Paper Citation Record · LEDGER

Semantic Segmentation of iPS Cells: Case Study on Model Complexity in Biomedical Imaging

As of 14 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 0 inbound Pith citation observations for arXiv:2507.21608.

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

pith.paper-citation-record.v1
2507.21608 v1

Coverage vector

measured 22 of 22 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T12:37:58.713765Z

measured 22 of 22 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

22 of 22 outbound references displayed

verified exact1
verified fuzzy18
unresolved3
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f31bcb35-eb03-48b8-ad6a-29e7aed06eb2 · outbound

This paper cites Automated human induced pluripotent stem cell colony segmen- tation for use in cell culture automation applications,.

Semantic Segmentation of iPS Cells: Case Study on Model Complexity in Biomedical Imaging Automated human induced pluripotent stem cell colony segmen- tation for use in cell culture automation applications,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:38:00.547859Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 75df4b33-6a12-41b9-bf1d-b7bc0a9659b2 · outbound

This paper cites TransUNet: Re- thinking the U-Net architecture design for medical im- age segmentation through the lens of transformers,.

Semantic Segmentation of iPS Cells: Case Study on Model Complexity in Biomedical Imaging TransUNet: Re- thinking the U-Net architecture design for medical im- age segmentation through the lens of transformers,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:38:00.461208Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f4fb7084-6e90-4daf-a737-7cb824b78d66 · outbound

This paper cites nnu-net revisited: A call for rigorous validation in 3d medical image seg- mentation,.

Semantic Segmentation of iPS Cells: Case Study on Model Complexity in Biomedical Imaging nnu-net revisited: A call for rigorous validation in 3d medical image seg- mentation,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:38:00.280219Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 3100a107-0413-4c27-b4a8-cd127ebc48f8 · outbound

This paper cites nnU-Net: a self-configuring method for deep learning-based biomedical image seg- mentation,.

Semantic Segmentation of iPS Cells: Case Study on Model Complexity in Biomedical Imaging nnU-Net: a self-configuring method for deep learning-based biomedical image seg- mentation,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:38:00.190677Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation b488525c-d7d3-4f16-b297-35bc76860cd2 · outbound

This paper cites Improving the reliability of semantic segmentation of medical images by uncertainty modeling with Bayesian deep networks and curriculum learning,.

Semantic Segmentation of iPS Cells: Case Study on Model Complexity in Biomedical Imaging Improving the reliability of semantic segmentation of medical images by uncertainty modeling with Bayesian deep networks and curriculum learning,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:38:00.083491Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 7d1d887a-8a16-446b-a86b-861911785b9b · outbound

This paper cites Segment anything,.

Semantic Segmentation of iPS Cells: Case Study on Model Complexity in Biomedical Imaging Segment anything,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:37:59.991888Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 51a74605-5697-46bb-b4a5-2c15fd7d57f0 · outbound

This paper cites SAM 2: Segment Anything in Images and Videos.

Semantic Segmentation of iPS Cells: Case Study on Model Complexity in Biomedical Imaging SAM 2: Segment Anything in Images and Videos

Reference 7

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Source-reported events for the cited work

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Observation 9128bce5-4bd2-4c52-bd16-f8dea789eac5 · outbound

This paper cites Segment anything in medical images,.

Semantic Segmentation of iPS Cells: Case Study on Model Complexity in Biomedical Imaging Segment anything in medical images,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:37:59.918342Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 9dc61416-9a7b-4894-976a-83a48e6e096f · outbound

This paper cites Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation,.

Semantic Segmentation of iPS Cells: Case Study on Model Complexity in Biomedical Imaging Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:37:59.849792Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a0f54efe-0e60-430f-8534-404aeb2ccbb1 · outbound

This paper cites Biomedical SAM 2: Segment Anything in Biomedical Images and Videos.

Semantic Segmentation of iPS Cells: Case Study on Model Complexity in Biomedical Imaging Biomedical SAM 2: Segment Anything in Biomedical Images and Videos

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T12:37:58.081259Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3c118467-08e8-4eb8-85dc-43ead5f78f8b · outbound

This paper cites Deeplab: Semantic image segmenta- tion with deep convolutional nets, atrous convolution, and fully connected crfs,.

Semantic Segmentation of iPS Cells: Case Study on Model Complexity in Biomedical Imaging Deeplab: Semantic image segmenta- tion with deep convolutional nets, atrous convolution, and fully connected crfs,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:37:59.769631Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 6b5177be-dfc1-4eb0-8867-5abffee8ec6b · outbound

This paper cites Train Neural Net for Semantic Seg- mentation with PyTorch in 50 Lines of Code,.

Semantic Segmentation of iPS Cells: Case Study on Model Complexity in Biomedical Imaging Train Neural Net for Semantic Seg- mentation with PyTorch in 50 Lines of Code,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:37:59.685161Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 1b78675a-3b2d-480d-ac21-57a771d906fc · outbound

This paper cites Video ob- ject segmentation using space-time memory networks,.

Semantic Segmentation of iPS Cells: Case Study on Model Complexity in Biomedical Imaging Video ob- ject segmentation using space-time memory networks,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:37:59.633983Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation e1ec017b-6ed9-4aaf-957a-694c13732854 · outbound

This paper cites Segmentation of Carotid Plaques Based on Improved DeepLabV3,.

Semantic Segmentation of iPS Cells: Case Study on Model Complexity in Biomedical Imaging Segmentation of Carotid Plaques Based on Improved DeepLabV3,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:37:59.551447Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 28e38cec-142e-4f6a-9f40-4dfeebf825fd · outbound

This paper cites Towards Segment Anything Model (SAM) for Medical Image Segmentation: A Survey.

Semantic Segmentation of iPS Cells: Case Study on Model Complexity in Biomedical Imaging Towards Segment Anything Model (SAM) for Medical Image Segmentation: A Survey

Reference 15

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Source-reported events for the cited work

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Observation d4ddca68-f705-4ef6-880e-a40ab8485138 · outbound

This paper cites Adapting segment anything models to medical imaging via fine-tuning without do- main pretraining,.

Semantic Segmentation of iPS Cells: Case Study on Model Complexity in Biomedical Imaging Adapting segment anything models to medical imaging via fine-tuning without do- main pretraining,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:37:59.468141Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 2488df5b-aa0d-412f-843d-38df337d2079 · outbound

This paper cites DeepLabV3 and Medical Imaging,.

Semantic Segmentation of iPS Cells: Case Study on Model Complexity in Biomedical Imaging DeepLabV3 and Medical Imaging,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:37:59.349410Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation dd9fdced-3008-45b3-bbd3-360bac8ac0c5 · outbound

This paper cites On the computational complexity of self-attention,.

Semantic Segmentation of iPS Cells: Case Study on Model Complexity in Biomedical Imaging On the computational complexity of self-attention,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:37:59.255878Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 5b828f1f-f49d-44d0-bddd-56527ee51a9e · outbound

This paper cites Domain Adaptation Guide,.

Semantic Segmentation of iPS Cells: Case Study on Model Complexity in Biomedical Imaging Domain Adaptation Guide,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:37:59.175534Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 73f4fc37-714f-4452-914f-6a4d11be79af · outbound

This paper cites SAMCL: Empowering SAM to Continually Learn from Dynamic Domains,.

Semantic Segmentation of iPS Cells: Case Study on Model Complexity in Biomedical Imaging SAMCL: Empowering SAM to Continually Learn from Dynamic Domains,

Reference 20

Resolution: verified exact; raw_fallback, observed 2026-08-06T12:37:58.881238Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation fa6bcdb1-b813-4c58-8807-761534652cad · outbound

This paper cites Ad- vancing medical imaging informatics by deep learning- based domain adaptation,.

Semantic Segmentation of iPS Cells: Case Study on Model Complexity in Biomedical Imaging Ad- vancing medical imaging informatics by deep learning- based domain adaptation,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:37:59.073670Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation fd476f0a-8201-4c05-9fab-c5eac8ea7861 · outbound

This paper cites iPS-Semantic-Segmentation,.

Semantic Segmentation of iPS Cells: Case Study on Model Complexity in Biomedical Imaging iPS-Semantic-Segmentation,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:37:58.996772Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Pith citing papers

No inbound Pith citation observations are available.