide images.

Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining Clinical-grade computational pathology using weakly supervised deep learning on whole slide images

Reference 11

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Observation ddecb5fa-cc89-4673-b5cf-4a31ee7a401d · outbound

This paper cites Multiple instance learning with center embeddings for histopathology classification.

Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining Multiple instance learning with center embeddings for histopathology classification

Reference 12

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Observation ddf627cc-260f-492b-88bc-1bc6c28995ab · outbound

This paper cites Data-efficient and weakly supervised computational pathology on whole-slide images.

Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining Data-efficient and weakly supervised computational pathology on whole-slide images

Reference 13

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Observation a6e019ef-3d40-4498-95ad-1c4470563981 · outbound

This paper cites Transmil: Transformer based correlated multiple instance learning for whole slide image classification.

Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining Transmil: Transformer based correlated multiple instance learning for whole slide image classification

Reference 14

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Observation 97d6cbd4-05b7-4587-83e3-83beffc3cae4 · outbound

This paper cites Dt-mil: deformable transformer for multi-instance learning on histopathological image.

Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining Dt-mil: deformable transformer for multi-instance learning on histopathological image

Reference 15

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Observation 06d0f922-1c78-4aa5-9008-4f77d2c27ce6 · outbound

This paper cites Dsnet: A dual-stream framework for weakly-supervised gigapixel pathology image analysis.

Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining Dsnet: A dual-stream framework for weakly-supervised gigapixel pathology image analysis

Reference 16

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Observation 2b9eaf5f-97db-4d50-a146-0fa2a0f2d21f · outbound

This paper cites Hˆ 2-mil: exploring hierarchical representation with heterogeneous multiple instance learning for whole slide image analysis.

Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining Hˆ 2-mil: exploring hierarchical representation with heterogeneous multiple instance learning for whole slide image analysis

Reference 17

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Observation 60254f94-f3bc-4a0c-a8d3-9efd0338682f · outbound

This paper cites A graph-transformer for whole slide image classification.

Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining A graph-transformer for whole slide image classification

Reference 18

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Observation 08299365-368b-4bb0-9333-5c9bb582e78f · outbound

This paper cites Dtfd-mil: Double-tier feature distillation multiple instance learning for histopathology whole slide image classification.

Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining Dtfd-mil: Double-tier feature distillation multiple instance learning for histopathology whole slide image classification

Reference 19

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Observation 7f37aa0a-808b-4bd8-a93d-2df880923218 · outbound

This paper cites Scl-wc: Cross-slide contrastive learning for weakly-supervised whole-slide image classification.

Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining Scl-wc: Cross-slide contrastive learning for weakly-supervised whole-slide image classification

Reference 20

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Observation d47ab9c1-27b6-4817-ab6b-ed9f84405283 · outbound

This paper cites Prototypical multiple instance learning for predicting lymph node metastasis of breast cancer from whole-slide pathological images.

Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining Prototypical multiple instance learning for predicting lymph node metastasis of breast cancer from whole-slide pathological images

Reference 21

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Observation a3d84ac2-7ee6-49ed-95e6-9bfa61e2ef18 · outbound

This paper cites Visual language pretrained multiple instance zero-shot transfer for histopathology images.

Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining Visual language pretrained multiple instance zero-shot transfer for histopathology images

Reference 22

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Observation dee57977-3d11-4e4c-9ab8-7a7bd1f54776 · outbound

This paper cites Interventional bag multi-instance learning on whole-slide pathological images.

Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining Interventional bag multi-instance learning on whole-slide pathological images

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:33:58.212906Z

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Observation 884f0af7-ea06-42c2-afc4-86912326afc9 · outbound

This paper cites Dynamic graph representation with knowledge-aware attention for histopathology whole slide image analysis.

Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining Dynamic graph representation with knowledge-aware attention for histopathology whole slide image analysis

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:33:58.200009Z

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Observation 7daa4033-e958-4038-a7e1-5b9a2750e4b9 · outbound

This paper cites Mambamil: Enhancing long sequence modeling with sequence reordering in computational pathology.

Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining Mambamil: Enhancing long sequence modeling with sequence reordering in computational pathology

Reference 25

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Observation 8169318e-ac38-46dd-aa2b-cc2e68c2698a · outbound

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

Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining Harnessing multimodal data integration to advance precision oncology

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:33:58.173823Z

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Observation 365fe9c1-08c4-47ca-8b1d-567213f25d99 · outbound

This paper cites Joint analysis of expression levels and histological images identifies genes associated with tissue morphology.

Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining Joint analysis of expression levels and histological images identifies genes associated with tissue morphology

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:33:58.161688Z

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Observation d1aea085-24c2-47a4-a264-3ad740b26e2c · outbound

This paper cites Pathomic fusion: an integrated framework for fusing histopathology and genomic features for cancer diagnosis and prognosis.

Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining Pathomic fusion: an integrated framework for fusing histopathology and genomic features for cancer diagnosis and prognosis

Reference 28

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Observation 2cd4e50b-9d61-4a0b-bba0-13c862bff333 · outbound

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

Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining Pan-cancer integrative histology-genomic analysis via multimodal deep learning

Reference 29

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Observation dd7afd4f-527c-4409-b9c8-74691993b2cc · outbound

This paper cites Modeling dense multimodal interactions between biological pathways and histology for survival prediction.

Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining Modeling dense multimodal interactions between biological pathways and histology for survival prediction

Reference 30

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Observation 867dd021-2255-4dd5-9cb0-f8d9d6ef9d5a · outbound

This paper cites Hfbsurv: hierarchical multimodal fusion with factorized bilinear models for cancer survival prediction.

Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining Hfbsurv: hierarchical multimodal fusion with factorized bilinear models for cancer survival prediction

Reference 31

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Observation 098cdcb1-1ba2-4109-bafe-8404196e8006 · outbound

This paper cites Predicting cancer outcomes from histology and genomics using convolutional networks.Proceedings of the National Academy of Sciences, 115(13):E2970– E2979, 2018.

Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining Predicting cancer outcomes from histology and genomics using convolutional networks.Proceedings of the National Academy of Sciences, 115(13):E2970– E2979, 2018

Reference 32

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Observation a9c80f9e-b5fb-4a5f-82c4-0a36d58b9516 · outbound

This paper cites A deep learning model to predict rna-seq expression of tumours from whole slide images.

Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining A deep learning model to predict rna-seq expression of tumours from whole slide images

Reference 33

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Observation 43dde54c-4600-49d3-b68e-c7c50a9bc54e · outbound

This paper cites Spatially resolved gene expression prediction from histology images via bi-modal contrastive learning.

Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining Spatially resolved gene expression prediction from histology images via bi-modal contrastive learning

Reference 34

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Observation 7b60af80-f241-4584-b9ed-aad1f22fbfbd · outbound

This paper cites Multimodal optimal transport-based co-attention transformer with global structure consistency for survival prediction.

Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining Multimodal optimal transport-based co-attention transformer with global structure consistency for survival prediction

Reference 35

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Observation 17a59698-6ea4-4402-8e96-470226b2e52e · outbound

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

Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining Cross-modal translation and alignment for survival analysis

Reference 36

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Observation 3e286301-5930-4750-9ca0-a1a8006d9ac0 · outbound

This paper cites Song, Richard J.

Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining Song, Richard J

Reference 37

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Observation 2d807195-e3f4-4f93-9243-1bf4ce2eeb4c · outbound

This paper cites Genomics- guided representation learning for pathologic pan-cancer tumor microenvironment subtype prediction.

Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining Genomics- guided representation learning for pathologic pan-cancer tumor microenvironment subtype prediction

Reference 38

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Observation dbc05ebb-f4e1-4823-bd01-10a9de370ba2 · outbound

This paper cites Healnet: Multimodal fusion for heteroge- neous biomedical data.

Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining Healnet: Multimodal fusion for heteroge- neous biomedical data

Reference 39

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Observation 225d5128-7473-4ae5-9242-1e40f01511c2 · outbound

This paper cites Histgen: Histopathol- ogy report generation via local-global feature encoding and cross-modal context interaction.

Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining Histgen: Histopathol- ogy report generation via local-global feature encoding and cross-modal context interaction

Reference 40

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Observation ce050ed1-e640-4ab2-b87f-3e95830baaa2 · outbound

This paper cites Scaling vision transformers to gigapixel images via hierarchical self-supervised learning.

Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining Scaling vision transformers to gigapixel images via hierarchical self-supervised learning

Reference 41

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Observation 621fb6b1-91d6-42ac-af09-f62467bc3bb5 · outbound

This paper cites Giga-ssl: Self-supervised learning for gigapixel images.

Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining Giga-ssl: Self-supervised learning for gigapixel images

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:33:57.985683Z

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Observation 82d99733-7b6e-443f-a5bc-7d8c8544c6fa · outbound

This paper cites Slpd: slide-level prototypical distillation for wsis.

Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining Slpd: slide-level prototypical distillation for wsis

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:33:57.973804Z

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Observation b72f605e-142b-4864-9e3e-0f24ab87d252 · outbound

This paper cites Position-aware masked autoencoder for histopathology wsi representation learning.

Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining Position-aware masked autoencoder for histopathology wsi representation learning

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:33:57.961868Z

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Observation d7a25c70-3ead-4759-9350-07b73b9bf553 · outbound

This paper cites Masked pre-training of transformers for histology image analysis.

Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining Masked pre-training of transformers for histology image analysis

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:33:57.949193Z

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Observation 9ffebd60-ae88-42ea-9b6d-39a22b386ada · outbound

This paper cites Morphological prototyping for unsupervised slide representation learning in computational pathology.

Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining Morphological prototyping for unsupervised slide representation learning in computational pathology

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:33:57.936479Z

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Observation 2cf3c3ab-2e9e-4539-8bb4-f0d2881a1743 · outbound

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

Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining Pathology-and-genomics multimodal transformer for survival outcome prediction

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:33:57.923729Z

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Observation 22d54a91-1838-4a2f-aa70-2d5e68db2d64 · outbound

This paper cites Gene-induced multimodal pre-training for image-omic classification.

Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining Gene-induced multimodal pre-training for image-omic classification

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:33:57.908501Z

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Observation 907cd7d8-19fb-4341-98d8-a6db8553f8f1 · outbound

This paper cites Transcriptomics-guided slide representation learning in computational pathology.

Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining Transcriptomics-guided slide representation learning in computational pathology

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:33:57.895716Z

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Observation 6c1c3b25-e299-48de-a57d-728a65c8b3cc · outbound

This paper cites Multistain Pretraining for Slide Representation Learning in Pathology.

Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining Multistain Pretraining for Slide Representation Learning in Pathology

Reference 50

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Observation 0fb847d3-86bc-4723-9bc3-e8fb9b61b89b · outbound

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

Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining A whole-slide foundation model for digital pathology from real-world data

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-15T20:33:57.206115Z

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Observation 59e07cbd-7568-4f86-a945-64fbfd67a052 · outbound

This paper cites A foundation model for clinical-grade computational pathology and rare cancers detection.

Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining A foundation model for clinical-grade computational pathology and rare cancers detection

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-15T20:33:57.210071Z

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Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining A pathology foundation model for cancer diagnosis and prognosis prediction

Reference 53

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

Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining A visual-language foundation model for computational pathology

Reference 54

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Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining A visual–language foundation model for pathology image analysis using medical twitter

Reference 55

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This paper cites Attention is all you need.

Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining Attention is all you need

Reference 56

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Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining scbert as a large-scale pretrained deep language model for cell type annotation of single-cell rna-seq data

Reference 57

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Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining Biobert: a pre-trained biomedical language representation model for biomedical text mining

Reference 58

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

Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining Learning transferable visual models from natural language supervision

Reference 59

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Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining Self-normalizing neural networks

Reference 60

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Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining Prototypical Information Bottlenecking and Disentangling for Multimodal Cancer Survival Prediction

Reference 61

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This paper cites Dual-stream multiple instance learning network for whole slide image classification with self-supervised contrastive learning.

Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining Dual-stream multiple instance learning network for whole slide image classification with self-supervised contrastive learning

Reference 62

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This paper cites Bracs: A dataset for breast carcinoma subtyping in h&e histology images.

Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining Bracs: A dataset for breast carcinoma subtyping in h&e histology images

Reference 63

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Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining Artificial intelligence for diagnosis and gleason grading of prostate cancer: the panda challenge

Reference 64

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This paper cites Predicting breast tumor proliferation from whole-slide images: the tupac16 challenge.

Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining Predicting breast tumor proliferation from whole-slide images: the tupac16 challenge

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:33:57.770857Z

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This paper cites Machine learning-driven histotype diagnosis of ovarian carcinoma: insights from the ocean ai challenge.

Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining Machine learning-driven histotype diagnosis of ovarian carcinoma: insights from the ocean ai challenge

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:33:57.757670Z

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This paper cites Inference of captions from histopathological patches, 2022.

Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining Inference of captions from histopathological patches, 2022

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:33:57.744965Z

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This paper cites Wsicaption: Multiple instance generation of pathology reports for gigapixel whole-slide images.

Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining Wsicaption: Multiple instance generation of pathology reports for gigapixel whole-slide images

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:33:57.733135Z

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This paper cites Gene2vec: distributed representation of genes based on co-expression.

Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining Gene2vec: distributed representation of genes based on co-expression

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:33:57.721336Z

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

Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining Review the cancer genome atlas (tcga): an immeasurable source of knowledge

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:33:57.708678Z

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This paper cites Integrative analysis of tp53 mutations in lung adenocarcinoma for immunotherapies and prognosis.

Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining Integrative analysis of tp53 mutations in lung adenocarcinoma for immunotherapies and prognosis

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:33:57.696770Z

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This paper cites Lung cancer in patients who have never smoked—an emerging disease.

Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining Lung cancer in patients who have never smoked—an emerging disease

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:33:57.683140Z

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This paper cites Bias in cross-entropy-based training of deep survival networks.

Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining Bias in cross-entropy-based training of deep survival networks

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:33:57.670084Z

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Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining Theory of partial likelihood

Reference 74

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This paper cites The dataset provides insights into morphological diversity in endometrial cancer and its prognostic implications.

Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining The dataset provides insights into morphological diversity in endometrial cancer and its prognostic implications

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:33:57.642783Z

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This paper cites The associated survival data enables investigation into morphology-outcome relationships.

Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining The associated survival data enables investigation into morphology-outcome relationships

Reference 76

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This paper cites The dataset facilitates comparative survival modeling across renal cancer subtypes.

Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining The dataset facilitates comparative survival modeling across renal cancer subtypes

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:33:57.615833Z

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This paper cites This dataset enables investigation into survival-relevant morphological features in renal cancers.

Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining This dataset enables investigation into survival-relevant morphological features in renal cancers

Reference 78

Resolution: verified exact; raw_fallback, observed 2026-08-15T20:33:57.489167Z

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Observation 453f5679-a3f3-4c87-89c6-f8557dc421e6 · outbound

This paper cites We use a learning rate of 5e−4 for STAD and 1e−4 for other datasets.

Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining We use a learning rate of 5e−4 for STAD and 1e−4 for other datasets

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:33:57.603812Z

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Observation 44a5ab7c-8a86-467b-bdd7-0e690df04ebe · outbound

This paper cites We use a learning rate of 1e−5 for BRACS and 1e−4 for other datasets.

Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining We use a learning rate of 1e−5 for BRACS and 1e−4 for other datasets

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:33:57.592504Z

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This paper cites We use a learning rate of 1e−5 for TP53 and 5e−5 for EGFR.

Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining We use a learning rate of 1e−5 for TP53 and 5e−5 for EGFR

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:33:57.580454Z

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Observation d27574b1-419e-4f23-beaa-12cbb18df08c · outbound

This paper cites All of the models of the downstream tasks are trained with Adam optimizer on a single NVIDIA A100.

Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining All of the models of the downstream tasks are trained with Adam optimizer on a single NVIDIA A100

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:33:57.568969Z

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