From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer | Paper Citation Record

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This paper cites From detection of individual metastases to classification of lymph node status at the pa- tient level: the camelyon17 challenge.

From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer From detection of individual metastases to classification of lymph node status at the pa- tient level: the camelyon17 challenge

Reference 1

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This paper cites Artifi- cial intelligence for diagnosis and gleason grading of prostate cancer: the panda challenge.

From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer Artifi- cial intelligence for diagnosis and gleason grading of prostate cancer: the panda challenge

Reference 2

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This paper cites Histopathology whole slide image anal- ysis with heterogeneous graph representation learning.

From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer Histopathology whole slide image anal- ysis with heterogeneous graph representation learning

Reference 3

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This paper cites Whole slide images are 2d point clouds: Context-aware survival prediction using patch-based graph convolutional networks.

From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer Whole slide images are 2d point clouds: Context-aware survival prediction using patch-based graph convolutional networks

Reference 4

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

From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer Scaling vision transformers to gigapixel images via hierarchical self-supervised learning

Reference 5

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

From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer Towards a general-purpose foundation model for computational pathology

Reference 6

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This paper cites Largekernel3d: Scaling up kernels in 3d sparse cnns.

From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer Largekernel3d: Scaling up kernels in 3d sparse cnns

Reference 7

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This paper cites 4d spatio-temporal convnets: Minkowski convolutional neural networks.

From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer 4d spatio-temporal convnets: Minkowski convolutional neural networks

Reference 8

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This paper cites Spatial architecture and arrangement of tumor-infiltrating lympho- cytes for predicting likelihood of recurrence in early-stage non–small cell lung cancer.

From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer Spatial architecture and arrangement of tumor-infiltrating lympho- cytes for predicting likelihood of recurrence in early-stage non–small cell lung cancer

Reference 9

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This paper cites Chang, Manolis Savva, Maciej Hal- ber, Thomas Funkhouser, and Matthias Nießner.

From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer Chang, Manolis Savva, Maciej Hal- ber, Thomas Funkhouser, and Matthias Nießner

Reference 10

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This paper cites Bladder cancer.

From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer Bladder cancer

Reference 11

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This paper cites An outcome pre- diction model for patients with clear cell renal cell carcinoma treated with radical nephrectomy based on tumor stage, size, grade and necrosis: the ssign score.

From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer An outcome pre- diction model for patients with clear cell renal cell carcinoma treated with radical nephrectomy based on tumor stage, size, grade and necrosis: the ssign score

Reference 12

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This paper cites PanNuke Dataset Extension, Insights and Baselines.

From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer PanNuke Dataset Extension, Insights and Baselines

Reference 13

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This paper cites 3d semantic segmentation with submani- fold sparse convolutional networks.

From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer 3d semantic segmentation with submani- fold sparse convolutional networks

Reference 14

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

From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer Hover-net: Simultaneous segmentation and classi- fication of nuclei in multi-tissue histology images

Reference 15

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This paper cites Lizard: A large-scale dataset for colonic nuclear instance segmentation and classification.

From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer Lizard: A large-scale dataset for colonic nuclear instance segmentation and classification

Reference 16

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This paper cites Toward a shared vision for cancer genomic data.

From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer Toward a shared vision for cancer genomic data

Reference 17

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This paper cites Deep residual learning for image recognition.

From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer Deep residual learning for image recognition

Reference 18

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This paper cites Quilt-1m: One million image-text pairs for histopathology.

From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer Quilt-1m: One million image-text pairs for histopathology

Reference 19

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This paper cites Attention-based deep multiple instance learning.

From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer Attention-based deep multiple instance learning

Reference 20

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This paper cites Model- ing dense multimodal interactions between biological path- ways and histology for survival prediction.

From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer Model- ing dense multimodal interactions between biological path- ways and histology for survival prediction

Reference 21

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This paper cites Adam: A Method for Stochastic Optimization.

From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer Adam: A Method for Stochastic Optimization

Reference 22

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This paper cites A dataset and a technique for generalized nuclear segmentation for computational pathology.

From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer A dataset and a technique for generalized nuclear segmentation for computational pathology

Reference 23

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This paper cites Pointpillars: Fast encoders for object detection from point clouds.

From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer Pointpillars: Fast encoders for object detection from point clouds

Reference 24

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

From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer Dynamic graph repre- sentation with knowledge-aware attention for histopathology whole slide image analysis

Reference 25

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This paper cites PointMamba: A Simple State Space Model for Point Cloud Analysis.

From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer PointMamba: A Simple State Space Model for Point Cloud Analysis

Reference 26

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

From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer Interventional bag multi-instance learning on whole-slide pathological images

Reference 27

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This paper cites An integrated tcga pan-cancer clinical data resource to drive high-quality survival outcome analyt- ics.

From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer An integrated tcga pan-cancer clinical data resource to drive high-quality survival outcome analyt- ics

Reference 28

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This paper cites On the Variance of the Adaptive Learning Rate and Beyond.

From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer On the Variance of the Adaptive Learning Rate and Beyond

Reference 29

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

From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer Data-efficient and weakly supervised computational pathology on whole- slide images

Reference 30

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This paper cites A Foundational Multimodal Vision Language AI Assistant for Human Pathology.

From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer A Foundational Multimodal Vision Language AI Assistant for Human Pathology

Reference 31

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

From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer A visual- language foundation model for computational pathology

Reference 32

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

From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer A visual- language foundation model for computational pathology

Reference 33

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This paper cites Re- thinking network design and local geometry in point cloud: A simple residual mlp framework.

From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer Re- thinking network design and local geometry in point cloud: A simple residual mlp framework

Reference 34

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This paper cites V oxnet: A 3d con- volutional neural network for real-time object recognition.

From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer V oxnet: A 3d con- volutional neural network for real-time object recognition

Reference 35

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This paper cites Sparse multi-modal graph transformer with shared-context processing for representation learning of giga-pixel images.

From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer Sparse multi-modal graph transformer with shared-context processing for representation learning of giga-pixel images

Reference 36

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This paper cites an unresolved cited work.

From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer Unresolved cited work

Reference 37

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This paper cites Pointnet: Deep learning on point sets for 3d classification and segmentation.

From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer Pointnet: Deep learning on point sets for 3d classification and segmentation

Reference 38

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This paper cites Pointnet++: Deep hierarchical feature learning on point sets in a metric space.

From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer Pointnet++: Deep hierarchical feature learning on point sets in a metric space

Reference 39

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This paper cites Ivt: An end-to-end instance-guided video transformer for 3d pose estimation.

From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer Ivt: An end-to-end instance-guided video transformer for 3d pose estimation

Reference 40

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This paper cites Learning degradation-robust spatiotemporal frequency-transformer for video super- resolution.

From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer Learning degradation-robust spatiotemporal frequency-transformer for video super- resolution

Reference 41

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Observation 361a7173-0877-4002-b881-b74c3e76953f · outbound

This paper cites End-to-end Multi-source Visual Prompt Tuning for Survival Analysis in Whole Slide Images.

From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer End-to-end Multi-source Visual Prompt Tuning for Survival Analysis in Whole Slide Images

Reference 42

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This paper cites The digital brain tumour atlas, an open histopathology resource.

From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer The digital brain tumour atlas, an open histopathology resource

Reference 43

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This paper cites Spatial organization and molecular correlation of tumor-infiltrating lymphocytes using deep learning on pathology images.

From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer Spatial organization and molecular correlation of tumor-infiltrating lymphocytes using deep learning on pathology images

Reference 44

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This paper cites The molecular and cellular heterogeneity of pancreatic ductal adenocarcinoma.

From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer The molecular and cellular heterogeneity of pancreatic ductal adenocarcinoma

Reference 45

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Observation 9b02b1a6-5b70-40d8-ba05-d502a6bcd852 · outbound

This paper cites Tumor micro-environment interactions guided graph learning for survival analysis of human can- cers from whole-slide pathological images.

From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer Tumor micro-environment interactions guided graph learning for survival analysis of human can- cers from whole-slide pathological images

Reference 46

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Observation db173581-03d0-45c2-a320-86e43c3f597c · outbound

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

From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer Transmil: Transformer based correlated multiple instance learning for whole slide image classification

Reference 47

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This paper cites Dpa-p2pnet: Deformable proposal-aware p2pnet for accurate point-based cell detection.

From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer Dpa-p2pnet: Deformable proposal-aware p2pnet for accurate point-based cell detection

Reference 48

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Observation 6d8bd9a6-9aed-4f48-a533-0526a3fd916d · outbound

This paper cites Mor- phological prototyping for unsupervised slide representation learning in computational pathology.

From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer Mor- phological prototyping for unsupervised slide representation learning in computational pathology

Reference 49

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Observation 96deeec0-2fd6-4e0a-8327-d86528cf01a4 · outbound

This paper cites A postoperative prognostic nomogram predicting recurrence for patients with conven- tional clear cell renal cell carcinoma.The Journal of urology, 173(1):48–51, 2005.

From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer A postoperative prognostic nomogram predicting recurrence for patients with conven- tional clear cell renal cell carcinoma.The Journal of urology, 173(1):48–51, 2005

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:24:04.718338Z

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

Observation 3505f66c-a793-4ea2-8609-d014f72974ac · outbound

This paper cites Pathasst: Redefining pathology through generative founda- tion ai assistant for pathology, 2023.

From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer Pathasst: Redefining pathology through generative founda- tion ai assistant for pathology, 2023

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:24:04.706477Z

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

Observation 687f545a-f117-4fff-99ce-ebff29dbdafa · outbound

This paper cites Methods for segmen- tation and classification of digital microscopy tissue images.

From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer Methods for segmen- tation and classification of digital microscopy tissue images

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:24:04.694981Z

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Observation 0a12505d-11af-4d70-9442-d59856488a71 · outbound

This paper cites Octformer: Octree-based transformers for 3d point clouds.

From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer Octformer: Octree-based transformers for 3d point clouds

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:24:04.682970Z

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

Observation da62e28b-6242-41b9-8b88-1c961f244ef1 · outbound

This paper cites Deep learning of cell spatial organizations identifies clinically relevant insights in tissue images.

From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer Deep learning of cell spatial organizations identifies clinically relevant insights in tissue images

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:24:04.670638Z

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

Observation d7009059-165d-4fe0-af30-fa710f708132 · outbound

This paper cites A pathology foundation model for can- cer diagnosis and prognosis prediction.

From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer A pathology foundation model for can- cer diagnosis and prognosis prediction

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Resolution: unresolved; no resolver link, observed 2026-08-11T10:24:04.487079Z

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Observation 68c0a926-117e-4c9a-a920-ca68d505fab9 · outbound

This paper cites Point transformer v2: Grouped vector atten- tion and partition-based pooling.

From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer Point transformer v2: Grouped vector atten- tion and partition-based pooling

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-11T10:24:04.492262Z

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Observation 52b4d7d0-a2d8-46b7-a430-19847f5b0e84 · outbound

This paper cites Point transformer v3: Simpler faster stronger.

From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer Point transformer v3: Simpler faster stronger

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:24:04.645998Z

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Observation 2f7dde4e-de73-41e6-ae81-e634a9c90b3d · outbound

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

From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer A whole-slide foundation model for digital pathology from real-world data

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:24:04.634801Z

Source-reported events for the cited work

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

Observation b168be1b-c8aa-42b6-be66-182fd30a8a34 · outbound

This paper cites Pointweb: Enhancing local neighborhood features for point cloud processing.

From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer Pointweb: Enhancing local neighborhood features for point cloud processing

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:24:04.623268Z

Source-reported events for the cited work

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

Observation 6e32a759-364d-4e38-b646-18c51d340883 · outbound

This paper cites Point transformer.

From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer Point transformer

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:24:04.610081Z

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