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Paper Citation Record · LEDGER

Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping

As of 17 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 0 inbound Pith citation observations for arXiv:2509.03408.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2509.03408 v1

Coverage vector

measured 48 of 48 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T10:58:55.939101Z

measured 48 of 48 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

48 of 48 outbound references displayed

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  • unresolved7
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 3242a8d1-ecc6-46c0-bdb7-815a855a09d1 · outbound

This paper cites Representation learning of histopathology images using graph neural networks.

Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping Representation learning of histopathology images using graph neural networks

Reference 1

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Observation 9262b7ca-1bb4-4003-9ac4-12eb100769d1 · outbound

This paper cites Estrogen and progesterone receptor testing in breast cancer: Asco/cap guideline update.

Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping Estrogen and progesterone receptor testing in breast cancer: Asco/cap guideline update

Reference 2

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Observation 08de668a-c72f-4420-88e6-f925d1541629 · outbound

This paper cites Structured crowdsourcing enables convolutional segmentation of histology images.

Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping Structured crowdsourcing enables convolutional segmentation of histology images

Reference 3

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Observation 29fcdfa2-83d0-4241-95a6-8841e5b2de1b · outbound

This paper cites Big-graph: Brain imaging genetics by graph neural network.

Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping Big-graph: Brain imaging genetics by graph neural network

Reference 4

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Observation 5847bbb1-fdc4-4a38-b57a-bc2360853fa0 · outbound

This paper cites Cell-graph mining for breast tissue modeling and classification.

Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping Cell-graph mining for breast tissue modeling and classification

Reference 5

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Source-reported events for the cited work

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Observation f8820fc3-fe5c-4336-8002-7e278b5dce8f · outbound

This paper cites Obesity and breast cancer: progress to understanding the relationship.

Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping Obesity and breast cancer: progress to understanding the relationship

Reference 6

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Observation 3c00022a-c75a-4e4c-9dc5-02030404117c · outbound

This paper cites an unresolved cited work.

Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping Unresolved cited work

Reference 7

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Observation 8ca15647-1cfc-4143-a051-25f6fffaff1f · outbound

This paper cites The cbio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data.

Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping The cbio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data

Reference 8

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Observation 74bae4e1-9758-4b85-9ee0-9bf0153c0f0d · outbound

This paper cites There is more than one kind of myofibroblast: analysis of cd34 expression in benign, in situ, and invasive breast lesions.

Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping There is more than one kind of myofibroblast: analysis of cd34 expression in benign, in situ, and invasive breast lesions

Reference 9

Resolution
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Observation bf0aac7b-b253-40ee-b644-55b7c2f6c9fd · outbound

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

Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping Pan-cancer integrative histology-genomic analysis via multimodal deep learning

Reference 10

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Observation dd121f19-4346-4e0a-b0f3-443c866c85c9 · outbound

This paper cites Expression of cluster of differentiation 34 and vascular endothelial growth factor in breast cancer, and their prognostic significance.

Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping Expression of cluster of differentiation 34 and vascular endothelial growth factor in breast cancer, and their prognostic significance

Reference 11

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Source-reported events for the cited work

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Observation 0a269213-5e34-4280-89a6-706fb563ad5f · outbound

This paper cites Diagnostic significance of the immunoexpression of cd34 and smooth muscle cell actin in benign and malignant tumors of the breast.

Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping Diagnostic significance of the immunoexpression of cd34 and smooth muscle cell actin in benign and malignant tumors of the breast

Reference 12

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Source-reported events for the cited work

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Observation 1b250fa0-be85-4a82-8763-70df8c419ab0 · outbound

This paper cites Principal neighbourhood aggregation for graph nets.

Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping Principal neighbourhood aggregation for graph nets

Reference 13

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Source-reported events for the cited work

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Observation 94b7711a-a6f3-40a1-afa1-f5655885d8e7 · outbound

This paper cites Tumour heterogeneity and resistance to cancer therapies.

Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping Tumour heterogeneity and resistance to cancer therapies

Reference 14

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 80544cbb-0c34-4119-9500-f4e37de2b9a6 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 15

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Source-reported events for the cited work

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Observation cf930bb8-7da8-46bb-826e-2b9bfa534c38 · outbound

This paper cites PanNuke Dataset Extension, Insights and Baselines.

Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping PanNuke Dataset Extension, Insights and Baselines

Reference 16

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Observation bf09d235-e8d4-4674-a405-969c835d5558 · outbound

This paper cites Estrogen receptor status, determined by immunohistochemistry, as a predictor of the recurrence of stage i endometrial carcinoma.

Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping Estrogen receptor status, determined by immunohistochemistry, as a predictor of the recurrence of stage i endometrial carcinoma

Reference 17

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Observation c7e7d3d4-cdf2-4bb8-add1-49ca3694c397 · outbound

This paper cites Hover-net: Simultaneous segmentation and classification of nuclei in multi-tissue histology images.

Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping Hover-net: Simultaneous segmentation and classification of nuclei in multi-tissue histology images

Reference 18

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Observation fa472135-32f9-46cc-aac4-0d0f0f0b0791 · outbound

This paper cites Multimodal fusion with deep neural networks for leveraging ct imaging and electronic health record: a case-study in pulmonary embolism detection.

Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping Multimodal fusion with deep neural networks for leveraging ct imaging and electronic health record: a case-study in pulmonary embolism detection

Reference 19

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Observation fa460d83-0c4f-4ec0-b525-f2a69f88d7fa · outbound

This paper cites Self-normalizing neural networks.

Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping Self-normalizing neural networks

Reference 20

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Observation 1482e2e5-7c9b-4dad-a460-486158c6a106 · outbound

This paper cites Topological feature extraction and visualization of whole slide images using graph neural networks.

Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping Topological feature extraction and visualization of whole slide images using graph neural networks

Reference 21

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Observation 489c1a33-0f6d-4c45-a945-68071c3151af · outbound

This paper cites Mmgk: Multimodality multiview graph representations and knowledge embedding for mild cognitive impairment diagnosis.

Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping Mmgk: Multimodality multiview graph representations and knowledge embedding for mild cognitive impairment diagnosis

Reference 22

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Source-reported events for the cited work

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Observation 32066283-c5a0-4d4a-a9df-6dbbd3190a60 · outbound

This paper cites A hybrid deep learning model for predicting molecular subtypes of human breast cancer using multimodal data.

Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping A hybrid deep learning model for predicting molecular subtypes of human breast cancer using multimodal data

Reference 23

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Observation 13d115ab-0ef4-4e14-8f2a-ccaf790231c9 · outbound

This paper cites RoBERTa: A Robustly Optimized BERT Pretraining Approach.

Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 24

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Observation 7dfa64eb-57c3-4cd3-abe4-d6c92cb58495 · outbound

This paper cites Feature driven local cell graph (fedeg): predicting overall survival in early stage lung cancer.

Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping Feature driven local cell graph (fedeg): predicting overall survival in early stage lung cancer

Reference 25

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Observation addba19f-22af-4f28-ac89-39528a767803 · outbound

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

Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping Data-efficient and weakly supervised computational pathology on whole-slide images

Reference 26

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Observation 92527e86-3c7c-43e3-9e63-363496deff3c · outbound

This paper cites Capturing cellular topology in multi-gigapixel pathology images.

Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping Capturing cellular topology in multi-gigapixel pathology images

Reference 27

Resolution
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Observation 67a0dae2-ac91-4cfd-852c-bc2067e19d72 · outbound

This paper cites Slidegraph+: Whole slide image level graphs to predict her2 status in breast cancer.

Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping Slidegraph+: Whole slide image level graphs to predict her2 status in breast cancer

Reference 28

Resolution
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Source-reported events for the cited work

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Observation ac4d2e59-6bdc-4e3e-96fc-6808d71aa348 · outbound

This paper cites Expression and methylation patterns partition luminal-a breast tumors into distinct prognostic subgroups.

Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping Expression and methylation patterns partition luminal-a breast tumors into distinct prognostic subgroups

Reference 29

Resolution
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Source-reported events for the cited work

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Observation 1f2d6204-cf14-4c35-b947-bd4d014ed40e · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision.

Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping DINOv2: Learning Robust Visual Features without Supervision

Reference 30

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Source-reported events for the cited work

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source=arxiv_source observed=2026-08-05T10:58:53.672846Z digest=sha256:dbd22aa227ce6114c00deff9999f5274d52436ddac49b5cd78b9431af0995d5d

Observation 6e6a3671-a94c-4fab-9105-079a1c96d859 · outbound

This paper cites Supervised risk predictor of breast cancer based on intrinsic subtypes.

Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping Supervised risk predictor of breast cancer based on intrinsic subtypes

Reference 31

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-05T10:58:53.843653Z digest=sha256:2ab4957d82c4814df394905e64213a989e7f731b7f27c9b06b217e11fe088071

Observation 5c143cb4-4a61-4396-aeec-b8f3ab3fe18d · outbound

This paper cites Hact-net: A hierarchical cell-to-tissue graph neural network for histopathological image classification.

Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping Hact-net: A hierarchical cell-to-tissue graph neural network for histopathological image classification

Reference 32

Resolution
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Source-reported events for the cited work

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source=arxiv_source observed=2026-08-05T10:58:53.957864Z digest=sha256:39ee307af33909d388e86c35091c7ee01dc0fbefdea18059841e2c3f9031585d

Observation 571f4714-a354-4610-84ff-e4c17e71b146 · outbound

This paper cites High timm17a expression is associated with adverse pathological and clinical outcomes in human breast cancer.

Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping High timm17a expression is associated with adverse pathological and clinical outcomes in human breast cancer

Reference 33

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-05T10:58:54.131243Z digest=sha256:c091d01529c3f4c29c638e252e8597a3bfe52c8874668f5fb543131b2799befd

Observation de8bed3f-ae9c-4a33-88bd-b637895e8e36 · outbound

This paper cites Creb-regulated transcription co-activator family stimulates promoter ii-driven aromatase expression in preadipocytes.

Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping Creb-regulated transcription co-activator family stimulates promoter ii-driven aromatase expression in preadipocytes

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T10:58:59.577596Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-05T10:58:54.231200Z digest=sha256:a3bbd6ea7862044e96e4e145922dce8b36eaa6a1983c2ff2cff9a89b17c239aa

Observation 4ab90da5-2892-4920-8964-7cb49b9f15c3 · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 35

Resolution
unresolved
no resolver link, observed 2026-08-05T10:58:54.376921Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T10:58:54.376921Z digest=sha256:eee98d0b1aef60861bd66ee25a2ee72d9e955d6c471b3c4be1afaf29ed86f395

Observation 3794f666-941b-4963-aae1-e3510a07e44f · outbound

This paper cites Multimodal deep learning for biomedical data fusion: a review.

Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping Multimodal deep learning for biomedical data fusion: a review

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T10:58:59.241124Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-05T10:58:54.525919Z digest=sha256:9fa437e038bc3edc632efe808e797d4ab3da432f751e14e7abde66cc372f0f6c

Observation e4cbb41c-27d0-4d7c-9db9-0dc89a556c18 · outbound

This paper cites Multimodal data fusion for cancer biomarker discovery with deep learning.

Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping Multimodal data fusion for cancer biomarker discovery with deep learning

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T10:58:58.944561Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-05T10:58:54.623908Z digest=sha256:30c2eb49e6c9c922fc80a4607d34b11af29cd80e8186f8e4e2ad5bb0d6325d44

Observation e828eede-0d35-4e84-a7f1-052cfe698b00 · outbound

This paper cites Axiomatic attribution for deep networks.

Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping Axiomatic attribution for deep networks

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T10:58:58.585336Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-05T10:58:54.785331Z digest=sha256:f06d8274e6e60e4e4652bc87a599fd76e754dce5f37f2f226285b14bacd514a5

Observation e2cf51b5-31ae-40d6-9e88-7a32d87fc0e3 · outbound

This paper cites Rethinking the inception architecture for computer vision.

Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping Rethinking the inception architecture for computer vision

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T10:58:58.303691Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-05T10:58:54.894186Z digest=sha256:c72b0fcb07a0a985babd164199879aa85aff3f2c0c679db0401740ce4f97b8ad

Observation d8055292-1f85-44d4-b865-ec78bbb84f32 · outbound

This paper cites Fusionbench: A comprehensive benchmark of deep model fusion.

Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping Fusionbench: A comprehensive benchmark of deep model fusion

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-05T10:58:54.998127Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T10:58:54.998127Z digest=sha256:510bc93d47587988b805e3a3c8cf1dc54fca167dbff7ae5084923b95b750292e

Observation 7efae94d-ed5d-46c4-a725-76cc4d932f1a · outbound

This paper cites Her2 immunohistochemical scores provide prognostic information for patients with her2-type invasive breast cancer.

Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping Her2 immunohistochemical scores provide prognostic information for patients with her2-type invasive breast cancer

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T10:58:57.990650Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-05T10:58:55.117387Z digest=sha256:d75a0aef8c709ccd477964f8a80f6e0f139dba0e8c706ea16338a6dbcab36f89

Observation c847c00f-b844-43f7-8d70-e8ff148f518b · outbound

This paper cites Lung cancer subtype diagnosis by fusing image-genomics data and hybrid deep networks.

Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping Lung cancer subtype diagnosis by fusing image-genomics data and hybrid deep networks

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T10:58:57.663832Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-05T10:58:55.210397Z digest=sha256:20025e094f19231379fda3adcae1538dccf4c57bdd38fb87567c474bf9d8695c

Observation 9d834887-f37b-4fe7-ab5b-f0aba441706a · outbound

This paper cites Redefining breast cancer subtypes to guide treatment prioritization and maximize response: Predictive biomarkers across 10 cancer therapies.

Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping Redefining breast cancer subtypes to guide treatment prioritization and maximize response: Predictive biomarkers across 10 cancer therapies

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T10:58:57.417434Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-05T10:58:55.315287Z digest=sha256:b0483d9f92904a31011655d6ec4f1178c2c948ccb0e8d775d5dae0079ff43733

Observation ac67c8ad-8eae-4faa-aebf-e5e3a3180443 · outbound

This paper cites Heterogeneous model reuse via optimizing multiparty multiclass margin.

Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping Heterogeneous model reuse via optimizing multiparty multiclass margin

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T10:58:57.174436Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-05T10:58:55.453037Z digest=sha256:917ed7338fc75228ce6226932fce7c5b90a1d9481e8c68aa73f5f2b6832e8aa6

Observation d3ee3bc6-3cd4-4eff-a830-d1aa45af49da · outbound

This paper cites Quantitative proteomics study of breast cancer cell lines isolated from a single patient: discovery of timm17a as a marker for breast cancer.

Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping Quantitative proteomics study of breast cancer cell lines isolated from a single patient: discovery of timm17a as a marker for breast cancer

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T10:58:56.966539Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-05T10:58:55.562232Z digest=sha256:03899e2bc6a3bd71e63e6fde817a0aad0ad6b254f5e6eaea9c3749c11c61bb9e

Observation ec5bdef4-aac3-41c6-9c52-b9d773ce6944 · outbound

This paper cites The impact of timm17a on aggressiveness of human breast cancer cells.

Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping The impact of timm17a on aggressiveness of human breast cancer cells

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T10:58:56.793404Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-05T10:58:55.699106Z digest=sha256:b0c970aa2716cd7b544d467f9832f2d76678cf7127370ea10cd7b18fbb5de868

Observation 382805ef-0d1e-47a3-a933-61ef056789b8 · outbound

This paper cites Triple-negative breast cancer molecular subtyping and treatment progress.

Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping Triple-negative breast cancer molecular subtyping and treatment progress

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T10:58:56.543468Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-05T10:58:55.815549Z digest=sha256:42ed8b10a6cac1c70896011a34cbd4bf61becd00acc3de0f476ed629415d77ee

Observation 3449f806-6658-4256-9dfc-fa2756a17451 · outbound

This paper cites Analysis of tumor genomic pathway alterations using broad-panel next-generation sequencing in surgically resected lung adenocarcinoma.

Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping Analysis of tumor genomic pathway alterations using broad-panel next-generation sequencing in surgically resected lung adenocarcinoma

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T10:58:56.340265Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-05T10:58:55.939101Z digest=sha256:d08afe74a8b0620a75304f2cc9451ccb07192eb89e421e5d325bf3fad4b6edf9

Pith citing papers

No inbound Pith citation observations are available.