Paper Citation Record · LEDGER

Segment Anything for Histopathology

As of 13 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 3 inbound Pith citation observations for arXiv:2502.00408.

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

pith.paper-citation-record.v1
2502.00408 v2

Coverage vector

measured 40 of 40 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T19:14:08.077830Z

measured 43 of 43 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T17:52:43.350078Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T06:09:37.629003Z

Reference resolution

40 of 40 outbound references displayed

verified exact5
verified fuzzy6
unresolved26
parse uncertain0
malformed identifier0
metadata mismatch3

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 2bc84e85-9af9-4801-9094-701b901dfbef · outbound

This paper cites Nuclick: A deep learning framework for interactive segmentation of microscopic images.

Segment Anything for Histopathology Nuclick: A deep learning framework for interactive segmentation of microscopic images

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-09T19:14:07.923722Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c29e2da4-9deb-4205-9161-f2707a1b7b87 · outbound

This paper cites Segment anything for microscopy.

Segment Anything for Histopathology Segment anything for microscopy

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-09T19:14:07.928169Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9356bfc7-b7e6-40ab-bacb-86fef988eab2 · outbound

This paper cites Medicosam: Towards foundation models for medical image segmentation, 2025 b.

Segment Anything for Histopathology Medicosam: Towards foundation models for medical image segmentation, 2025 b

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-09T19:14:07.932098Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b5793a7b-db71-4b7a-931b-407b88c77b8a · outbound

This paper cites Loughrey, José A.

Segment Anything for Histopathology Loughrey, José A

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-09T19:14:07.936043Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 87e8e2f3-d0a3-4079-8c49-9ec29117d157 · outbound

This paper cites Nagtegaal, Maria Rodriguez Martinez, and Inti Zlobec.

Segment Anything for Histopathology Nagtegaal, Maria Rodriguez Martinez, and Inti Zlobec

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:14:09.570018Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation fa3a3e63-aeea-4e45-87ba-1d04fae07117 · outbound

This paper cites Caicedo, Allen Goodman, Kyle W.

Segment Anything for Histopathology Caicedo, Allen Goodman, Kyle W

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-09T19:14:07.944233Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 87e3fd43-b3be-4980-9b09-de8d30562ca5 · outbound

This paper cites SAM-Med2D.

Segment Anything for Histopathology SAM-Med2D

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-09T19:14:07.950468Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7c7a3a1b-4a51-44f6-a7f7-b408dff89b3c · outbound

This paper cites Landman, Yucheng Tang, Lee E.

Segment Anything for Histopathology Landman, Yucheng Tang, Lee E

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:14:09.559408Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 8d3d6a1e-7f03-42a1-8973-e07f59b2fb25 · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

Segment Anything for Histopathology An image is worth 16x16 words: Transformers for image recognition at scale

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-09T19:14:07.958259Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f359e908-1ed6-4540-8342-4335a568d192 · outbound

This paper cites Pannuke: an open pan-cancer histology dataset for nuclei instance segmentation and classification.

Segment Anything for Histopathology Pannuke: an open pan-cancer histology dataset for nuclei instance segmentation and classification

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-09T19:14:07.961683Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c77abe24-e85e-4a04-9e2e-55b170b6e8ab · outbound

This paper cites InstanSeg: an embedding-based instance segmentation algorithm optimized for accurate, efficient and portable cell segmentation.

Segment Anything for Histopathology InstanSeg: an embedding-based instance segmentation algorithm optimized for accurate, efficient and portable cell segmentation

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-09T19:14:07.966333Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 49f28460-e495-40bd-bcf9-6aadbaba4b49 · outbound

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

Segment Anything for Histopathology Hover-net: Simultaneous segmentation and classification of nuclei in multi-tissue histology images

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-09T19:14:07.971553Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c996ba1d-f513-4974-9af4-961b11f0e2f4 · outbound

This paper cites Ahmed Raza, Hesham El Daly, Kishore Gopalakrishnan, David Snead, and Nasir M.

Segment Anything for Histopathology Ahmed Raza, Hesham El Daly, Kishore Gopalakrishnan, David Snead, and Nasir M

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:14:09.541359Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 49715353-a18f-4091-8999-64735dfb2b40 · outbound

This paper cites Roth, and Daguang Xu.

Segment Anything for Histopathology Roth, and Daguang Xu

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:14:09.530460Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 632ac7b3-49bf-4286-9340-53750d22202b · outbound

This paper cites o rst, Moritz Rempe, Lukas Heine, Constantin Seibold, Julius Keyl, Giulia Baldini, Selma Ugurel, Jens Siveke, Barbara Gr\.

Segment Anything for Histopathology o rst, Moritz Rempe, Lukas Heine, Constantin Seibold, Julius Keyl, Giulia Baldini, Selma Ugurel, Jens Siveke, Barbara Gr\

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-09T19:14:07.982741Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c527a45b-9dc8-4fcd-aa69-8b4c2c074e34 · outbound

This paper cites Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases.

Segment Anything for Histopathology Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases

Reference 16

Resolution: metadata mismatch; raw_fallback, observed 2026-08-09T19:14:09.155740Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation a0750cbd-c370-42d9-9397-e870a273fce8 · outbound

This paper cites Berg, Wan-Yen Lo, Piotr Dollar, and Ross Girshick.

Segment Anything for Histopathology Berg, Wan-Yen Lo, Piotr Dollar, and Ross Girshick

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:14:09.519487Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 1cb72091-8778-4a2e-8755-69a9c418bb12 · outbound

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

Segment Anything for Histopathology A dataset and a technique for generalized nuclear segmentation for computational pathology

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-09T19:14:07.993491Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f3999808-4c10-46fd-ac12-19b59d07ebe0 · outbound

This paper cites Cryonuseg: A dataset for nuclei instance segmentation of cryosectioned h&e-stained histological images.

Segment Anything for Histopathology Cryonuseg: A dataset for nuclei instance segmentation of cryosectioned h&e-stained histological images

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-09T19:14:07.996453Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 266e2f24-cd17-4bae-bc6b-1a6ca0d4e18a · outbound

This paper cites Nuinsseg: A fully annotated dataset for nuclei instance segmentation in h&e-stained histological images.

Segment Anything for Histopathology Nuinsseg: A fully annotated dataset for nuclei instance segmentation in h&e-stained histological images

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-09T19:14:08.000051Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 68e1f355-98d9-4858-a495-e70ce9a8003d · outbound

This paper cites Holy-net: Segmentation of histological images of diffuse large b-cell lymphoma.

Segment Anything for Histopathology Holy-net: Segmentation of histological images of diffuse large b-cell lymphoma

Reference 21

Resolution: metadata mismatch; raw_fallback, observed 2026-08-09T19:14:08.902012Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation d2e42245-4cff-47fb-97dd-df953f2ab192 · outbound

This paper cites Segmentation of nuclei in histopathology images by deep regression of the distance map.

Segment Anything for Histopathology Segmentation of nuclei in histopathology images by deep regression of the distance map

Reference 22

Resolution: metadata mismatch; raw_fallback, observed 2026-08-09T19:14:08.769388Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation e16e1533-9521-4d62-bf11-e6588f0c7391 · outbound

This paper cites SAM 2: Segment anything in images and videos.

Segment Anything for Histopathology SAM 2: Segment anything in images and videos

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-09T19:14:08.011305Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0cdee58c-0d97-4007-a0fe-81763438beb8 · outbound

This paper cites U-Net: Convolutional Networks for Biomedical Image Segmentation, page 234–241.

Segment Anything for Histopathology U-Net: Convolutional Networks for Biomedical Image Segmentation, page 234–241

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-09T19:14:08.014791Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b67bfcbb-697e-4604-941e-c3d8573d60d7 · outbound

This paper cites A novel dataset for nuclei and tissue segmentation in melanoma with baseline nuclei segmentation and tissue segmentation benchmarks.

Segment Anything for Histopathology A novel dataset for nuclei and tissue segmentation in melanoma with baseline nuclei segmentation and tissue segmentation benchmarks

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-09T19:14:08.018778Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation facd113e-91db-490b-90d5-2dbe19481f91 · outbound

This paper cites Pluim, Hao Chen, Xiaojuan Qi, Pheng-Ann Heng, Yun Bo Guo, Li Yang Wang, Bogdan J.

Segment Anything for Histopathology Pluim, Hao Chen, Xiaojuan Qi, Pheng-Ann Heng, Yun Bo Guo, Li Yang Wang, Bogdan J

Reference 26

Resolution: verified exact; doi, observed 2026-08-09T19:14:08.161529Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 3eafa649-26d1-4bd3-a1ea-fdf018702527 · outbound

This paper cites an unresolved cited work.

Segment Anything for Histopathology Unresolved cited work

Reference 27

Resolution: verified exact; doi, observed 2026-08-09T19:14:09.501785Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 7e4e6ce2-30b5-4062-8ba8-749c0ececd92 · outbound

This paper cites Training deep learning models for cell image segmentation with sparse annotations.

Segment Anything for Histopathology Training deep learning models for cell image segmentation with sparse annotations

Reference 28

Resolution: verified exact; doi, observed 2026-08-09T19:14:08.149437Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 18a0f3da-9c92-416f-80dd-e3cf4d0727c8 · outbound

This paper cites hover-unet.

Segment Anything for Histopathology hover-unet

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-09T19:14:08.035143Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e21974cc-f493-4f72-add0-7d4be36c8dc6 · outbound

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

Segment Anything for Histopathology Deep learning in histopathology: the path to the clinic

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-09T19:14:08.038464Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a62f3f6b-b566-4aa7-8f36-e7e3a2e76115 · outbound

This paper cites Ahmed Raza, Nasir Rajpoot, Xiyi Wu, Huai Chen, Yijie Huang, Lisheng Wang, Hyun Jung, G.

Segment Anything for Histopathology Ahmed Raza, Nasir Rajpoot, Xiyi Wu, Huai Chen, Yijie Huang, Lisheng Wang, Hyun Jung, G

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-09T19:14:08.042113Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7c26709f-4578-49b3-97ec-1d34f089774c · outbound

This paper cites Methods for segmentation and classification of digital microscopy tissue images.

Segment Anything for Histopathology Methods for segmentation and classification of digital microscopy tissue images

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-09T19:14:08.045612Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 08361981-af56-4856-9f9c-09d0b10d30d4 · outbound

This paper cites Simultaneously segmenting and classifying cell nuclei by using multi-task learning in multiplex immunohistochemical tissue microarray sections.

Segment Anything for Histopathology Simultaneously segmenting and classifying cell nuclei by using multi-task learning in multiplex immunohistochemical tissue microarray sections

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-09T19:14:08.049835Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2297025e-2118-4219-80a4-4b00c5b382e2 · outbound

This paper cites Nuclei instance segmentation and classification in histopathology images with stardist.

Segment Anything for Histopathology Nuclei instance segmentation and classification in histopathology images with stardist

Reference 34

Resolution: verified exact; raw_fallback, observed 2026-08-09T19:14:08.384124Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation d8ac8231-3bb9-430d-8e45-616a6ac8cfad · outbound

This paper cites On generalisability of segment anything model for nuclear instance segmentation in histology images.

Segment Anything for Histopathology On generalisability of segment anything model for nuclear instance segmentation in histology images

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-09T19:14:08.057670Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5699b7f8-802a-4aa2-a910-0d89f3ff138e · outbound

This paper cites Surgicalsam: Efficient class promptable surgical instrument segmentation.

Segment Anything for Histopathology Surgicalsam: Efficient class promptable surgical instrument segmentation

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-09T19:14:08.061867Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a0e08ce9-229b-4b8d-9084-55ab9db19a56 · outbound

This paper cites SAM-Path: A Segment Anything Model for Semantic Segmentation in Digital Pathology, page 161–170.

Segment Anything for Histopathology SAM-Path: A Segment Anything Model for Semantic Segmentation in Digital Pathology, page 161–170

Reference 37

Resolution: verified exact; doi, observed 2026-08-09T19:14:08.119992Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 888dcac8-82ae-43ff-889e-cf1c6e99c21d · outbound

This paper cites Glandsam: Injecting morphology knowledge into segment anything model for label-free gland segmentation.

Segment Anything for Histopathology Glandsam: Injecting morphology knowledge into segment anything model for label-free gland segmentation

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-09T19:14:08.070842Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5129e0b3-634c-4ee4-9a3a-da8bd9c65cd2 · outbound

This paper cites A foundation model for joint segmentation, detection and recognition of biomedical objects across nine modalities.

Segment Anything for Histopathology A foundation model for joint segmentation, detection and recognition of biomedical objects across nine modalities

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-09T19:14:08.074190Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3371b933-b784-492f-a9ce-be30d1a9b770 · outbound

This paper cites Segment everything everywhere all at once.

Segment Anything for Histopathology Segment everything everywhere all at once

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:14:09.488066Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Pith citing papers

Observation 93588d6e-b299-41aa-9e89-2699ccef80cb · inbound

Evaluating Vision Foundation Models for Pixel and Object Classification in Microscopy cites this paper.

Evaluating Vision Foundation Models for Pixel and Object Classification in Microscopy Segment Anything for Histopathology

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-02T17:52:43.350078Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 616b45f7-bac8-4acb-9c04-1f969bac0034 · inbound

$\mu$Match: Foundation Models for Semi-supervised Learning and Domain Adaptation in EM cites this paper.

$\mu$Match: Foundation Models for Semi-supervised Learning and Domain Adaptation in EM Segment Anything for Histopathology

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-07-04T06:09:37.630947Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 989161bc-e31f-48e6-9d78-5a651373cb39 · inbound

Towards Autonomous and Auditable Medical Imaging Model Development cites this paper.

Towards Autonomous and Auditable Medical Imaging Model Development Segment Anything for Histopathology

Reference 13

Resolution: unresolved; no resolver link, observed 2026-07-14T11:05:51.130839Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.