Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-12T20:24:57.602239Z
Paper Citation Record · LEDGER
NACNet: A Histology Context-aware Transformer Graph Convolution Network for Predicting Treatment Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer
As of 20 August 2026, this Paper Citation Record lists 73 of 73 outbound references and 0 inbound Pith citation observations for arXiv:2411.09766.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
73 of 73 outbound references displayed
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External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 9c778599-9c1a-4af3-9b7d-1eb5315a1824 · outbound
This paper cites Triple-negative breast cancer: epidemiological considerations and recommendations.
NACNet: A Histology Context-aware Transformer Graph Convolution Network for Predicting Treatment Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer Triple-negative breast cancer: epidemiological considerations and recommendations
Reference 1
- Resolution
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Observation 2d507fb6-8f0c-4b85-818c-a3e3b69706bc · outbound
This paper cites Tissue-resident macrophages are major tumor- associated macrophage resources, contributing to early tnbc development, recurrence, and metastases.
NACNet: A Histology Context-aware Transformer Graph Convolution Network for Predicting Treatment Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer Tissue-resident macrophages are major tumor- associated macrophage resources, contributing to early tnbc development, recurrence, and metastases
Reference 2
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Observation e885d632-718c-42ee-80b3-51a22f4317c3 · outbound
This paper cites Molecular features and clinical implications of the heterogeneity in chinese patients with her2-low breast cancer.
NACNet: A Histology Context-aware Transformer Graph Convolution Network for Predicting Treatment Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer Molecular features and clinical implications of the heterogeneity in chinese patients with her2-low breast cancer
Reference 3
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 12612870-85ec-4768-ae3c-939f9e1d753a · outbound
This paper cites Triple- negative breast cancer: current perspective on the evolving therapeutic landscape.
NACNet: A Histology Context-aware Transformer Graph Convolution Network for Predicting Treatment Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer Triple- negative breast cancer: current perspective on the evolving therapeutic landscape
Reference 4
- Resolution
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Observation f96691c2-b950-4806-8844-5e6672d6f8c4 · outbound
This paper cites Immunotherapy in triple-negative breast cancer: A literature review and new advances.
NACNet: A Histology Context-aware Transformer Graph Convolution Network for Predicting Treatment Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer Immunotherapy in triple-negative breast cancer: A literature review and new advances
Reference 5
- Resolution
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Source-reported events for the cited work
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Observation 629461d7-d205-49da-a110-57f22cc2b611 · outbound
This paper cites Triple- negative breast cancer: challenges and opportunities of a heterogeneous disease.
NACNet: A Histology Context-aware Transformer Graph Convolution Network for Predicting Treatment Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer Triple- negative breast cancer: challenges and opportunities of a heterogeneous disease
Reference 6
- Resolution
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Source-reported events for the cited work
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Observation ae7a380a-614e-497d-b53a-65ffbc494b0e · outbound
This paper cites Pathological complete response and long-term clinical benefit in breast cancer: the ctneobc pooled analysis.
NACNet: A Histology Context-aware Transformer Graph Convolution Network for Predicting Treatment Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer Pathological complete response and long-term clinical benefit in breast cancer: the ctneobc pooled analysis
Reference 7
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 87c03c27-0c30-44ce-b64c-1c749e24a29e · outbound
This paper cites Relevance of pathological complete response after neoadjuvant therapy for breast cancer.
NACNet: A Histology Context-aware Transformer Graph Convolution Network for Predicting Treatment Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer Relevance of pathological complete response after neoadjuvant therapy for breast cancer
Reference 8
- Resolution
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Source-reported events for the cited work
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Observation c206941d-e21e-4e15-b959-34a182920e94 · outbound
This paper cites Conservative surgery after neoadjuvant chemotherapy in patients with operable breast cancer.
NACNet: A Histology Context-aware Transformer Graph Convolution Network for Predicting Treatment Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer Conservative surgery after neoadjuvant chemotherapy in patients with operable breast cancer
Reference 9
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 5c76d1b8-6c6e-448c-85ad-a1df30f94c11 · outbound
This paper cites Pathological tumor response to neoadjuvant chemotherapy using anthracycline and taxanes in patients with triple-negative breast cancer.
NACNet: A Histology Context-aware Transformer Graph Convolution Network for Predicting Treatment Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer Pathological tumor response to neoadjuvant chemotherapy using anthracycline and taxanes in patients with triple-negative breast cancer
Reference 10
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation e18b6573-18d8-40dd-b934-a4408a002f6d · outbound
This paper cites A pathology foundation model for cancer diagnosis and prognosis prediction.
NACNet: A Histology Context-aware Transformer Graph Convolution Network for Predicting Treatment Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer A pathology foundation model for cancer diagnosis and prognosis prediction
Reference 11
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation b48918de-46fe-43c9-8cd6-413b810ba238 · outbound
This paper cites Histopathologic image–based deep learning classifier for predicting platinum-based treatment responses in high-grade serous ovarian cancer.
NACNet: A Histology Context-aware Transformer Graph Convolution Network for Predicting Treatment Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer Histopathologic image–based deep learning classifier for predicting platinum-based treatment responses in high-grade serous ovarian cancer
Reference 12
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 946ad0bb-0e02-4d46-a7e4-fe29f01fd331 · outbound
This paper cites Deep learning for the prediction of early on-treatment response in metastatic colorectal cancer from serial medical imaging.Nature communications, 12(1):6654, 2021.
NACNet: A Histology Context-aware Transformer Graph Convolution Network for Predicting Treatment Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer Deep learning for the prediction of early on-treatment response in metastatic colorectal cancer from serial medical imaging.Nature communications, 12(1):6654, 2021
Reference 13
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 28ce8b15-c3da-4f95-a7f5-c9211c037a77 · outbound
This paper cites A deep-learning framework to predict cancer treatment response from histopathology images through imputed transcriptomics.
NACNet: A Histology Context-aware Transformer Graph Convolution Network for Predicting Treatment Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer A deep-learning framework to predict cancer treatment response from histopathology images through imputed transcriptomics
Reference 14
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 063619c2-73de-4318-a72c-25cfab916106 · outbound
This paper cites Prediction of early clinical response to neoadjuvant chemotherapy in triple-negative breast cancer: Incorporating radiomics through breast mri.
NACNet: A Histology Context-aware Transformer Graph Convolution Network for Predicting Treatment Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer Prediction of early clinical response to neoadjuvant chemotherapy in triple-negative breast cancer: Incorporating radiomics through breast mri
Reference 15
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 106540ef-5137-4568-bc7f-b7fd8847d31a · outbound
This paper cites Machine learning for genetics-based classification and treatment response prediction in cancer of unknown primary.
NACNet: A Histology Context-aware Transformer Graph Convolution Network for Predicting Treatment Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer Machine learning for genetics-based classification and treatment response prediction in cancer of unknown primary
Reference 16
- Resolution
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Source-reported events for the cited work
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Observation df3a90db-defd-44c8-bcb7-41bcc5ff95e5 · outbound
This paper cites Whole slide imaging (wsi) in pathology: current perspectives and future directions.
NACNet: A Histology Context-aware Transformer Graph Convolution Network for Predicting Treatment Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer Whole slide imaging (wsi) in pathology: current perspectives and future directions
Reference 17
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 9eb0d18f-de9f-4bf0-a5a9-11ae9b683dcc · outbound
This paper cites Deep learning for whole slide image analysis: an overview.
NACNet: A Histology Context-aware Transformer Graph Convolution Network for Predicting Treatment Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer Deep learning for whole slide image analysis: an overview
Reference 18
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation fe5ed6bf-1dc0-4a48-948c-fdbd047469a7 · outbound
This paper cites Orbit image analysis: an open-source whole slide image analysis tool.
NACNet: A Histology Context-aware Transformer Graph Convolution Network for Predicting Treatment Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer Orbit image analysis: an open-source whole slide image analysis tool
Reference 19
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation de66c63f-791d-4ae2-9fb4-fdcacc7fddab · outbound
This paper cites Prediction of pathological complete response to neoadjuvant chemo- therapy in breast cancer using deep learning with integrative imaging, molecular and demographic data.
NACNet: A Histology Context-aware Transformer Graph Convolution Network for Predicting Treatment Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer Prediction of pathological complete response to neoadjuvant chemo- therapy in breast cancer using deep learning with integrative imaging, molecular and demographic data
Reference 20
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation a050bb6b-7ac2-461f-b59b-92b7972ab437 · outbound
This paper cites an unresolved cited work.
NACNet: A Histology Context-aware Transformer Graph Convolution Network for Predicting Treatment Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer Unresolved cited work
Reference 21
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation a1686cf5-96ad-4405-888b-022c7ebf98ad · outbound
This paper cites Prediction of pathologic response to neoadjuvant chemotherapy in patients with breast cancer using diffusion-weighted imaging and mrs.
NACNet: A Histology Context-aware Transformer Graph Convolution Network for Predicting Treatment Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer Prediction of pathologic response to neoadjuvant chemotherapy in patients with breast cancer using diffusion-weighted imaging and mrs
Reference 22
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 6e265eb7-8aff-4754-9528-52a6b44de867 · outbound
This paper cites Prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer using a deep learning (dl) method.
NACNet: A Histology Context-aware Transformer Graph Convolution Network for Predicting Treatment Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer Prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer using a deep learning (dl) method
Reference 23
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 2ea85b72-bc13-495e-bf62-a28df1640fbc · outbound
This paper cites A machine learning model to predict the triple negative breast cancer immune subtype.
NACNet: A Histology Context-aware Transformer Graph Convolution Network for Predicting Treatment Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer A machine learning model to predict the triple negative breast cancer immune subtype
Reference 24
- Resolution
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Source-reported events for the cited work
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Observation c5c713cd-bca5-482a-a600-3acc5ae7ab79 · outbound
This paper cites A graph-transformer for whole slide image classification.
NACNet: A Histology Context-aware Transformer Graph Convolution Network for Predicting Treatment Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer A graph-transformer for whole slide image classification
Reference 25
- Resolution
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Source-reported events for the cited work
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Observation 466fdd75-2580-4423-9f4b-6514551b0328 · outbound
This paper cites Multiple instance learning with general- ized support vector machines.
NACNet: A Histology Context-aware Transformer Graph Convolution Network for Predicting Treatment Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer Multiple instance learning with general- ized support vector machines
Reference 26
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 26ff4458-4eb8-4e55-b969-bf2dd8c0eee8 · outbound
This paper cites Multimodal co-attention transformer for survival prediction in gigapixel whole slide images.
NACNet: A Histology Context-aware Transformer Graph Convolution Network for Predicting Treatment Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer Multimodal co-attention transformer for survival prediction in gigapixel whole slide images
Reference 27
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 74c5842c-1259-4f02-b653-b62ee421efe4 · outbound
This paper cites Automatic detection of invasive ductal carcinoma in whole slide images with convolutional neural networks.
NACNet: A Histology Context-aware Transformer Graph Convolution Network for Predicting Treatment Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer Automatic detection of invasive ductal carcinoma in whole slide images with convolutional neural networks
Reference 28
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 01a7715b-34b8-41aa-a6a3-8ccea6961efb · outbound
This paper cites Gradient-based learning applied to document recognition.
NACNet: A Histology Context-aware Transformer Graph Convolution Network for Predicting Treatment Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer Gradient-based learning applied to document recognition
Reference 29
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation aa62c914-131d-4740-8d77-052637663630 · outbound
This paper cites Weakly supervised discriminative localization and classification: a joint learning process.
NACNet: A Histology Context-aware Transformer Graph Convolution Network for Predicting Treatment Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer Weakly supervised discriminative localization and classification: a joint learning process
Reference 30
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 267c887d-d7de-4cb4-9b89-66bb384258f2 · outbound
This paper cites Treatment landscape of triple- negative breast cancer—expanded options, evolving needs.
NACNet: A Histology Context-aware Transformer Graph Convolution Network for Predicting Treatment Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer Treatment landscape of triple- negative breast cancer—expanded options, evolving needs
Reference 31
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 14903d4d-8813-4fe8-b179-c36517cbd9a9 · outbound
This paper cites A structured tumor-immune microenvironment in triple negative breast cancer revealed by multiplexed ion beam imaging.
NACNet: A Histology Context-aware Transformer Graph Convolution Network for Predicting Treatment Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer A structured tumor-immune microenvironment in triple negative breast cancer revealed by multiplexed ion beam imaging
Reference 32
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 41886cbe-5f27-4211-8ce8-3cc368673660 · outbound
This paper cites Mucosal b cell differentiation and regulation.
NACNet: A Histology Context-aware Transformer Graph Convolution Network for Predicting Treatment Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer Mucosal b cell differentiation and regulation
Reference 33
- Resolution
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Source-reported events for the cited work
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Observation aa69be07-29f0-4fb9-881c-9952bbad520f · outbound
This paper cites An overview of lasers in dentistry.
NACNet: A Histology Context-aware Transformer Graph Convolution Network for Predicting Treatment Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer An overview of lasers in dentistry
Reference 34
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 0957b525-4a51-411a-a7c3-73b938267609 · outbound
This paper cites Graph cnn for survival analysis on whole slide pathological images.
NACNet: A Histology Context-aware Transformer Graph Convolution Network for Predicting Treatment Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer Graph cnn for survival analysis on whole slide pathological images
Reference 35
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation e5b103dc-b597-4758-af80-0c323641afcf · outbound
This paper cites Topological feature extraction and visualization of whole slide images using graph neural networks.
NACNet: A Histology Context-aware Transformer Graph Convolution Network for Predicting Treatment Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer Topological feature extraction and visualization of whole slide images using graph neural networks
Reference 36
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 6d191e89-128b-4de3-9ac8-0816dc1abb1e · outbound
This paper cites Whole slide images are 2d point clouds: Context-aware survival prediction using patch-based graph convolutional networks.
NACNet: A Histology Context-aware Transformer Graph Convolution Network for Predicting Treatment Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer Whole slide images are 2d point clouds: Context-aware survival prediction using patch-based graph convolutional networks
Reference 37
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation cc283c74-a374-4c34-93a3-7a051698a374 · outbound
This paper cites Representation Learning on Graphs: Methods and Applications.
NACNet: A Histology Context-aware Transformer Graph Convolution Network for Predicting Treatment Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer Representation Learning on Graphs: Methods and Applications
Reference 38
- Resolution
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Source-reported events for the cited work
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Observation c64bffdd-7062-44b7-ac90-3d4fcc9b2d6c · outbound
This paper cites Slide- graph+: Whole slide image level graphs to predict her2 status in breast cancer.
NACNet: A Histology Context-aware Transformer Graph Convolution Network for Predicting Treatment Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer Slide- graph+: Whole slide image level graphs to predict her2 status in breast cancer
Reference 39
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 5cf326f1-1430-418f-8c85-047840d0cb45 · outbound
This paper cites How Powerful are Graph Neural Networks?.
NACNet: A Histology Context-aware Transformer Graph Convolution Network for Predicting Treatment Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer How Powerful are Graph Neural Networks?
Reference 40
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 59d9977c-f1b8-4cf2-b8d2-7b8d1d81d1a1 · outbound
This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.
NACNet: A Histology Context-aware Transformer Graph Convolution Network for Predicting Treatment Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer Very Deep Convolutional Networks for Large-Scale Image Recognition
Reference 41
- Resolution
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Source-reported events for the cited work
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Observation 3ff9682f-315a-4d55-8f62-3ef29ee4eb5f · outbound
This paper cites Guided soft attention network for classification of breast cancer histopathology images.
NACNet: A Histology Context-aware Transformer Graph Convolution Network for Predicting Treatment Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer Guided soft attention network for classification of breast cancer histopathology images
Reference 42
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 48834415-d647-4343-a549-cac5b8eb5c79 · outbound
This paper cites Hallmarks of cancer: the next generation.
NACNet: A Histology Context-aware Transformer Graph Convolution Network for Predicting Treatment Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer Hallmarks of cancer: the next generation
Reference 43
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T20:24:58.066530Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation fb201895-7fc5-40e4-bf8a-338c095f016b · outbound
This paper cites Tumor-associated lymphocytes as an independent predictor of response to neoadjuvant chemotherapy in breast cancer.
NACNet: A Histology Context-aware Transformer Graph Convolution Network for Predicting Treatment Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer Tumor-associated lymphocytes as an independent predictor of response to neoadjuvant chemotherapy in breast cancer
Reference 44
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation f65694b3-27af-4ffc-a37a-d8d89e7eb554 · outbound
This paper cites Role of angiogenesis in tumor growth and metastasis.
NACNet: A Histology Context-aware Transformer Graph Convolution Network for Predicting Treatment Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer Role of angiogenesis in tumor growth and metastasis
Reference 45
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation d025d82f-ea3d-4d25-8f26-ee5bad1f71ff · outbound
This paper cites Neural graph modelling of whole slide images for survival ranking.
NACNet: A Histology Context-aware Transformer Graph Convolution Network for Predicting Treatment Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer Neural graph modelling of whole slide images for survival ranking
Reference 46
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation b3126733-a88e-4ae5-a1e7-72e7f8a5d556 · outbound
This paper cites Social network analysis: a powerful strategy, also for the information sciences.
NACNet: A Histology Context-aware Transformer Graph Convolution Network for Predicting Treatment Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer Social network analysis: a powerful strategy, also for the information sciences
Reference 47
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 397aebe0-ef2b-43e8-ba0a-38f85f8d1d85 · outbound
This paper cites The google pagerank algorithm and how it works.
NACNet: A Histology Context-aware Transformer Graph Convolution Network for Predicting Treatment Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer The google pagerank algorithm and how it works
Reference 48
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation e352d5d3-ede1-4c18-b7ea-736bd4aef38b · outbound
This paper cites Autoencoder for words.
NACNet: A Histology Context-aware Transformer Graph Convolution Network for Predicting Treatment Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer Autoencoder for words
Reference 49
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 8a46f5cf-b1f5-4fd8-8b54-d80febc0e3e7 · outbound
This paper cites Universal graph transformer self-attention networks.
NACNet: A Histology Context-aware Transformer Graph Convolution Network for Predicting Treatment Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer Universal graph transformer self-attention networks
Reference 50
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation c96d0827-6ff4-4420-8df4-84501671ea88 · outbound
This paper cites Semi-Supervised Classification with Graph Convolutional Networks.
NACNet: A Histology Context-aware Transformer Graph Convolution Network for Predicting Treatment Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer Semi-Supervised Classification with Graph Convolutional Networks
Reference 51
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Observation 9df7b171-de25-4cb5-a4fc-261ef1fa0b0f · outbound
This paper cites Deep graph kernels.
NACNet: A Histology Context-aware Transformer Graph Convolution Network for Predicting Treatment Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer Deep graph kernels
Reference 52
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation ae333d57-7a00-4856-9d3e-9042f1e57f0e · outbound
This paper cites Deep residual learning for image recognition.
NACNet: A Histology Context-aware Transformer Graph Convolution Network for Predicting Treatment Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer Deep residual learning for image recognition
Reference 53
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Source-reported events for the cited work
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Observation 22055434-2875-4a54-b869-5608f9cf7e76 · outbound
This paper cites Layer Normalization.
NACNet: A Histology Context-aware Transformer Graph Convolution Network for Predicting Treatment Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer Layer Normalization
Reference 54
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3426aedc-edbb-49e4-b721-e7430b068b43 · outbound
This paper cites Minimum redundancy feature selection from microarray gene expression data.
NACNet: A Histology Context-aware Transformer Graph Convolution Network for Predicting Treatment Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer Minimum redundancy feature selection from microarray gene expression data
Reference 55
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation e30b983a-bd8f-4274-8e31-7686329cea16 · outbound
This paper cites Adam: A Method for Stochastic Optimization.
NACNet: A Histology Context-aware Transformer Graph Convolution Network for Predicting Treatment Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer Adam: A Method for Stochastic Optimization
Reference 56
- Resolution
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Observation 43f2477e-20a3-4125-8eeb-f25d9a3ac6f7 · outbound
This paper cites Maximum Entropy Weighted Independent Set Pooling for Graph Neural Networks.
NACNet: A Histology Context-aware Transformer Graph Convolution Network for Predicting Treatment Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer Maximum Entropy Weighted Independent Set Pooling for Graph Neural Networks
Reference 57
- Resolution
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Source-reported events for the cited work
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Observation 3e13c798-4335-42be-a8c0-5bd632113697 · outbound
This paper cites An end-to-end deep learning architecture for graph classification.
NACNet: A Histology Context-aware Transformer Graph Convolution Network for Predicting Treatment Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer An end-to-end deep learning architecture for graph classification
Reference 58
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 1fad1d0e-fbc6-4aca-9bf0-fd08e91e4149 · outbound
This paper cites Self-attention graph pooling.
NACNet: A Histology Context-aware Transformer Graph Convolution Network for Predicting Treatment Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer Self-attention graph pooling
Reference 59
- Resolution
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Source-reported events for the cited work
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Observation 0ec379b0-3071-46b2-a7e4-c4433c99b011 · outbound
This paper cites Hierarchical Graph Pooling with Structure Learning.
NACNet: A Histology Context-aware Transformer Graph Convolution Network for Predicting Treatment Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer Hierarchical Graph Pooling with Structure Learning
Reference 60
- Resolution
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Source-reported events for the cited work
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Observation aad2b642-8ad5-440c-a4f1-22a3142c171a · outbound
This paper cites Imagenet classification with deep convolutional neural networks.
NACNet: A Histology Context-aware Transformer Graph Convolution Network for Predicting Treatment Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer Imagenet classification with deep convolutional neural networks
Reference 61
- Resolution
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Source-reported events for the cited work
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Observation 02e24581-4da9-4d10-abc4-ca7c4ddec8cc · outbound
This paper cites Applied logistic regression, 2.
NACNet: A Histology Context-aware Transformer Graph Convolution Network for Predicting Treatment Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer Applied logistic regression, 2
Reference 62
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 64a828e8-c07b-4f61-b98b-1e1321d20683 · outbound
This paper cites Nearest neighbor pattern classification.
NACNet: A Histology Context-aware Transformer Graph Convolution Network for Predicting Treatment Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer Nearest neighbor pattern classification
Reference 63
- Resolution
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Source-reported events for the cited work
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Observation 44832131-0e94-40f1-a0d3-7f4f82bba712 · outbound
This paper cites Support-vector networks.
NACNet: A Histology Context-aware Transformer Graph Convolution Network for Predicting Treatment Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer Support-vector networks
Reference 64
- Resolution
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Source-reported events for the cited work
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Observation 1082e7fe-9427-434f-9105-be2cef7a61aa · outbound
This paper cites The optimality of naive bayes.
NACNet: A Histology Context-aware Transformer Graph Convolution Network for Predicting Treatment Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer The optimality of naive bayes
Reference 65
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 0c32df84-bc51-473f-83ff-526cd5465af6 · outbound
This paper cites Ross Quinlan.
Reference 66
- Resolution
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Source-reported events for the cited work
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Observation b1fc2453-ff4f-44d1-85f0-1c1fcf3d3b68 · outbound
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NACNet: A Histology Context-aware Transformer Graph Convolution Network for Predicting Treatment Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer Unresolved cited work
Reference 67
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation b647c9ca-631d-48bf-a749-47213d39f3aa · outbound
This paper cites Extremely randomized trees.
NACNet: A Histology Context-aware Transformer Graph Convolution Network for Predicting Treatment Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer Extremely randomized trees
Reference 68
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 141cbe47-f85b-470c-b5a6-dfe16d8320ba · outbound
This paper cites Linear discriminant analysis-a brief tutorial.
NACNet: A Histology Context-aware Transformer Graph Convolution Network for Predicting Treatment Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer Linear discriminant analysis-a brief tutorial
Reference 69
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation f69ea0a2-ac22-4d0a-9143-af8a3ec446bb · outbound
This paper cites Scikit-learn: Machine learning in python.
NACNet: A Histology Context-aware Transformer Graph Convolution Network for Predicting Treatment Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer Scikit-learn: Machine learning in python
Reference 70
- Resolution
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Source-reported events for the cited work
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Observation bd297bf6-bb9e-4647-9e53-90e4a21510a6 · outbound
This paper cites Inflammation and cancer: triggers, mechanisms, and con- sequences.
NACNet: A Histology Context-aware Transformer Graph Convolution Network for Predicting Treatment Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer Inflammation and cancer: triggers, mechanisms, and con- sequences
Reference 71
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 93b02bbd-3313-4792-beac-b0dbfff3f1f2 · outbound
This paper cites Deep learning-based tumor microenvironment analysis in colon adenocarcinoma histopathological whole-slide images.
NACNet: A Histology Context-aware Transformer Graph Convolution Network for Predicting Treatment Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer Deep learning-based tumor microenvironment analysis in colon adenocarcinoma histopathological whole-slide images
Reference 72
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 6482eb2a-d476-4461-89f9-c12aea27e154 · outbound
This paper cites A population-level compu- tational histologic signature for invasive breast cancer prognosis.
NACNet: A Histology Context-aware Transformer Graph Convolution Network for Predicting Treatment Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer A population-level compu- tational histologic signature for invasive breast cancer prognosis
Reference 73
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Pith citing papers
No inbound Pith citation observations are available.