Democratizing and accelerating AI-driven pathology research through agentic intelligence | Paper Citation Record

Observation 44f942dd-2ed7-4096-b8c7-c2f2c00b90dd · outbound

This paper cites A.et al.Application of artificial intelligence and digital tools in cancer pathology.The Lancet Digit.

Democratizing and accelerating AI-driven pathology research through agentic intelligence A.et al.Application of artificial intelligence and digital tools in cancer pathology.The Lancet Digit

Reference 1

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Observation 46c1e568-7e3f-4711-b3fa-d8762b17df4a · outbound

This paper cites PathBench: A comprehensive comparison benchmark for pathology foundation models towards precision oncology.

Democratizing and accelerating AI-driven pathology research through agentic intelligence PathBench: A comprehensive comparison benchmark for pathology foundation models towards precision oncology

Reference 2

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Observation be0f7148-8f0b-43ff-8b59-e211cb2e4550 · outbound

This paper cites J.et al.Towards a general-purpose foundation model for computational pathology.Nat.

Democratizing and accelerating AI-driven pathology research through agentic intelligence J.et al.Towards a general-purpose foundation model for computational pathology.Nat

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Observation b8d22c4a-99ac-467e-acb5-f73d213afdad · outbound

This paper cites medicine30, 2924–2935 (2024).

Democratizing and accelerating AI-driven pathology research through agentic intelligence medicine30, 2924–2935 (2024)

Reference 4

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This paper cites an unresolved cited work.

Democratizing and accelerating AI-driven pathology research through agentic intelligence Unresolved cited work

Reference 5

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Observation e7026822-881b-479c-b3d1-c4948986e91a · outbound

This paper cites Molecular-driven Foundation Model for Oncologic Pathology.

Democratizing and accelerating AI-driven pathology research through agentic intelligence Molecular-driven Foundation Model for Oncologic Pathology

Reference 6

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Observation e1db35cb-153f-4831-a7fa-7000c47d098f · outbound

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

Democratizing and accelerating AI-driven pathology research through agentic intelligence Y .et al.Data-efficient and weakly supervised computational pathology on whole-slide images.Nat

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Observation a44e45eb-60c9-4ac2-a52e-37b6bbb23d98 · outbound

This paper cites A Survey on Computational Pathology Foundation Models: Datasets, Adaptation Strategies, and Evaluation Tasks.

Democratizing and accelerating AI-driven pathology research through agentic intelligence A Survey on Computational Pathology Foundation Models: Datasets, Adaptation Strategies, and Evaluation Tasks

Reference 8

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This paper cites an unresolved cited work.

Democratizing and accelerating AI-driven pathology research through agentic intelligence Unresolved cited work

Reference 9

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This paper cites an unresolved cited work.

Democratizing and accelerating AI-driven pathology research through agentic intelligence Unresolved cited work

Reference 10

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Observation 6101c982-108a-4fb9-8066-19c3ebcf1689 · outbound

This paper cites Foundation Models in Computational Pathology: A Review of Challenges, Opportunities, and Impact.

Democratizing and accelerating AI-driven pathology research through agentic intelligence Foundation Models in Computational Pathology: A Review of Challenges, Opportunities, and Impact

Reference 11

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Observation b783bb38-2b43-4e44-803a-42b3a547466a · outbound

This paper cites Autonomous Agents for Scientific Discovery: Orchestrating Scientists, Language, Code, and Physics.

Democratizing and accelerating AI-driven pathology research through agentic intelligence Autonomous Agents for Scientific Discovery: Orchestrating Scientists, Language, Code, and Physics

Reference 12

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Observation d2f331a2-3dec-4cd9-a67c-4d50e778ffd7 · outbound

This paper cites Medicine1–13 (2026).

Democratizing and accelerating AI-driven pathology research through agentic intelligence Medicine1–13 (2026)

Reference 13

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This paper cites an unresolved cited work.

Democratizing and accelerating AI-driven pathology research through agentic intelligence Unresolved cited work

Reference 14

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Observation d39606de-19b1-4b07-bae1-65ab2fa43712 · outbound

This paper cites 23.Xu, G.et al.A comprehensive survey of agentic ai in healthcare.Authorea Prepr.(2025).

Democratizing and accelerating AI-driven pathology research through agentic intelligence 23.Xu, G.et al.A comprehensive survey of agentic ai in healthcare.Authorea Prepr.(2025)

Reference 15

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Observation 6bd09ee7-a4f3-4af5-8fe9-82ad3300a36a · outbound

This paper cites Lammi-pathology: A tool-centric bottom- up lvlm-agent framework for molecularly informed medical intelligence in pathology.

Democratizing and accelerating AI-driven pathology research through agentic intelligence Lammi-pathology: A tool-centric bottom- up lvlm-agent framework for molecularly informed medical intelligence in pathology

Reference 16

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Observation 77cf193f-8331-43c3-a981-ca1c93059ac7 · outbound

This paper cites an unresolved cited work.

Democratizing and accelerating AI-driven pathology research through agentic intelligence Unresolved cited work

Reference 17

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Observation fbba6abe-b8a3-4e91-bec5-6e53ea8c3086 · outbound

This paper cites Data12, 138 (2025).

Democratizing and accelerating AI-driven pathology research through agentic intelligence Data12, 138 (2025)

Reference 18

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Observation 7f831701-40f8-4dbb-b442-9d61c42776d9 · outbound

This paper cites & Bjerregaard, B.

Democratizing and accelerating AI-driven pathology research through agentic intelligence & Bjerregaard, B

Reference 19

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Democratizing and accelerating AI-driven pathology research through agentic intelligence Unresolved cited work

Reference 20

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This paper cites an unresolved cited work.

Democratizing and accelerating AI-driven pathology research through agentic intelligence Unresolved cited work

Reference 21

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Observation 720e3faf-af37-4527-9d06-330474d80a35 · outbound

This paper cites 31.Weinstein, J.

Democratizing and accelerating AI-driven pathology research through agentic intelligence 31.Weinstein, J

Reference 22

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Observation 3a27a2fd-a894-4a12-8b72-f624728560a6 · outbound

This paper cites J.et al.The cptac data portal: a resource for cancer proteomics research.J.

Democratizing and accelerating AI-driven pathology research through agentic intelligence J.et al.The cptac data portal: a resource for cancer proteomics research.J

Reference 23

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Observation 40746767-ba84-4a7d-ad75-966ed5eff554 · outbound

This paper cites medicine28, 154–163 (2022).

Democratizing and accelerating AI-driven pathology research through agentic intelligence medicine28, 154–163 (2022)

Reference 24

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Observation ad567b7b-fa92-4ca4-9a8e-72a8b358e29f · outbound

This paper cites an unresolved cited work.

Democratizing and accelerating AI-driven pathology research through agentic intelligence Unresolved cited work

Reference 25

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Observation 786ef690-dbcb-4471-8369-20617ceff760 · outbound

This paper cites & Goswami, S.

Democratizing and accelerating AI-driven pathology research through agentic intelligence & Goswami, S

Reference 26

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This paper cites an unresolved cited work.

Democratizing and accelerating AI-driven pathology research through agentic intelligence Unresolved cited work

Reference 27

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Observation a3bee85a-bb96-4c8c-8199-eed6240f8bfa · outbound

This paper cites I want to train a segmentation model using the Liver_OS dataset to precisely delineate the tumor boundaries in the WSIs.

Democratizing and accelerating AI-driven pathology research through agentic intelligence I want to train a segmentation model using the Liver_OS dataset to precisely delineate the tumor boundaries in the WSIs

Reference 28

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Observation 44caec31-d747-486c-909a-557e05535ef6 · outbound

This paper cites Please set up a patient survival prediction task using the PanNuke dataset.

Democratizing and accelerating AI-driven pathology research through agentic intelligence Please set up a patient survival prediction task using the PanNuke dataset

Reference 29

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Observation a640d57a-3bc7-4a04-adc8-23d0e8aeca35 · outbound

This paper cites Can we run a survival analysis on the BRACS dataset? I want to predict patient outcomes.

Democratizing and accelerating AI-driven pathology research through agentic intelligence Can we run a survival analysis on the BRACS dataset? I want to predict patient outcomes

Reference 30

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Observation 9bb2c21c-66e4-47a5-8759-1db467274e93 · outbound

This paper cites Train a WSI classification model using the CRC-MSI dataset to differentiate the slides.

Democratizing and accelerating AI-driven pathology research through agentic intelligence Train a WSI classification model using the CRC-MSI dataset to differentiate the slides

Reference 31

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Observation 47c5116e-29c8-4d7b-af4c-9257d3fd066b · outbound

This paper cites I’d like to use the SegPC dataset to classify bone marrow cancer vs normal tissues.

Democratizing and accelerating AI-driven pathology research through agentic intelligence I’d like to use the SegPC dataset to classify bone marrow cancer vs normal tissues

Reference 32

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Observation 028785b2-d0ec-470b-9d1b-8d916b4f35ef · outbound

This paper cites Let’s do tissue segmentation on the Lung_Cancer Nanfang Cohort WSIs.

Democratizing and accelerating AI-driven pathology research through agentic intelligence Let’s do tissue segmentation on the Lung_Cancer Nanfang Cohort WSIs

Reference 33

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Observation 70b3732e-9704-419c-9f48-6a3ccb81bc54 · outbound

This paper cites Configure a prognosis survival model on the HiCervix cell patches.

Democratizing and accelerating AI-driven pathology research through agentic intelligence Configure a prognosis survival model on the HiCervix cell patches

Reference 34

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Observation a03e2ee0-22f5-455f-a47a-b792a3287ac5 · outbound

This paper cites Build an ISUP grading segmentation mask generator using the PANDA dataset.

Democratizing and accelerating AI-driven pathology research through agentic intelligence Build an ISUP grading segmentation mask generator using the PANDA dataset

Reference 35

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Observation d86beb58-7f38-43cb-9107-82549374095f · outbound

This paper cites Use the CAMELYON16 WSIs to extract 256x256 patches and directly output a segmentation mask using U-Net.

Democratizing and accelerating AI-driven pathology research through agentic intelligence Use the CAMELYON16 WSIs to extract 256x256 patches and directly output a segmentation mask using U-Net

Reference 36

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Observation 0a04793d-8a15-4fa8-a67e-52330386f41d · outbound

This paper cites Can you train a Cox proportional hazards model on the UBC-OCEAN dataset?.

Democratizing and accelerating AI-driven pathology research through agentic intelligence Can you train a Cox proportional hazards model on the UBC-OCEAN dataset?

Reference 37

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Observation a0db73dc-f3bd-4751-9896-9f73c340c6ca · outbound

This paper cites 帮我用Liver_OS数据集训练一个分割模型，我想把肝癌WSI里的肿瘤区域精准勾画出来。.

Democratizing and accelerating AI-driven pathology research through agentic intelligence 帮我用Liver_OS数据集训练一个分割模型，我想把肝癌WSI里的肿瘤区域精准勾画出来。

Reference 38

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Observation 846b6b5d-4978-4e50-b010-24fea5997c59 · outbound

This paper cites 请用PanNuke细胞核数据集建一个患者生存期预测模型。.

Democratizing and accelerating AI-driven pathology research through agentic intelligence 请用PanNuke细胞核数据集建一个患者生存期预测模型。

Reference 39

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Observation 954ab159-ed52-4748-a877-6c11f01e9b74 · outbound

This paper cites 用BRACS乳腺癌数据集跑一个生存分析吧，我想看看患者预后。.

Democratizing and accelerating AI-driven pathology research through agentic intelligence 用BRACS乳腺癌数据集跑一个生存分析吧，我想看看患者预后。

Reference 40

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Observation 3fa02dad-2d59-4457-a525-856e1315547e · outbound

This paper cites 在CRC-MSI这个数据集上，配置一个整切片(WSI)级别的分类任务。.

Democratizing and accelerating AI-driven pathology research through agentic intelligence 在CRC-MSI这个数据集上，配置一个整切片(WSI)级别的分类任务。

Reference 41

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Observation 8a42e75c-dc9b-4f59-a534-1b4cf1570f4f · outbound

This paper cites 我想用SegPC数据集训练一个骨髓瘤细胞的分类器（正常vs异常）。.

Democratizing and accelerating AI-driven pathology research through agentic intelligence 我想用SegPC数据集训练一个骨髓瘤细胞的分类器（正常vs异常）。

Reference 42

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Observation e46622c1-bf6a-4795-bff4-07d14b7730a8 · outbound

This paper cites 帮我在Lung_Cancer南方医院队列上做一个组织分割任务，把良恶性区域割出来。.

Democratizing and accelerating AI-driven pathology research through agentic intelligence 帮我在Lung_Cancer南方医院队列上做一个组织分割任务，把良恶性区域割出来。

Reference 43

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Observation 8bb44f52-e957-407a-88cd-f30752fa2d07 · outbound

This paper cites 用HiCervix宫颈细胞切片预测一下患者的总生存期(OS)。.

Democratizing and accelerating AI-driven pathology research through agentic intelligence 用HiCervix宫颈细胞切片预测一下患者的总生存期(OS)。

Reference 44

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Observation df97aca5-fca2-4b10-9799-05b4990f6219 · outbound

This paper cites 基于PANDA数据集，训练一个U-Net模型来生成前列腺癌的掩膜(Mask)。.

Democratizing and accelerating AI-driven pathology research through agentic intelligence 基于PANDA数据集，训练一个U-Net模型来生成前列腺癌的掩膜(Mask)。

Reference 45

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Observation 1d7b691a-b1cd-4e8d-ad7d-e34014abe742 · outbound

This paper cites 用CAMELYON16训练一个模型，输入整张WSI，直接输出乳腺癌转移的精准分割边界。.

Democratizing and accelerating AI-driven pathology research through agentic intelligence 用CAMELYON16训练一个模型，输入整张WSI，直接输出乳腺癌转移的精准分割边界。

Reference 46

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Observation 02f98cd0-3c2b-453c-b112-4b1c0ef057e3 · outbound

This paper cites 针对UBC-OCEAN数据集，配置一个Cox生存预测任务吧。.

Democratizing and accelerating AI-driven pathology research through agentic intelligence 针对UBC-OCEAN数据集，配置一个Cox生存预测任务吧。

Reference 47

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Observation 840c5a25-aee9-4e5d-8ae6-9258ed0b80c9 · outbound

This paper cites Configure a training task on the LUAD_LUSC (TCGA) dataset. Set the batch_size to 64 to speed up WSI training.

Democratizing and accelerating AI-driven pathology research through agentic intelligence Configure a training task on the LUAD_LUSC (TCGA) dataset. Set the batch_size to 64 to speed up WSI training

Reference 48

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Observation 1300897d-173f-40ef-b130-e80c7feb2a8e · outbound

This paper cites For the CRC-MSI patch dataset, please use the ABMIL aggregator to train the model.

Democratizing and accelerating AI-driven pathology research through agentic intelligence For the CRC-MSI patch dataset, please use the ABMIL aggregator to train the model

Reference 49

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Observation fa9f90be-e9d8-46c2-b8d8-9fede3e81fc5 · outbound

This paper cites Train a U-Net segmentation model on the PANDA dataset to find the Gleason patterns.

Democratizing and accelerating AI-driven pathology research through agentic intelligence Train a U-Net segmentation model on the PANDA dataset to find the Gleason patterns

Reference 50

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Observation e22456da-5b3d-498a-88fa-c2f339fe062b · outbound

This paper cites Let’s use TransMIL to aggregate features for the HiCervix cell classification task.

Democratizing and accelerating AI-driven pathology research through agentic intelligence Let’s use TransMIL to aggregate features for the HiCervix cell classification task

Reference 51

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Observation 92ef7718-0bb5-45f3-9b05-906dfe861e80 · outbound

This paper cites Train a classification model on CAMELYON16 using a simple ’linear’ classifier head.

Democratizing and accelerating AI-driven pathology research through agentic intelligence Train a classification model on CAMELYON16 using a simple ’linear’ classifier head

Reference 52

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Observation eb8f0239-96e8-4fa6-ba5b-98725b175508 · outbound

This paper cites Set up a foundation model segmentation task using ’vit_l_16’ as the backbone.

Democratizing and accelerating AI-driven pathology research through agentic intelligence Set up a foundation model segmentation task using ’vit_l_16’ as the backbone

Reference 53

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Observation 05ec6c76-6131-4fca-af01-ab9f9f06a8c1 · outbound

This paper cites Use CLAM_MB on the SegPC dataset to find the multiple cell branches.

Democratizing and accelerating AI-driven pathology research through agentic intelligence Use CLAM_MB on the SegPC dataset to find the multiple cell branches

Reference 54

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Observation 32730a05-3c90-4a7a-ba17-ee14314915c0 · outbound

This paper cites Train a survival model on Liver_OS using L1 Loss.

Democratizing and accelerating AI-driven pathology research through agentic intelligence Train a survival model on Liver_OS using L1 Loss

Reference 55

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Observation 6dede568-4a27-4334-98a4-7d102e32f069 · outbound

This paper cites Set the learning rate to 0.5 and batch size to 128 for the Lung-MUT-EGFR WSI dataset.

Democratizing and accelerating AI-driven pathology research through agentic intelligence Set the learning rate to 0.5 and batch size to 128 for the Lung-MUT-EGFR WSI dataset

Reference 56

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Observation a66e7e90-dca0-4a49-99ac-0902a35c5201 · outbound

This paper cites For the PanNuke dataset, use maxmil as the aggregator to classify the nuclei.

Democratizing and accelerating AI-driven pathology research through agentic intelligence For the PanNuke dataset, use maxmil as the aggregator to classify the nuclei

Reference 57

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Observation 01ef417c-aa46-475d-a29b-a6bbc71f1841 · outbound

This paper cites 配置LUAD_LUSC数据集的训练任务，为了加速收敛，把batch_size设置成64跑WSI。.

Democratizing and accelerating AI-driven pathology research through agentic intelligence 配置LUAD_LUSC数据集的训练任务，为了加速收敛，把batch_size设置成64跑WSI。

Reference 58

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Observation bd520162-8196-4798-9ce7-c1282df83dca · outbound

This paper cites 针对CRC-MSI这个Patch数据集，帮我配置一个ABMIL聚合器。.

Democratizing and accelerating AI-driven pathology research through agentic intelligence 针对CRC-MSI这个Patch数据集，帮我配置一个ABMIL聚合器。

Reference 59

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Observation a592c538-a884-428e-9914-84f644130aa4 · outbound

This paper cites 用U-Net架构在PANDA数据集上跑，我想把不同Gleason分级的区域分出来。.

Democratizing and accelerating AI-driven pathology research through agentic intelligence 用U-Net架构在PANDA数据集上跑，我想把不同Gleason分级的区域分出来。

Reference 60

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Observation 6f36a71b-ea85-43b4-a4ee-c59e82791955 · outbound

This paper cites 在HiCervix宫颈细胞分类上，使用TransMIL算法来聚合细胞特征。.

Democratizing and accelerating AI-driven pathology research through agentic intelligence 在HiCervix宫颈细胞分类上，使用TransMIL算法来聚合细胞特征。

Reference 61

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Observation 4af31ec0-d86b-4986-a2f8-bbf31d9b17d2 · outbound

This paper cites 用CAMELYON16训练分类模型，分类头直接选最简单的’linear’就行。.

Democratizing and accelerating AI-driven pathology research through agentic intelligence 用CAMELYON16训练分类模型，分类头直接选最简单的’linear’就行。

Reference 62

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Observation 56d3a557-abe4-4fd8-a00d-690b7676406d · outbound

This paper cites 帮我建一个基础模型分割任务，骨干网络(backbone)指定用vit_l_16。.

Democratizing and accelerating AI-driven pathology research through agentic intelligence 帮我建一个基础模型分割任务，骨干网络(backbone)指定用vit_l_16。

Reference 63

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Observation 6232409c-ccf1-44f3-93c9-428085500394 · outbound

This paper cites 在SegPC数据集上用CLAM_MB多分支模型来预测骨髓瘤细胞。.

Democratizing and accelerating AI-driven pathology research through agentic intelligence 在SegPC数据集上用CLAM_MB多分支模型来预测骨髓瘤细胞。

Reference 64

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Observation 70165a5c-1b6e-42c0-8861-19a25275bbc3 · outbound

This paper cites 用Liver_OS训练生存预测模型，损失函数帮我选L1 Loss。.

Democratizing and accelerating AI-driven pathology research through agentic intelligence 用Liver_OS训练生存预测模型，损失函数帮我选L1 Loss。

Reference 65

Resolution: unresolved; no resolver link, observed 2026-06-27T04:34:18.363493Z

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Observation e593bd6a-c562-4985-be54-d44aad7bef6d · outbound

This paper cites 训练Lung-MUT-EGFR，把学习率设为0.5，batch size设为128猛跑。.

Democratizing and accelerating AI-driven pathology research through agentic intelligence 训练Lung-MUT-EGFR，把学习率设为0.5，batch size设为128猛跑。

Reference 66

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Observation 1b55aade-f9f1-4c9f-b15d-76d840fc0456 · outbound

This paper cites PanNuke数据集，用maxmil聚合器把每个细胞核的类别最高分聚合起来。.

Democratizing and accelerating AI-driven pathology research through agentic intelligence PanNuke数据集，用maxmil聚合器把每个细胞核的类别最高分聚合起来。

Reference 67

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Observation b74a78ee-9728-4b07-9c33-3b73af41e4cd · outbound

This paper cites I want to train a model to ONLY recognize LUAD. Filter the LUAD_LUSC dataset to keep only LUAD cases, and train a classifier on it.

Democratizing and accelerating AI-driven pathology research through agentic intelligence I want to train a model to ONLY recognize LUAD. Filter the LUAD_LUSC dataset to keep only LUAD cases, and train a classifier on it

Reference 68

Resolution: unresolved; no resolver link, observed 2026-06-27T04:34:18.363493Z

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Observation a43877dd-6ac9-45b2-901c-772697ec7780 · outbound

This paper cites Set up a 5-fold cross-validation training task for the ’Study of Abnormal Cells in Body Fluids’ dataset.

Democratizing and accelerating AI-driven pathology research through agentic intelligence Set up a 5-fold cross-validation training task for the ’Study of Abnormal Cells in Body Fluids’ dataset

Reference 69

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Observation ff08dcd6-5765-4144-8c22-befa0dee90b6 · outbound

This paper cites Filter the Liver_OS dataset to only include patients who died (event=1), and train the survival model to predict their death.

Democratizing and accelerating AI-driven pathology research through agentic intelligence Filter the Liver_OS dataset to only include patients who died (event=1), and train the survival model to predict their death

Reference 70

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Observation 8670cc0d-1bf1-43ed-a22c-390aa58c1e8b · outbound

This paper cites Use the random 7:1:2 split strategy for the ’Study of Abnormal Cells in Body Fluids’ dataset.

Democratizing and accelerating AI-driven pathology research through agentic intelligence Use the random 7:1:2 split strategy for the ’Study of Abnormal Cells in Body Fluids’ dataset

Reference 71

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Observation a9913587-a58b-4dd5-a6de-28a18c799305 · outbound

This paper cites Train a model on BRCA-MUT-TP53 to only predict TP53 positive cases without any negative controls.

Democratizing and accelerating AI-driven pathology research through agentic intelligence Train a model on BRCA-MUT-TP53 to only predict TP53 positive cases without any negative controls

Reference 72

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Observation 2256b665-4d6b-4949-a505-fdcf754abef1 · outbound

This paper cites Let’s do a 50-fold cross validation on the Lung_Cancer dataset to get highly robust metrics.

Democratizing and accelerating AI-driven pathology research through agentic intelligence Let’s do a 50-fold cross validation on the Lung_Cancer dataset to get highly robust metrics

Reference 73

Resolution: unresolved; no resolver link, observed 2026-06-27T04:34:18.363493Z

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Observation c5ab248a-1ab0-40a4-b295-76656cb0e52f · outbound

This paper cites Remove all the benign cases from Lung_Cancer Nanfang Cohort and train a diagnostic classifier.

Democratizing and accelerating AI-driven pathology research through agentic intelligence Remove all the benign cases from Lung_Cancer Nanfang Cohort and train a diagnostic classifier

Reference 74

Resolution: unresolved; no resolver link, observed 2026-06-27T04:34:18.363493Z

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Observation 4de04d62-2f50-4d9a-9b53-f37b9940fe0f · outbound

This paper cites Filter out all censored data in Liver_OS because I only care about exact death times, then train Cox.

Democratizing and accelerating AI-driven pathology research through agentic intelligence Filter out all censored data in Liver_OS because I only care about exact death times, then train Cox

Reference 75

Resolution: unresolved; no resolver link, observed 2026-06-27T04:34:18.363493Z

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Observation c6ea589c-5e45-4f09-8a95-33333e446a8e · outbound

This paper cites Run a random split classification training on a tiny subset of PANDA containing only 6 WSIs.

Democratizing and accelerating AI-driven pathology research through agentic intelligence Run a random split classification training on a tiny subset of PANDA containing only 6 WSIs

Reference 76

Resolution: unresolved; no resolver link, observed 2026-06-27T04:34:18.363493Z

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Observation caf971a1-a0c4-4929-a1c4-b881e14d14e9 · outbound

This paper cites Train a classifier on the CAMELYON16 dataset, but only feed it the normal slides to teach it what normal looks like.

Democratizing and accelerating AI-driven pathology research through agentic intelligence Train a classifier on the CAMELYON16 dataset, but only feed it the normal slides to teach it what normal looks like

Reference 77

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Observation 8782601c-7764-4169-a635-fbc382cfb779 · outbound

This paper cites 我想训练一个专门识别LUAD的分类器。把LUAD_LUSC数据集里的LUSC全删掉，只用LUAD训练。.

Democratizing and accelerating AI-driven pathology research through agentic intelligence 我想训练一个专门识别LUAD的分类器。把LUAD_LUSC数据集里的LUSC全删掉，只用LUAD训练。

Reference 78

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Observation 1a2ee479-da12-4ab1-84c2-fe7bc006e5ea · outbound

This paper cites 用’Study of Abnormal Cells in Body Fluids’数据集，帮我跑一个10折交叉验证(10-fold CV)看看效果。.

Democratizing and accelerating AI-driven pathology research through agentic intelligence 用’Study of Abnormal Cells in Body Fluids’数据集，帮我跑一个10折交叉验证(10-fold CV)看看效果。

Reference 79

Resolution: unresolved; no resolver link, observed 2026-06-27T04:34:18.363493Z

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Observation c78761c4-b1d9-418f-a2c2-cd0528815408 · outbound

This paper cites 把Liver_OS里那些没死的患者(censored=0)全剔除，只用明确死亡的患者训练生存预测模型。.

Democratizing and accelerating AI-driven pathology research through agentic intelligence 把Liver_OS里那些没死的患者(censored=0)全剔除，只用明确死亡的患者训练生存预测模型。

Reference 80

Resolution: unresolved; no resolver link, observed 2026-06-27T04:34:18.363493Z

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Observation 1cfad8c2-28f1-41e6-a164-2cfaa9c5da3e · outbound

This paper cites 在’Study of Abnormal Cells in Body Fluids’数据集上，使用随机7:1:2的策略划分训练集、验证集和测试集。.

Democratizing and accelerating AI-driven pathology research through agentic intelligence 在’Study of Abnormal Cells in Body Fluids’数据集上，使用随机7:1:2的策略划分训练集、验证集和测试集。

Reference 81

Resolution: unresolved; no resolver link, observed 2026-06-27T04:34:18.363493Z

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Observation 5f661c4d-c9f9-46e4-83c7-bb048ccf9443 · outbound

This paper cites 训练一个BRCA-MUT-TP53模型，只输入突变阳性的样本，让它学会认突变。.

Democratizing and accelerating AI-driven pathology research through agentic intelligence 训练一个BRCA-MUT-TP53模型，只输入突变阳性的样本，让它学会认突变。

Reference 82

Resolution: unresolved; no resolver link, observed 2026-06-27T04:34:18.363493Z

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Observation 832337f8-60b0-49cd-b20d-f67bfb7e81cc · outbound

This paper cites 为了让评估绝对客观，在Lung_Cancer数据集上给我配置一个50折交叉验证。.

Democratizing and accelerating AI-driven pathology research through agentic intelligence 为了让评估绝对客观，在Lung_Cancer数据集上给我配置一个50折交叉验证。

Reference 83

Resolution: unresolved; no resolver link, observed 2026-06-27T04:34:18.363493Z

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Observation 3c6f4d79-38af-4ca8-893a-fa45ee2e4268 · outbound

This paper cites 把Lung_Cancer南方医院队列里的良性切片全去掉，纯用恶性切片训练一个诊断模型。.

Democratizing and accelerating AI-driven pathology research through agentic intelligence 把Lung_Cancer南方医院队列里的良性切片全去掉，纯用恶性切片训练一个诊断模型。

Reference 84

Resolution: unresolved; no resolver link, observed 2026-06-27T04:34:18.363493Z

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Observation 1c5d5473-a8a3-4087-b678-b67ef2e3f96b · outbound

This paper cites 生存分析太麻烦，直接把Liver_OS里失访的数据删了，纯用发生事件的数据跑模型。.

Democratizing and accelerating AI-driven pathology research through agentic intelligence 生存分析太麻烦，直接把Liver_OS里失访的数据删了，纯用发生事件的数据跑模型。

Reference 85

Resolution: unresolved; no resolver link, observed 2026-06-27T04:34:18.363493Z

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Observation 086f7545-4fb5-49e5-ad10-7ecd21cc6fd8 · outbound

This paper cites 我从PANDA里挑了5张切片建了个子集，帮我按7:1:2随机划分跑个分类试试。.

Democratizing and accelerating AI-driven pathology research through agentic intelligence 我从PANDA里挑了5张切片建了个子集，帮我按7:1:2随机划分跑个分类试试。

Reference 86

Resolution: unresolved; no resolver link, observed 2026-06-27T04:34:18.363493Z

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Observation e1dd4979-59b2-4c4e-ad24-f2e7b4adff95 · outbound

This paper cites 用CAMELYON16训练模型，但我不给它看转移癌，只喂给它正常组织，让它学会什么是正常的。.

Democratizing and accelerating AI-driven pathology research through agentic intelligence 用CAMELYON16训练模型，但我不给它看转移癌，只喂给它正常组织，让它学会什么是正常的。

Reference 87

Resolution: unresolved; no resolver link, observed 2026-06-27T04:34:18.363493Z

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Observation 46e7101d-6cbb-4b2e-9e0b-47b4a4923523 · outbound

This paper cites I just trained a model on LUAD_LUSC. Can you run external validation using the exact same LUAD_LUSC dataset to double-check?.

Democratizing and accelerating AI-driven pathology research through agentic intelligence I just trained a model on LUAD_LUSC. Can you run external validation using the exact same LUAD_LUSC dataset to double-check?

Reference 88

Resolution: unresolved; no resolver link, observed 2026-06-27T04:34:18.363493Z

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Observation 9ab5bdc8-2953-4639-b34f-55090e92da62 · outbound

This paper cites Validate my Liver_OS survival model on the CAMELYON16 external dataset.

Democratizing and accelerating AI-driven pathology research through agentic intelligence Validate my Liver_OS survival model on the CAMELYON16 external dataset

Reference 89

Resolution: unresolved; no resolver link, observed 2026-06-27T04:34:18.363493Z

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Observation 9ac7ef56-464f-41ec-8d73-3b8c77f0b0bd · outbound

This paper cites I have a WSI model trained on BRACS. Run an external validation on the PanNuke dataset.

Democratizing and accelerating AI-driven pathology research through agentic intelligence I have a WSI model trained on BRACS. Run an external validation on the PanNuke dataset

Reference 90

Resolution: unresolved; no resolver link, observed 2026-06-27T04:34:18.363493Z

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Observation da107104-3b57-4493-98d5-cd8b4c79545a · outbound

This paper cites Take my prostate PANDA grading model (6 classes) and run external validation on Gastric_Intestinal_Metaplasia.

Democratizing and accelerating AI-driven pathology research through agentic intelligence Take my prostate PANDA grading model (6 classes) and run external validation on Gastric_Intestinal_Metaplasia

Reference 91

Resolution: unresolved; no resolver link, observed 2026-06-27T04:34:18.363493Z

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Observation e9b88008-53b0-4e61-bc8d-f3777079ffca · outbound

This paper cites Validate the HiCervix cell classification model on the Liver_OS WSIs.

Democratizing and accelerating AI-driven pathology research through agentic intelligence Validate the HiCervix cell classification model on the Liver_OS WSIs

Reference 92

Resolution: unresolved; no resolver link, observed 2026-06-27T04:34:18.363493Z

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Observation e7f58f41-289b-4b3f-8c8a-2f2d3c8d6ef5 · outbound

This paper cites Run external validation for my Lung-MUT-EGFR model on the CRC-MUT-BRAF dataset, maybe the mutations look similar.

Democratizing and accelerating AI-driven pathology research through agentic intelligence Run external validation for my Lung-MUT-EGFR model on the CRC-MUT-BRAF dataset, maybe the mutations look similar

Reference 93

Resolution: unresolved; no resolver link, observed 2026-06-27T04:34:18.363493Z

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Observation 60077a1e-92b4-4605-9476-fdb02c8898bf · outbound

This paper cites I trained a model on UBC-OCEAN with ResNet50 (dim 1024). Validate it externally on a dataset that only has Virchow (dim 1536) extracted features.

Democratizing and accelerating AI-driven pathology research through agentic intelligence I trained a model on UBC-OCEAN with ResNet50 (dim 1024). Validate it externally on a dataset that only has Virchow (dim 1536) extracted features

Reference 94

Resolution: unresolved; no resolver link, observed 2026-06-27T04:34:18.363493Z

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Observation 56316fbb-e4de-44f5-84ce-5e12e5f1cb0b · outbound

This paper cites Validate my CRC-MSI (patch) model on the LUAD_LUSC (WSI) dataset.

Democratizing and accelerating AI-driven pathology research through agentic intelligence Validate my CRC-MSI (patch) model on the LUAD_LUSC (WSI) dataset

Reference 95

Resolution: unresolved; no resolver link, observed 2026-06-27T04:34:18.363493Z

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Observation 460c7a6e-145e-44a1-9f0e-cb3c93b92202 · outbound

This paper cites Run a segmentation validation for my SegPC model using the Lung_Cancer dataset.

Democratizing and accelerating AI-driven pathology research through agentic intelligence Run a segmentation validation for my SegPC model using the Lung_Cancer dataset

Reference 96

Resolution: unresolved; no resolver link, observed 2026-06-27T04:34:18.363493Z

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Observation 5faca3cf-7ff3-494b-9e2c-ef2fec0b1e52 · outbound

This paper cites I want to do external validation. My model predicts ’Tumor’ vs ’Normal’, but the target dataset has labels ’Malignant’ vs ’Benign’. Just force it to run.

Democratizing and accelerating AI-driven pathology research through agentic intelligence I want to do external validation. My model predicts ’Tumor’ vs ’Normal’, but the target dataset has labels ’Malignant’ vs ’Benign’. Just force it to run

Reference 97

Resolution: unresolved; no resolver link, observed 2026-06-27T04:34:18.363493Z

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Observation dab29039-cf54-43d4-8c78-0a370c36d159 · outbound

This paper cites 我刚在LUAD_LUSC 上训练完一个模型，请用相同的LUAD_LUSC 数据集跑一次外部验证，让我复核一下。.

Democratizing and accelerating AI-driven pathology research through agentic intelligence 我刚在LUAD_LUSC 上训练完一个模型，请用相同的LUAD_LUSC 数据集跑一次外部验证，让我复核一下。

Reference 98

Resolution: unresolved; no resolver link, observed 2026-06-27T04:34:18.363493Z

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Observation f624a0d3-5191-41b4-987b-ef05096e7f68 · outbound

This paper cites 把我的Liver_OS生存预测模型，拿到CAMELYON16乳腺癌数据集上去做个外部验证。.

Democratizing and accelerating AI-driven pathology research through agentic intelligence 把我的Liver_OS生存预测模型，拿到CAMELYON16乳腺癌数据集上去做个外部验证。

Reference 99

Resolution: unresolved; no resolver link, observed 2026-06-27T04:34:18.363493Z

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Observation 24c62dc9-6d66-473a-8704-aacae85614ff · outbound

This paper cites 我用BRACS训练了一个整切片(WSI)模型，帮我在PanNuke数据集上跑外部验证测测泛化性。.

Democratizing and accelerating AI-driven pathology research through agentic intelligence 我用BRACS训练了一个整切片(WSI)模型，帮我在PanNuke数据集上跑外部验证测测泛化性。

Reference 100

Resolution: unresolved; no resolver link, observed 2026-06-27T04:34:18.363493Z

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Pith citing papers