Paper Citation Record · LEDGER

Efficient Knowledge Distillation of SAM for Medical Image Segmentation

As of 13 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 1 inbound Pith citation observation for arXiv:2501.16740.

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

pith.paper-citation-record.v1
2501.16740 v1

Coverage vector

measured 26 of 26 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T11:06:41.554107Z

measured 27 of 27 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T11:06:41.334573Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-10T11:06:41.692893Z

Reference resolution

26 of 26 outbound references displayed

verified exact0
verified fuzzy14
unresolved11
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malformed identifier0
metadata mismatch1

External citation measurements

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Outbound references

Observation 378f6567-7b31-41b0-b150-bc0996f695d0 · outbound

This paper cites The Segment Any- thing Model (SAM) [1] has established itself as a powerful tool in this domain, leveraging a Vision Transformer (ViT).

Efficient Knowledge Distillation of SAM for Medical Image Segmentation The Segment Any- thing Model (SAM) [1] has established itself as a powerful tool in this domain, leveraging a Vision Transformer (ViT)

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T11:06:41.990083Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation b1f1689a-7ce1-4053-9b34-09d6aec079f0 · outbound

This paper cites However, the significant computational demands of SAM hinder its deployment in real-time and resource-constrained environ- ments, such as mobile devices and edge platforms.

Efficient Knowledge Distillation of SAM for Medical Image Segmentation However, the significant computational demands of SAM hinder its deployment in real-time and resource-constrained environ- ments, such as mobile devices and edge platforms

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T11:06:41.974167Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 662c822c-66cf-43f2-bb18-9f2d6113f2a3 · outbound

This paper cites Initially de- veloped for classification tasks [5], it has been adapted for dense prediction tasks such as semantic segmentation [6] and object detection [7].

Efficient Knowledge Distillation of SAM for Medical Image Segmentation Initially de- veloped for classification tasks [5], it has been adapted for dense prediction tasks such as semantic segmentation [6] and object detection [7]

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T11:06:41.957923Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 8ec38d85-5356-426d-abea-8a1e2d77517c · outbound

This paper cites an unresolved cited work.

Efficient Knowledge Distillation of SAM for Medical Image Segmentation Unresolved cited work

Reference 4

Resolution: unresolved; raw_fallback, observed 2026-08-10T11:06:41.909613Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation e62832e6-074f-4e83-8ef8-b83ce3db61fb · outbound

This paper cites This approach addresses the computational limitations of SAM’s Vision Transformer (ViT) encoder by distilling its knowledge into a lightweight ResNet [12] based encoder.

Efficient Knowledge Distillation of SAM for Medical Image Segmentation This approach addresses the computational limitations of SAM’s Vision Transformer (ViT) encoder by distilling its knowledge into a lightweight ResNet [12] based encoder

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T11:06:41.942292Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 8c6c2d13-3c78-4ca3-a622-1b94c5e24277 · outbound

This paper cites Structured knowledge distil- lation for semantic segmentation,.

Efficient Knowledge Distillation of SAM for Medical Image Segmentation Structured knowledge distil- lation for semantic segmentation,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T11:06:41.839707Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 9c651c25-4260-43df-982c-687548bf25dd · outbound

This paper cites Learning efficient object de- tection models with knowledge distillation,.

Efficient Knowledge Distillation of SAM for Medical Image Segmentation Learning efficient object de- tection models with knowledge distillation,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T11:06:41.824662Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 89c4d4f7-09c6-4148-9a2c-f1bbd9711632 · outbound

This paper cites As shown in Table 1, the results demonstrate that KD SAM achieves comparable or superior performance to the base- line models across most datasets.

Efficient Knowledge Distillation of SAM for Medical Image Segmentation As shown in Table 1, the results demonstrate that KD SAM achieves comparable or superior performance to the base- line models across most datasets

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T11:06:41.893829Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation eaaeac91-ef7a-4093-879f-4f1ee726f061 · outbound

This paper cites Efficient Knowledge Distillation of SAM for Medical Image Segmentation.

Efficient Knowledge Distillation of SAM for Medical Image Segmentation Efficient Knowledge Distillation of SAM for Medical Image Segmentation

Reference 9

Resolution: metadata mismatch; local_arxiv, observed 2026-08-10T11:06:41.700325Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation cc248c98-eab7-4ed8-8464-b16d318d247e · outbound

This paper cites Segment anything,.

Efficient Knowledge Distillation of SAM for Medical Image Segmentation Segment anything,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-10T11:06:41.470886Z

Source-reported events for the cited work

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Observation 31e5ac34-a631-4f8d-9dce-56376c9dbae5 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Efficient Knowledge Distillation of SAM for Medical Image Segmentation An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-10T11:06:41.476435Z

Source-reported events for the cited work

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Observation 2985e752-4691-4909-ac89-41027cf7bdd0 · outbound

This paper cites Faster Segment Anything: Towards Lightweight SAM for Mobile Applications.

Efficient Knowledge Distillation of SAM for Medical Image Segmentation Faster Segment Anything: Towards Lightweight SAM for Mobile Applications

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-10T11:06:41.481914Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cfa31bbf-5665-49f7-ae7d-a31582d27a38 · outbound

This paper cites an unresolved cited work.

Efficient Knowledge Distillation of SAM for Medical Image Segmentation Unresolved cited work

Reference 13

Resolution: unresolved; raw_fallback, observed 2026-08-10T11:06:41.926645Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 5cc14a7c-b232-41fb-9f7d-ab77638531b6 · outbound

This paper cites Per- ceptual losses for real-time style transfer and super- resolution,.

Efficient Knowledge Distillation of SAM for Medical Image Segmentation Per- ceptual losses for real-time style transfer and super- resolution,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-10T11:06:41.487804Z

Source-reported events for the cited work

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Observation 6d1fb369-ae49-4fa9-a61c-02a850493505 · outbound

This paper cites Differ- entiable feature aggregation search for knowledge dis- tillation,.

Efficient Knowledge Distillation of SAM for Medical Image Segmentation Differ- entiable feature aggregation search for knowledge dis- tillation,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T11:06:41.854908Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 0abc2230-d381-40ed-aae4-6f75944ef568 · outbound

This paper cites Fast Segment Anything.

Efficient Knowledge Distillation of SAM for Medical Image Segmentation Fast Segment Anything

Reference 16

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Source-reported events for the cited work

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Observation 857bd50b-fad6-4c3f-be3f-264f2d8d0e36 · outbound

This paper cites Yolact: Real-time instance segmentation,.

Efficient Knowledge Distillation of SAM for Medical Image Segmentation Yolact: Real-time instance segmentation,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T11:06:41.809928Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 7a56c7b4-0ff7-429b-ac2f-54f9394b08d4 · outbound

This paper cites Efficientsam: Leveraged masked image pretraining for efficient seg- ment anything,.

Efficient Knowledge Distillation of SAM for Medical Image Segmentation Efficientsam: Leveraged masked image pretraining for efficient seg- ment anything,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T11:06:41.794053Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation b7716ba7-a39b-4c3b-9d96-490d93c2f041 · outbound

This paper cites EdgeSAM: Prompt-In-the-Loop Distillation for SAM.

Efficient Knowledge Distillation of SAM for Medical Image Segmentation EdgeSAM: Prompt-In-the-Loop Distillation for SAM

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-10T11:06:41.519364Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4a4e934d-401b-47de-b229-dcbbf5f48999 · outbound

This paper cites Deep residual learning for image recognition,.

Efficient Knowledge Distillation of SAM for Medical Image Segmentation Deep residual learning for image recognition,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-10T11:06:41.524522Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d30640c4-0a67-42a3-b228-275c09087660 · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

Efficient Knowledge Distillation of SAM for Medical Image Segmentation Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-10T11:06:41.529581Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a1c6f9b3-1b53-435f-8743-49cbe18539b6 · outbound

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

Efficient Knowledge Distillation of SAM for Medical Image Segmentation Kvasir-seg: A segmented polyp dataset,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T11:06:41.766366Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation eacd315b-7911-44ac-a989-e8c9afec2374 · outbound

This paper cites Analysis of the isic image datasets: Usage, benchmarks and recommendations,.

Efficient Knowledge Distillation of SAM for Medical Image Segmentation Analysis of the isic image datasets: Usage, benchmarks and recommendations,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T11:06:41.749683Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 763691fa-f6fb-4512-b578-31453a2ca9d8 · outbound

This paper cites Automated mea- surement of fetal head circumference using 2d ultra- sound images,.

Efficient Knowledge Distillation of SAM for Medical Image Segmentation Automated mea- surement of fetal head circumference using 2d ultra- sound images,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T11:06:41.734362Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 2a85c310-1557-46ad-9e55-ba137f9c54ca · outbound

This paper cites Dataset of breast ultrasound images,.

Efficient Knowledge Distillation of SAM for Medical Image Segmentation Dataset of breast ultrasound images,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T11:06:41.718236Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation cec5f871-49d7-4f46-88f3-bf56c8d2a9fc · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Efficient Knowledge Distillation of SAM for Medical Image Segmentation Adam: A Method for Stochastic Optimization

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-10T11:06:41.554107Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation eaaeac91-ef7a-4093-879f-4f1ee726f061 · inbound