Paper Citation Record · LEDGER

Simple is what you need for efficient and accurate medical image segmentation

As of 21 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 0 inbound Pith citation observations for arXiv:2506.13415.

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

pith.paper-citation-record.v1
2506.13415 v1

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:08:15.961712Z

measured 39 of 39 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+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

39 of 39 outbound references displayed

verified exact1
verified fuzzy21
unresolved17
parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 96ee8f6f-796e-48ab-b069-1cbb8a48e0a0 · outbound

This paper cites U-net: Convolutional networks for biomedical image segmentation,.

Simple is what you need for efficient and accurate medical image segmentation U-net: Convolutional networks for biomedical image segmentation,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-15T20:08:15.729300Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 17b4c6bd-af15-4b82-955b-39f4c594bdb9 · outbound

This paper cites Mambasam: A visual mamba-adapted sam frame- work for medical image segmentation,.

Simple is what you need for efficient and accurate medical image segmentation Mambasam: A visual mamba-adapted sam frame- work for medical image segmentation,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:08:17.130590Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation df0366dd-2aa2-4dda-9ecb-73166f9d8240 · outbound

This paper cites Mambasam: A visual mamba-adapted sam framework for med- ical image segmentation,.

Simple is what you need for efficient and accurate medical image segmentation Mambasam: A visual mamba-adapted sam framework for med- ical image segmentation,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:08:17.114674Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e92b711f-6db6-4b5b-878b-91cf25aba418 · outbound

This paper cites Thyfusion: A lightweight attribute enhancement module for thyroid nodule diagnosis using gradient and frequency-domain awareness,.

Simple is what you need for efficient and accurate medical image segmentation Thyfusion: A lightweight attribute enhancement module for thyroid nodule diagnosis using gradient and frequency-domain awareness,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:08:17.097704Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 8c7434c1-c9c0-4440-91b7-dfc2b18d5e1f · outbound

This paper cites TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation.

Simple is what you need for efficient and accurate medical image segmentation TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-15T20:08:15.765632Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ad790d58-58eb-4818-8945-bec8e0567883 · outbound

This paper cites Esknet: An enhanced adaptive selection kernel convolution for ultrasound breast tumors segmentation,.

Simple is what you need for efficient and accurate medical image segmentation Esknet: An enhanced adaptive selection kernel convolution for ultrasound breast tumors segmentation,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:08:17.079279Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 27e068c9-80ff-43fb-af5f-23f1954484bb · outbound

This paper cites Ukan: Unbound kolmogorov-arnold network accompanied with accelerated library,.

Simple is what you need for efficient and accurate medical image segmentation Ukan: Unbound kolmogorov-arnold network accompanied with accelerated library,

Reference 7

Resolution: verified exact; raw_fallback, observed 2026-08-15T20:08:16.385676Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 192661b0-627f-42b2-a7b8-b5292d26c3ff · outbound

This paper cites Mlmseg: a multi-view learning model for ultrasound thyroid nodule segmentation,.

Simple is what you need for efficient and accurate medical image segmentation Mlmseg: a multi-view learning model for ultrasound thyroid nodule segmentation,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:08:17.058838Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 2d174dc6-27a2-49a6-8279-dd54f8263f1a · outbound

This paper cites Mobileunet- fpn: A semantic segmentation model for fetal ultrasound four-chamber segmentation in edge computing environments,.

Simple is what you need for efficient and accurate medical image segmentation Mobileunet- fpn: A semantic segmentation model for fetal ultrasound four-chamber segmentation in edge computing environments,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:08:17.040716Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e41eecbb-bf13-452f-a4ba-10e7868badd7 · outbound

This paper cites Xception: Deep learning with depthwise separable convolu- tions,.

Simple is what you need for efficient and accurate medical image segmentation Xception: Deep learning with depthwise separable convolu- tions,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:08:17.021792Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 0c5c482a-24b5-472d-b17d-e3a9d7331c6b · outbound

This paper cites VM-UNet: Vision Mamba UNet for Medical Image Segmentation.

Simple is what you need for efficient and accurate medical image segmentation VM-UNet: Vision Mamba UNet for Medical Image Segmentation

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-15T20:08:15.800197Z

Source-reported events for the cited work

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Observation 795aef1e-4ad4-4e4f-98fe-0924e75d5170 · outbound

This paper cites Lb-unet: A lightweight boundary-assisted unet for skin lesion segmentation,.

Simple is what you need for efficient and accurate medical image segmentation Lb-unet: A lightweight boundary-assisted unet for skin lesion segmentation,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:08:17.001015Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 5857f413-cf8d-4109-9f54-839cf47eed78 · outbound

This paper cites UltraLight VM-UNet: Parallel Vision Mamba Significantly Reduces Parameters for Skin Lesion Segmentation.

Simple is what you need for efficient and accurate medical image segmentation UltraLight VM-UNet: Parallel Vision Mamba Significantly Reduces Parameters for Skin Lesion Segmentation

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-15T20:08:15.811174Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 83fbe5d6-358a-4613-ae64-a40ec0d2f6e6 · outbound

This paper cites ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation.

Simple is what you need for efficient and accurate medical image segmentation ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-15T20:08:15.816584Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4c52a0c3-5809-4fc6-9aec-163e17c459af · outbound

This paper cites Linknet: Exploiting encoder repre- sentations for efficient semantic segmentation,.

Simple is what you need for efficient and accurate medical image segmentation Linknet: Exploiting encoder repre- sentations for efficient semantic segmentation,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:08:16.978116Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 39371dc3-d780-4235-b262-f3efe3bbb9a3 · outbound

This paper cites Unext: Mlp-based rapid medical image segmentation network,.

Simple is what you need for efficient and accurate medical image segmentation Unext: Mlp-based rapid medical image segmentation network,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:08:16.955505Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation af3b7da3-4af9-4a64-bbb8-1622638a6958 · outbound

This paper cites Lfu-net: a lightweight u-net with full skip connections for medical image segmen- tation,.

Simple is what you need for efficient and accurate medical image segmentation Lfu-net: a lightweight u-net with full skip connections for medical image segmen- tation,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:08:16.934731Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 635f0142-7571-4672-97ac-09cbc48eadbd · outbound

This paper cites an unresolved cited work.

Simple is what you need for efficient and accurate medical image segmentation Unresolved cited work

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-15T20:08:15.735811Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c5444997-97cf-45fc-937b-6caa7731c6cc · outbound

This paper cites Malunet: A multi-attention and light-weight unet for skin lesion segmentation,.

Simple is what you need for efficient and accurate medical image segmentation Malunet: A multi-attention and light-weight unet for skin lesion segmentation,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:08:16.915962Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation ff8d68ca-c23d-4b00-933f-cddcd5aca767 · outbound

This paper cites Mamba: Linear-Time Sequence Modeling with Selective State Spaces.

Simple is what you need for efficient and accurate medical image segmentation Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-15T20:08:15.851330Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c1e56018-418a-4d4c-9d6d-e0a7f9179bba · outbound

This paper cites Cbam: Convolutional block attention module,.

Simple is what you need for efficient and accurate medical image segmentation Cbam: Convolutional block attention module,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-15T20:08:15.856985Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 93cd10aa-365e-4886-aef5-5e433b632730 · outbound

This paper cites Attention U-Net: Learning Where to Look for the Pancreas.

Simple is what you need for efficient and accurate medical image segmentation Attention U-Net: Learning Where to Look for the Pancreas

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-15T20:08:15.862737Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 66a0b890-25fc-4a0b-b335-0058f2b44aad · outbound

This paper cites Dau- net: Dual attention-aided u-net for segmenting tumor in breast ultrasound images,.

Simple is what you need for efficient and accurate medical image segmentation Dau- net: Dual attention-aided u-net for segmenting tumor in breast ultrasound images,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:08:16.884194Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 2dece12f-dd75-4f9a-8fe5-b197facdc546 · outbound

This paper cites Mda-net: Multiscale dual attention-based network for breast lesion segmentation using ultrasound images,.

Simple is what you need for efficient and accurate medical image segmentation Mda-net: Multiscale dual attention-based network for breast lesion segmentation using ultrasound images,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:08:16.848993Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 1d51ddc3-7b08-4b3a-ae7d-67f315aed2c0 · outbound

This paper cites Unet++: A nested u-net architecture for medical image segmenta- tion,.

Simple is what you need for efficient and accurate medical image segmentation Unet++: A nested u-net architecture for medical image segmenta- tion,

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-15T20:08:15.880960Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 373602da-d39d-4dd0-9d20-28deb4e8047a · outbound

This paper cites Mf-net: Multiple-feature extraction network for breast lesion segmentation in ultrasound images,.

Simple is what you need for efficient and accurate medical image segmentation Mf-net: Multiple-feature extraction network for breast lesion segmentation in ultrasound images,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:08:16.802060Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 9e555775-30c8-4866-9abf-96091ee573bd · outbound

This paper cites Unet 3+: A full-scale connected unet for medical image segmentation,.

Simple is what you need for efficient and accurate medical image segmentation Unet 3+: A full-scale connected unet for medical image segmentation,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:08:16.777727Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation a757c1fb-5b73-4167-a106-50f36f9e33a6 · outbound

This paper cites Tinyu-net: Lighter yet better u-net with cascaded multi-receptive fields,.

Simple is what you need for efficient and accurate medical image segmentation Tinyu-net: Lighter yet better u-net with cascaded multi-receptive fields,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:08:16.756381Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation ea75d96b-1935-420f-84e3-e12c5545e1bd · outbound

This paper cites Dataset of breast ultrasound images.

Simple is what you need for efficient and accurate medical image segmentation Dataset of breast ultrasound images

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:08:16.735682Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation b85000a7-8bcb-4022-bb6b-ca78c8beb541 · outbound

This paper cites Bus-set: A benchmark for quantitative evaluation of breast ultrasound segmentation networks with public datasets,.

Simple is what you need for efficient and accurate medical image segmentation Bus-set: A benchmark for quantitative evaluation of breast ultrasound segmentation networks with public datasets,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:08:16.712454Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 79e4f262-878a-46ac-98fd-137b84e353bf · outbound

This paper cites Curated benchmark dataset for ultrasound based breast lesion analysis,.

Simple is what you need for efficient and accurate medical image segmentation Curated benchmark dataset for ultrasound based breast lesion analysis,

Reference 31

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 191358e6-b067-49f7-92dd-e64ba48f0887 · outbound

This paper cites Bus-bra: A breast ultrasound dataset for assessing computer-aided diagnosis systems,.

Simple is what you need for efficient and accurate medical image segmentation Bus-bra: A breast ultrasound dataset for assessing computer-aided diagnosis systems,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:08:16.662813Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 9eae7969-6623-496d-b23c-cecf19d652db · outbound

This paper cites ISIC 2017 - Skin Lesion Analysis Towards Melanoma Detection.

Simple is what you need for efficient and accurate medical image segmentation ISIC 2017 - Skin Lesion Analysis Towards Melanoma Detection

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-15T20:08:15.928744Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1468e9c8-4ca3-4bf2-84bb-cfdf2b23bf30 · outbound

This paper cites ISIC 2018-A Method for Lesion Segmentation.

Simple is what you need for efficient and accurate medical image segmentation ISIC 2018-A Method for Lesion Segmentation

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-15T20:08:15.934851Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 07301cdb-d757-4b6d-bf35-9791684f777f · outbound

This paper cites Kvasir-seg: A segmented polyp dataset,.

Simple is what you need for efficient and accurate medical image segmentation Kvasir-seg: A segmented polyp dataset,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:08:16.476591Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 53c8c4da-be4f-4a12-bb3e-a42c1ad33a33 · outbound

This paper cites an unresolved cited work.

Simple is what you need for efficient and accurate medical image segmentation Unresolved cited work

Reference 36

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:08:16.451894Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 08eb7dcc-be48-4e70-8c87-b82a801e649f · outbound

This paper cites Decoupled Weight Decay Regularization.

Simple is what you need for efficient and accurate medical image segmentation Decoupled Weight Decay Regularization

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-15T20:08:15.950932Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8f2564f6-49c0-4132-ad45-05486c5d96e1 · outbound

This paper cites SGDR: Stochastic Gradient Descent with Warm Restarts.

Simple is what you need for efficient and accurate medical image segmentation SGDR: Stochastic Gradient Descent with Warm Restarts

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-15T20:08:15.956285Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d7f9d4ba-eed5-49a7-9f82-8aec86106b8b · outbound

This paper cites U-Net v2: Rethinking the Skip Connections of U-Net for Medical Image Segmentation.

Simple is what you need for efficient and accurate medical image segmentation U-Net v2: Rethinking the Skip Connections of U-Net for Medical Image Segmentation

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-15T20:08:15.961712Z

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Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.