Paper Citation Record · LEDGER

Grounding DINO-US-SAM: Text-Prompted Multi-Organ Segmentation in Ultrasound with LoRA-Tuned Vision-Language Models

As of 19 August 2026, this Paper Citation Record lists 60 of 60 outbound references and 1 inbound Pith citation observation for arXiv:2506.23903.

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

pith.paper-citation-record.v1
2506.23903 v3

Coverage vector

measured 60 of 60 reference resolution

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Pith citing papers itemized under the disclosed page cap.

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Source: arxiv_reference, observed 2026-05-18T18:56:45.936071Z

Reference resolution

60 of 60 outbound references displayed

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External citation measurements

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Outbound references

Observation f036440d-a463-4072-afbf-7aca5a743983 · outbound

This paper cites Fine-tuning u-net for ultrasound image segmentation: different layers, different outcomes,.

Grounding DINO-US-SAM: Text-Prompted Multi-Organ Segmentation in Ultrasound with LoRA-Tuned Vision-Language Models Fine-tuning u-net for ultrasound image segmentation: different layers, different outcomes,

Reference 1

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Observation 79405cd8-10e4-4ead-9489-68c1c2f33a41 · outbound

This paper cites Hctnet: A hybrid cnn-transformer network for breast ultrasound image segmentation,.

Grounding DINO-US-SAM: Text-Prompted Multi-Organ Segmentation in Ultrasound with LoRA-Tuned Vision-Language Models Hctnet: A hybrid cnn-transformer network for breast ultrasound image segmentation,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:36:10.414232Z

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Observation e1c17472-5cf6-4e08-8191-40378e8d4d33 · outbound

This paper cites Mcv-unet: A modified convolution & transformer hybrid encoder-decoder network with multi-scale information fusion for ultrasound image semantic segmentation,.

Grounding DINO-US-SAM: Text-Prompted Multi-Organ Segmentation in Ultrasound with LoRA-Tuned Vision-Language Models Mcv-unet: A modified convolution & transformer hybrid encoder-decoder network with multi-scale information fusion for ultrasound image semantic segmentation,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:36:10.298911Z

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Observation dae675f8-4259-4ee2-9e6b-f016e8a4283b · outbound

This paper cites Intrapartum ultrasound image segmentation of pubic sym- physis and fetal head using dual student-teacher framework with cnn-vit collaborative learning,.

Grounding DINO-US-SAM: Text-Prompted Multi-Organ Segmentation in Ultrasound with LoRA-Tuned Vision-Language Models Intrapartum ultrasound image segmentation of pubic sym- physis and fetal head using dual student-teacher framework with cnn-vit collaborative learning,

Reference 4

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Observation 7c17b8ff-b375-406f-9c3f-f840b74e1bad · outbound

This paper cites Hybrid-structure-oriented transformer for arm musculoskeletal ultra- sound segmentation,.

Grounding DINO-US-SAM: Text-Prompted Multi-Organ Segmentation in Ultrasound with LoRA-Tuned Vision-Language Models Hybrid-structure-oriented transformer for arm musculoskeletal ultra- sound segmentation,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:36:10.099933Z

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Observation 6b7e6940-ac61-4fcd-905b-8a01c578b789 · outbound

This paper cites Microsegnet: A deep learning approach for prostate segmentation on micro-ultrasound images,.

Grounding DINO-US-SAM: Text-Prompted Multi-Organ Segmentation in Ultrasound with LoRA-Tuned Vision-Language Models Microsegnet: A deep learning approach for prostate segmentation on micro-ultrasound images,

Reference 6

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Observation c57bcfd6-40b7-4880-916f-718bfc065853 · outbound

This paper cites Lightbtseg: A lightweight breast tumor segmentation model using ultrasound images via dual-path joint knowledge distillation,.

Grounding DINO-US-SAM: Text-Prompted Multi-Organ Segmentation in Ultrasound with LoRA-Tuned Vision-Language Models Lightbtseg: A lightweight breast tumor segmentation model using ultrasound images via dual-path joint knowledge distillation,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:36:09.940990Z

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Observation 8f8beca5-181d-4239-935d-fafa9e72163c · outbound

This paper cites Aau-net: an adaptive attention u-net for breast lesions segmentation in ultrasound images,.

Grounding DINO-US-SAM: Text-Prompted Multi-Organ Segmentation in Ultrasound with LoRA-Tuned Vision-Language Models Aau-net: an adaptive attention u-net for breast lesions segmentation in ultrasound images,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:36:09.787462Z

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Observation 15b84023-aa2c-401f-bfda-9e843cd11263 · outbound

This paper cites Universeg: Universal medical image segmentation,.

Grounding DINO-US-SAM: Text-Prompted Multi-Organ Segmentation in Ultrasound with LoRA-Tuned Vision-Language Models Universeg: Universal medical image segmentation,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:36:09.597764Z

Source-reported events for the cited work

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Observation fa0ed19a-7cad-4108-ae72-7273129e35d4 · outbound

This paper cites Striving for simplicity: Simple yet effective prior-aware pseudo-labeling for semi-supervised ultrasound image segmentation,.

Grounding DINO-US-SAM: Text-Prompted Multi-Organ Segmentation in Ultrasound with LoRA-Tuned Vision-Language Models Striving for simplicity: Simple yet effective prior-aware pseudo-labeling for semi-supervised ultrasound image segmentation,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:36:09.470131Z

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Observation b57495b1-ae6a-4330-a075-1ac20bded40b · outbound

This paper cites Ul- trasound segmentation using semi-supervised learning: Application in point-of-care sarcopenia assessment,.

Grounding DINO-US-SAM: Text-Prompted Multi-Organ Segmentation in Ultrasound with LoRA-Tuned Vision-Language Models Ul- trasound segmentation using semi-supervised learning: Application in point-of-care sarcopenia assessment,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:36:09.340270Z

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Observation f4cc2ee6-3dc3-4d3a-b8c3-368d95595095 · outbound

This paper cites Deep spectral methods for unsupervised ultrasound image interpretation,.

Grounding DINO-US-SAM: Text-Prompted Multi-Organ Segmentation in Ultrasound with LoRA-Tuned Vision-Language Models Deep spectral methods for unsupervised ultrasound image interpretation,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:36:09.183386Z

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Observation 7fb99202-1254-4c8b-b501-4038ece0d9e2 · outbound

This paper cites Shan: Shape guided network for thyroid nodule ultrasound cross-domain segmenta- tion,.

Grounding DINO-US-SAM: Text-Prompted Multi-Organ Segmentation in Ultrasound with LoRA-Tuned Vision-Language Models Shan: Shape guided network for thyroid nodule ultrasound cross-domain segmenta- tion,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:36:09.043701Z

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Observation 92882bfe-d15e-45cf-aec8-31ff238990a7 · outbound

This paper cites Learning transferable visual models from natural language supervision,.

Grounding DINO-US-SAM: Text-Prompted Multi-Organ Segmentation in Ultrasound with LoRA-Tuned Vision-Language Models Learning transferable visual models from natural language supervision,

Reference 14

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Source-reported events for the cited work

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Observation 7aafe1dd-afca-4ed1-8887-181c5afb5c40 · outbound

This paper cites Medclip-sam: Bridging text and image towards universal medical image segmenta- tion,.

Grounding DINO-US-SAM: Text-Prompted Multi-Organ Segmentation in Ultrasound with LoRA-Tuned Vision-Language Models Medclip-sam: Bridging text and image towards universal medical image segmenta- tion,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:36:08.892698Z

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Observation ec641b8c-4ab2-4bd2-a9ed-18d42d9e46d5 · outbound

This paper cites Grounding dino: Marrying dino with grounded pre-training for open-set object detection,.

Grounding DINO-US-SAM: Text-Prompted Multi-Organ Segmentation in Ultrasound with LoRA-Tuned Vision-Language Models Grounding dino: Marrying dino with grounded pre-training for open-set object detection,

Reference 16

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Observation 458cb8af-95c2-426e-b785-ff63e5fa2557 · outbound

This paper cites Medclip: Contrastive learning from unpaired medical images and text,.

Grounding DINO-US-SAM: Text-Prompted Multi-Organ Segmentation in Ultrasound with LoRA-Tuned Vision-Language Models Medclip: Contrastive learning from unpaired medical images and text,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:36:08.718623Z

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Observation 01aaae1e-be31-4f8f-92c1-cdb92350b2b8 · outbound

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

Grounding DINO-US-SAM: Text-Prompted Multi-Organ Segmentation in Ultrasound with LoRA-Tuned Vision-Language Models SAM 2: Segment Anything in Images and Videos

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-06T21:36:00.165936Z

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Observation 7838abdd-2ba4-48c4-a6f0-3916f608a549 · outbound

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

Grounding DINO-US-SAM: Text-Prompted Multi-Organ Segmentation in Ultrasound with LoRA-Tuned Vision-Language Models A foundation model for joint segmentation, detection and recognition of biomedical objects across nine modalities,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:36:08.514108Z

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Observation 9eda4b04-0e63-4ed0-87d2-cdea1bdae6a1 · outbound

This paper cites MedCLIP-SAMv2: Towards Universal Text-Driven Medical Image Segmentation.

Grounding DINO-US-SAM: Text-Prompted Multi-Organ Segmentation in Ultrasound with LoRA-Tuned Vision-Language Models MedCLIP-SAMv2: Towards Universal Text-Driven Medical Image Segmentation

Reference 20

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Observation a0d1a5c2-0cf2-4dc9-85cd-e48d72cfbd05 · outbound

This paper cites Segment anything in medical images,.

Grounding DINO-US-SAM: Text-Prompted Multi-Organ Segmentation in Ultrasound with LoRA-Tuned Vision-Language Models Segment anything in medical images,

Reference 21

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Observation 701fc702-5ddb-4189-9131-c121f7a1ca7d · outbound

This paper cites Segment anything small for ultrasound: enhanced segmen- tation of small anatomical structures using iterative point prompts and image transformations,.

Grounding DINO-US-SAM: Text-Prompted Multi-Organ Segmentation in Ultrasound with LoRA-Tuned Vision-Language Models Segment anything small for ultrasound: enhanced segmen- tation of small anatomical structures using iterative point prompts and image transformations,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:36:08.360470Z

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Observation 3835e09b-7cc9-4d31-8519-34cf233e6f1c · outbound

This paper cites Meyer, A.

Grounding DINO-US-SAM: Text-Prompted Multi-Organ Segmentation in Ultrasound with LoRA-Tuned Vision-Language Models Meyer, A

Reference 23

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Observation f6eb0a48-9ae6-4980-a103-fb5b0a510fe7 · outbound

This paper cites Sam-medus: a foundational model for universal ultrasound image segmentation,.

Grounding DINO-US-SAM: Text-Prompted Multi-Organ Segmentation in Ultrasound with LoRA-Tuned Vision-Language Models Sam-medus: a foundational model for universal ultrasound image segmentation,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:36:08.232744Z

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Observation afd4bc01-0200-4309-9db5-b47fd2a7edf7 · outbound

This paper cites Clicksam: Fine-tuning segment anything model using click prompts for ultrasound image segmentation,.

Grounding DINO-US-SAM: Text-Prompted Multi-Organ Segmentation in Ultrasound with LoRA-Tuned Vision-Language Models Clicksam: Fine-tuning segment anything model using click prompts for ultrasound image segmentation,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:36:08.114231Z

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Observation cad618d8-7b12-422d-b92e-889deb8cbd1c · outbound

This paper cites Beyond adapting sam: Towards end-to-end ultrasound image segmentation via auto prompting,.

Grounding DINO-US-SAM: Text-Prompted Multi-Organ Segmentation in Ultrasound with LoRA-Tuned Vision-Language Models Beyond adapting sam: Towards end-to-end ultrasound image segmentation via auto prompting,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:36:07.978427Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 225f6cca-c462-4214-9ae3-5ffdfddecb82 · outbound

This paper cites Apg-sam: Auto- matic prompt generation for sam-based breast lesion segmentation with boundary-aware optimization,.

Grounding DINO-US-SAM: Text-Prompted Multi-Organ Segmentation in Ultrasound with LoRA-Tuned Vision-Language Models Apg-sam: Auto- matic prompt generation for sam-based breast lesion segmentation with boundary-aware optimization,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:36:07.834951Z

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Observation bf8fef54-ee43-47be-a8ef-f28228dfff89 · outbound

This paper cites Cc-sam: Sam with cross-feature attention and context for ultrasound image segmentation,.

Grounding DINO-US-SAM: Text-Prompted Multi-Organ Segmentation in Ultrasound with LoRA-Tuned Vision-Language Models Cc-sam: Sam with cross-feature attention and context for ultrasound image segmentation,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:36:07.687320Z

Source-reported events for the cited work

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Observation c9635d7c-2927-4c3b-afba-cb8b44e5efa4 · outbound

This paper cites Multi-organ foundation model for universal ultrasound image segmentation with task prompt and anatomical prior,.

Grounding DINO-US-SAM: Text-Prompted Multi-Organ Segmentation in Ultrasound with LoRA-Tuned Vision-Language Models Multi-organ foundation model for universal ultrasound image segmentation with task prompt and anatomical prior,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:36:07.541415Z

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Observation 72a1c900-adc3-4860-8801-ba0717db12f1 · outbound

This paper cites Prompting foundational models for omni-supervised instance segmentation,.

Grounding DINO-US-SAM: Text-Prompted Multi-Organ Segmentation in Ultrasound with LoRA-Tuned Vision-Language Models Prompting foundational models for omni-supervised instance segmentation,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:36:07.395517Z

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Observation 868d67e6-5ea9-4288-ae98-207e72bc3ff2 · outbound

This paper cites Interpreting object-level foundation models via visual precision search,.

Grounding DINO-US-SAM: Text-Prompted Multi-Organ Segmentation in Ultrasound with LoRA-Tuned Vision-Language Models Interpreting object-level foundation models via visual precision search,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:36:07.204916Z

Source-reported events for the cited work

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Observation 9465a8b7-550e-4ab4-bd85-e2b30fa7fef2 · outbound

This paper cites Efficientvit-sam: Accelerated segment anything model without performance loss,.

Grounding DINO-US-SAM: Text-Prompted Multi-Organ Segmentation in Ultrasound with LoRA-Tuned Vision-Language Models Efficientvit-sam: Accelerated segment anything model without performance loss,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:36:07.080007Z

Source-reported events for the cited work

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Observation 4f727b01-81f7-48b4-bc28-d406c4f02e2d · outbound

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

Grounding DINO-US-SAM: Text-Prompted Multi-Organ Segmentation in Ultrasound with LoRA-Tuned Vision-Language Models Curated benchmark dataset for ultrasound based breast lesion analysis,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:36:06.915707Z

Source-reported events for the cited work

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Observation 910ff579-db3f-43dc-8e22-ac677819435f · outbound

This paper cites An open-access breast lesion ultrasound image database: Applicable in artificial intelligence studies,.

Grounding DINO-US-SAM: Text-Prompted Multi-Organ Segmentation in Ultrasound with LoRA-Tuned Vision-Language Models An open-access breast lesion ultrasound image database: Applicable in artificial intelligence studies,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:36:06.753555Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e379206c-a8c0-47fb-bca9-bec4cbd01dc2 · outbound

This paper cites Artificial intelligence, bi-rads evalua- tion and morphometry: A novel combination to diagnose breast cancer using ultrasonography, results from multi-center cohorts,.

Grounding DINO-US-SAM: Text-Prompted Multi-Organ Segmentation in Ultrasound with LoRA-Tuned Vision-Language Models Artificial intelligence, bi-rads evalua- tion and morphometry: A novel combination to diagnose breast cancer using ultrasonography, results from multi-center cohorts,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:36:06.649432Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3db6745e-5c39-4880-9f28-d7ab0e879857 · outbound

This paper cites Applications of machine-learning algorithms for prediction of benign and malignant breast lesions using ultrasound radiomics signatures: A multi-center study,.

Grounding DINO-US-SAM: Text-Prompted Multi-Organ Segmentation in Ultrasound with LoRA-Tuned Vision-Language Models Applications of machine-learning algorithms for prediction of benign and malignant breast lesions using ultrasound radiomics signatures: A multi-center study,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:36:06.493001Z

Source-reported events for the cited work

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Observation cbee4b2a-1bbd-4cef-aa54-ce67bac03b12 · outbound

This paper cites Iqbal, “BUS UC,” Mendeley Data, V1, 2023, accessed: 2024-06-07.

Grounding DINO-US-SAM: Text-Prompted Multi-Organ Segmentation in Ultrasound with LoRA-Tuned Vision-Language Models Iqbal, “BUS UC,” Mendeley Data, V1, 2023, accessed: 2024-06-07

Reference 37

Resolution: verified exact; doi, observed 2026-08-06T21:36:04.076809Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 27933d88-c3ec-40ce-abe1-ab65ee2f2967 · outbound

This paper cites Bus-uclm: Breast ultrasound lesion segmentation dataset,.

Grounding DINO-US-SAM: Text-Prompted Multi-Organ Segmentation in Ultrasound with LoRA-Tuned Vision-Language Models Bus-uclm: Breast ultrasound lesion segmentation dataset,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:36:06.371518Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a9a09bc3-7fbd-4f8c-9122-0af135471374 · outbound

This paper cites Automated breast ultrasound lesions detection using convolutional neural networks,.

Grounding DINO-US-SAM: Text-Prompted Multi-Organ Segmentation in Ultrasound with LoRA-Tuned Vision-Language Models Automated breast ultrasound lesions detection using convolutional neural networks,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:36:06.216552Z

Source-reported events for the cited work

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Observation a1047f64-9db1-4505-8d82-e24bdeec0f61 · outbound

This paper cites Dataset of breast ultrasound images,.

Grounding DINO-US-SAM: Text-Prompted Multi-Organ Segmentation in Ultrasound with LoRA-Tuned Vision-Language Models Dataset of breast ultrasound images,

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-06T21:36:01.959362Z

Source-reported events for the cited work

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Observation c6285174-0c15-41fd-9e0d-881a5ca83797 · outbound

This paper cites Stu-hospital,.

Grounding DINO-US-SAM: Text-Prompted Multi-Organ Segmentation in Ultrasound with LoRA-Tuned Vision-Language Models Stu-hospital,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:36:06.046967Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 099a554c-c822-4a37-8c89-892a6fe053d5 · outbound

This paper cites Segmentation and recognition of breast ultrasound images based on an expanded u-net,.

Grounding DINO-US-SAM: Text-Prompted Multi-Organ Segmentation in Ultrasound with LoRA-Tuned Vision-Language Models Segmentation and recognition of breast ultrasound images based on an expanded u-net,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:36:05.886925Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 16e5b623-fd45-4620-b5cb-747f892bfa11 · outbound

This paper cites Multi-task learning for thyroid nodule segmentation with thyroid region prior,.

Grounding DINO-US-SAM: Text-Prompted Multi-Organ Segmentation in Ultrasound with LoRA-Tuned Vision-Language Models Multi-task learning for thyroid nodule segmentation with thyroid region prior,

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-06T21:36:02.229164Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d95e61ef-5617-4d7d-a36a-d08ea5df8353 · outbound

This paper cites Less is more: Adaptive curriculum learning for thyroid nodule diagnosis,.

Grounding DINO-US-SAM: Text-Prompted Multi-Organ Segmentation in Ultrasound with LoRA-Tuned Vision-Language Models Less is more: Adaptive curriculum learning for thyroid nodule diagnosis,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:36:05.746494Z

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Observation b216bd07-3b6e-4943-bc88-60fba8e50d9f · outbound

This paper cites Algorithm guided outlining of 105 pancreatic cancer liver metastases in ultrasound,.

Grounding DINO-US-SAM: Text-Prompted Multi-Organ Segmentation in Ultrasound with LoRA-Tuned Vision-Language Models Algorithm guided outlining of 105 pancreatic cancer liver metastases in ultrasound,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:36:05.557271Z

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This paper cites Improving artificial intelligence pipeline for liver malignancy diagnosis using ultrasound images and video frames,.

Grounding DINO-US-SAM: Text-Prompted Multi-Organ Segmentation in Ultrasound with LoRA-Tuned Vision-Language Models Improving artificial intelligence pipeline for liver malignancy diagnosis using ultrasound images and video frames,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:36:05.363653Z

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This paper cites U2-bench: Benchmarking large vision-language models on ultrasound understanding,.

Grounding DINO-US-SAM: Text-Prompted Multi-Organ Segmentation in Ultrasound with LoRA-Tuned Vision-Language Models U2-bench: Benchmarking large vision-language models on ultrasound understanding,

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-06T21:36:02.569452Z

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This paper cites Hamid, I.

Grounding DINO-US-SAM: Text-Prompted Multi-Organ Segmentation in Ultrasound with LoRA-Tuned Vision-Language Models Hamid, I

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:36:05.118732Z

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Observation ae6d910b-c6f7-445e-9306-7cf30b375b43 · outbound

This paper cites The open kidney ultrasound data set,.

Grounding DINO-US-SAM: Text-Prompted Multi-Organ Segmentation in Ultrasound with LoRA-Tuned Vision-Language Models The open kidney ultrasound data set,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:36:04.935717Z

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Observation 3472a860-37d1-4847-97b5-f41ea3cdf8be · outbound

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

Grounding DINO-US-SAM: Text-Prompted Multi-Organ Segmentation in Ultrasound with LoRA-Tuned Vision-Language Models Bus-bra: a breast ultrasound dataset for assessing computer-aided diagnosis systems,

Reference 50

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Observation 63f75a5c-24bd-4ff7-b97f-bbaaf07def38 · outbound

This paper cites Thyroid nodule segmentation and classification in ultrasound images,.

Grounding DINO-US-SAM: Text-Prompted Multi-Organ Segmentation in Ultrasound with LoRA-Tuned Vision-Language Models Thyroid nodule segmentation and classification in ultrasound images,

Reference 51

Resolution: verified exact; doi, observed 2026-08-06T21:36:03.904595Z

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Observation 51ed1768-5d8b-4ecd-92d3-8cac0327ef9c · outbound

This paper cites Luminous database: lumbar multifidus muscle segmentation from ultrasound images,.

Grounding DINO-US-SAM: Text-Prompted Multi-Organ Segmentation in Ultrasound with LoRA-Tuned Vision-Language Models Luminous database: lumbar multifidus muscle segmentation from ultrasound images,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:36:04.733755Z

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Observation 3b693de9-1cd5-47d8-ac98-e7d946cc66cd · outbound

This paper cites Lora: Low-rank adaptation of large language models.

Grounding DINO-US-SAM: Text-Prompted Multi-Organ Segmentation in Ultrasound with LoRA-Tuned Vision-Language Models Lora: Low-rank adaptation of large language models

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-06T21:36:03.085156Z

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Observation cbceaa98-d3e0-432d-ae7e-3cd742c3a44a · outbound

This paper cites Generalized intersection over union: A metric and a loss for bounding box regression,.

Grounding DINO-US-SAM: Text-Prompted Multi-Organ Segmentation in Ultrasound with LoRA-Tuned Vision-Language Models Generalized intersection over union: A metric and a loss for bounding box regression,

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-06T21:36:03.165145Z

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Observation c52409e6-e70c-407b-83df-ecdb1e6371fd · outbound

This paper cites Focal loss for dense object detection,.

Grounding DINO-US-SAM: Text-Prompted Multi-Organ Segmentation in Ultrasound with LoRA-Tuned Vision-Language Models Focal loss for dense object detection,

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-06T21:36:03.253284Z

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Observation 5634d2e2-41a9-4061-902d-c4bb6ce60060 · outbound

This paper cites The pascal visual object classes (voc) challenge,.

Grounding DINO-US-SAM: Text-Prompted Multi-Organ Segmentation in Ultrasound with LoRA-Tuned Vision-Language Models The pascal visual object classes (voc) challenge,

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-06T21:36:03.371681Z

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Observation 34483cc4-ab1e-4143-bf74-9fc2f5f80b7f · outbound

This paper cites Measures of the amount of ecologic association between species,.

Grounding DINO-US-SAM: Text-Prompted Multi-Organ Segmentation in Ultrasound with LoRA-Tuned Vision-Language Models Measures of the amount of ecologic association between species,

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-06T21:36:03.481871Z

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This paper cites Decoupled Weight Decay Regularization.

Grounding DINO-US-SAM: Text-Prompted Multi-Organ Segmentation in Ultrasound with LoRA-Tuned Vision-Language Models Decoupled Weight Decay Regularization

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-06T21:36:03.587486Z

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Observation 5f7b0b9b-0a01-47d9-bc19-dd25f7091512 · outbound

This paper cites Segment anything,.

Grounding DINO-US-SAM: Text-Prompted Multi-Organ Segmentation in Ultrasound with LoRA-Tuned Vision-Language Models Segment anything,

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-06T21:36:03.655646Z

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Observation c0806b98-00ad-43bf-a71e-7ea757c9ed4b · outbound

This paper cites Biomedcoop: Learning to prompt for biomedical vision-language models,.

Grounding DINO-US-SAM: Text-Prompted Multi-Organ Segmentation in Ultrasound with LoRA-Tuned Vision-Language Models Biomedcoop: Learning to prompt for biomedical vision-language models,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:36:04.488672Z

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Pith citing papers

Observation 29388302-d5d5-42ac-aeaf-d3d24ff0706d · inbound

CLIP-SVD: Efficient and Interpretable Vision-Language Adaptation via Singular Values cites this paper.

CLIP-SVD: Efficient and Interpretable Vision-Language Adaptation via Singular Values Grounding DINO-US-SAM: Text-Prompted Multi-Organ Segmentation in Ultrasound with LoRA-Tuned Vision-Language Models

Reference 59

Resolution: verified exact; arxiv_id, observed 2026-05-18T18:56:45.939464Z

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