Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-05-08T08:37:19.723297Z
Paper Citation Record · LEDGER
Learning from Noisy Prompts: Saliency-Guided Prompt Distillation for Robust Segmentation with SAM
As of 13 August 2026, this Paper Citation Record lists 49 of 49 outbound references and 0 inbound Pith citation observations for arXiv:2604.23314.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
49 of 49 outbound references displayed
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- verified fuzzy40
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External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 13720804-4a67-4567-9cdb-dd9a1d04a03a · outbound
This paper cites Maier-Hein, Peter M.
Learning from Noisy Prompts: Saliency-Guided Prompt Distillation for Robust Segmentation with SAM Maier-Hein, Peter M
Reference 1
- Resolution
- verified fuzzy
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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 c4890c9b-40fe-417d-8149-f52b5fa38569 · outbound
This paper cites Swin-unet: Unet-like pure transformer for medical image segmentation.
Learning from Noisy Prompts: Saliency-Guided Prompt Distillation for Robust Segmentation with SAM Swin-unet: Unet-like pure transformer for medical image segmentation
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T16:48:04.391059Z
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 3b12d363-c355-46df-b5ad-9d9291adf611 · outbound
This paper cites Ma-sam: Modality-agnostic sam adaptation for 3d med- ical image segmentation.
Learning from Noisy Prompts: Saliency-Guided Prompt Distillation for Robust Segmentation with SAM Ma-sam: Modality-agnostic sam adaptation for 3d med- ical image segmentation
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T16:48:04.376150Z
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 cd78abd6-b6eb-46dc-b32c-3a5dc761c30b · outbound
This paper cites Yuille, and Yuyin Zhou.
Learning from Noisy Prompts: Saliency-Guided Prompt Distillation for Robust Segmentation with SAM Yuille, and Yuyin Zhou
Reference 4
- Resolution
- verified fuzzy
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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 a9ba287a-0670-4715-8da9-2a607d498556 · outbound
This paper cites SAM on Medical Images: A Comprehensive Study on Three Prompt Modes.
Learning from Noisy Prompts: Saliency-Guided Prompt Distillation for Robust Segmentation with SAM SAM on Medical Images: A Comprehensive Study on Three Prompt Modes
Reference 5
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T20:31:14.725395Z
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 0b65e670-6e6b-41a5-ae40-40398ca62917 · outbound
This paper cites Sam-med2d.
Learning from Noisy Prompts: Saliency-Guided Prompt Distillation for Robust Segmentation with SAM Sam-med2d
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T16:48:04.383818Z
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 52a21332-0e44-472e-aad5-c7c06bbe2a2d · outbound
This paper cites Esr essentials: a step-by-step guide of segmentation for radiologists-practice recommen- dations by the european society of medical imaging infor- matics.European radiology.
Learning from Noisy Prompts: Saliency-Guided Prompt Distillation for Robust Segmentation with SAM Esr essentials: a step-by-step guide of segmentation for radiologists-practice recommen- dations by the european society of medical imaging infor- matics.European radiology
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T16:48:04.369236Z
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 91b5c0a9-4617-477d-9df2-f7cbd20ccb90 · outbound
This paper cites Assessing the inflammatory severity of the terminal ileum in crohn disease using radiomics based on mri.BMC Medical Imaging, 22.
Learning from Noisy Prompts: Saliency-Guided Prompt Distillation for Robust Segmentation with SAM Assessing the inflammatory severity of the terminal ileum in crohn disease using radiomics based on mri.BMC Medical Imaging, 22
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T16:48:04.333027Z
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 23fc4678-0646-4b4c-a7bd-5e8fa0186c33 · outbound
This paper cites Few-shot medical image segmentation with cycle- resemblance attention.
Learning from Noisy Prompts: Saliency-Guided Prompt Distillation for Robust Segmentation with SAM Few-shot medical image segmentation with cycle- resemblance attention
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T16:48:04.365136Z
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 fe53ad75-c120-46c6-a2e0-bba0b517cd23 · outbound
This paper cites Stable Segment Anything Model.
Learning from Noisy Prompts: Saliency-Guided Prompt Distillation for Robust Segmentation with SAM Stable Segment Anything Model
Reference 10
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T20:31:14.695849Z
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 c38fc299-f04d-4f39-ba5f-4cbb72c5f391 · outbound
This paper cites The kits21 chal- lenge: Automatic segmentation of kidneys, renal tumors, and renal cysts in corticomedullary-phase ct.
Learning from Noisy Prompts: Saliency-Guided Prompt Distillation for Robust Segmentation with SAM The kits21 chal- lenge: Automatic segmentation of kidneys, renal tumors, and renal cysts in corticomedullary-phase ct
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T16:48:04.373409Z
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 e7c3fce4-0f8a-4b15-8fc9-cd20672b8cbe · outbound
This paper cites Lora: Low-rank adaptation of large language models.ICLR, 1(2):3.
Learning from Noisy Prompts: Saliency-Guided Prompt Distillation for Robust Segmentation with SAM Lora: Low-rank adaptation of large language models.ICLR, 1(2):3
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T16:48:04.377154Z
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 b3d82dab-ed74-4ff7-8be5-b60902853c9e · outbound
This paper cites Learning to Prompt Segment Anything Models.
Learning from Noisy Prompts: Saliency-Guided Prompt Distillation for Robust Segmentation with SAM Learning to Prompt Segment Anything Models
Reference 13
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T20:31:14.688043Z
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 68dffe25-a695-4707-b877-82a81217bf9c · outbound
This paper cites Segment anything model for medical images?Medical Image Analysis, 92:103061.
Learning from Noisy Prompts: Saliency-Guided Prompt Distillation for Robust Segmentation with SAM Segment anything model for medical images?Medical Image Analysis, 92:103061
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T16:48:04.380461Z
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 04ee22dd-4bbe-4e7f-b151-d9c4f3d41047 · outbound
This paper cites Oxford University Press.
Learning from Noisy Prompts: Saliency-Guided Prompt Distillation for Robust Segmentation with SAM Oxford University Press
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T16:48:04.397719Z
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 88f43eb2-646e-4f81-b7a7-eab25e174eb6 · outbound
This paper cites Jaeger, Simon Kohl, Jakob Wasserthal, Gregor Koehler, Tobias Norajitra, Sebastian Wirkert, and Klaus H.
Learning from Noisy Prompts: Saliency-Guided Prompt Distillation for Robust Segmentation with SAM Jaeger, Simon Kohl, Jakob Wasserthal, Gregor Koehler, Tobias Norajitra, Sebastian Wirkert, and Klaus H
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T16:48:04.404806Z
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 f7f78aa8-d90c-4310-b583-4020b375cc93 · outbound
This paper cites Copycats: the many lives of a publicly available medical imaging dataset.
Learning from Noisy Prompts: Saliency-Guided Prompt Distillation for Robust Segmentation with SAM Copycats: the many lives of a publicly available medical imaging dataset
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T16:48:04.357819Z
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 0cbec216-87f0-4a90-b800-46e85685485f · outbound
This paper cites Deep learning with noisy labels: Exploring tech- niques and remedies in medical image analysis.Medical im- age analysis, 65:101759.
Learning from Noisy Prompts: Saliency-Guided Prompt Distillation for Robust Segmentation with SAM Deep learning with noisy labels: Exploring tech- niques and remedies in medical image analysis.Medical im- age analysis, 65:101759
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T16:48:04.336497Z
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 7e319e68-331f-4763-9589-c5386cf4428a · outbound
This paper cites Segment any- thing.
Learning from Noisy Prompts: Saliency-Guided Prompt Distillation for Robust Segmentation with SAM Segment any- thing
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T16:48:04.333264Z
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 8ddf1744-5d1d-4453-a18a-46f1d4e9f484 · outbound
This paper cites Correia, Xue Feng, Kibrom B.
Learning from Noisy Prompts: Saliency-Guided Prompt Distillation for Robust Segmentation with SAM Correia, Xue Feng, Kibrom B
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T16:48:04.349955Z
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 0990dfba-0b6c-4fe4-a642-a7860c01c28f · outbound
This paper cites Self- supervised alignment learning for medical image segmenta- tion.
Learning from Noisy Prompts: Saliency-Guided Prompt Distillation for Robust Segmentation with SAM Self- supervised alignment learning for medical image segmenta- tion
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T16:48:04.340214Z
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 f46fde5e-a4e0-4829-bac9-cfaa4b07aaa6 · outbound
This paper cites Scribblevc: Scribble-supervised medical im- age segmentation with vision-class embedding.
Learning from Noisy Prompts: Saliency-Guided Prompt Distillation for Robust Segmentation with SAM Scribblevc: Scribble-supervised medical im- age segmentation with vision-class embedding
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T16:48:04.322272Z
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 c252e644-2bd3-4897-aff3-a1668a3614f0 · outbound
This paper cites Few shot medical image segmentation with cross attention transformer.
Learning from Noisy Prompts: Saliency-Guided Prompt Distillation for Robust Segmentation with SAM Few shot medical image segmentation with cross attention transformer
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T16:48:04.390852Z
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 1c81f0d5-10e0-4aa5-8916-bdadff76ae85 · outbound
This paper cites Samrefiner: Taming segment anything model for universal mask refine- ment.
Learning from Noisy Prompts: Saliency-Guided Prompt Distillation for Robust Segmentation with SAM Samrefiner: Taming segment anything model for universal mask refine- ment
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T16:48:04.326168Z
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 62132007-7e0f-467d-9f48-36db10e9e7f8 · outbound
This paper cites Adaptive early-learning correc- tion for segmentation from noisy annotations.
Learning from Noisy Prompts: Saliency-Guided Prompt Distillation for Robust Segmentation with SAM Adaptive early-learning correc- tion for segmentation from noisy annotations
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T16:48:04.285814Z
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 d52b4e5a-fb11-43c7-9945-85ca6c34beb6 · outbound
This paper cites Segment anything in medical images.Nature Communications, 15(1).
Learning from Noisy Prompts: Saliency-Guided Prompt Distillation for Robust Segmentation with SAM Segment anything in medical images.Nature Communications, 15(1)
Reference 26
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T16:48:04.314874Z
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 ca697586-aa6d-4e95-a047-fe25ac84a43d · outbound
This paper cites MedSAM2: Segment Anything in 3D Medical Images and Videos.
Learning from Noisy Prompts: Saliency-Guided Prompt Distillation for Robust Segmentation with SAM MedSAM2: Segment Anything in 3D Medical Images and Videos
Reference 27
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T20:31:14.669886Z
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 31b19f07-7af3-4720-91b9-ebc0ae6b0615 · outbound
This paper cites A new dataset of computed-tomography angiography images for computer-aided detection of pulmonary embolism.Scientific Data, 5(180180):1–9.
Learning from Noisy Prompts: Saliency-Guided Prompt Distillation for Robust Segmentation with SAM A new dataset of computed-tomography angiography images for computer-aided detection of pulmonary embolism.Scientific Data, 5(180180):1–9
Reference 28
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- raw_fallback, observed 2026-05-26T16:48:04.318120Z
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 107a8600-1247-44e3-aade-c9a393b65040 · outbound
This paper cites Cross prompting con- sistency with segment anything model for semi-supervised medical image segmentation.
Learning from Noisy Prompts: Saliency-Guided Prompt Distillation for Robust Segmentation with SAM Cross prompting con- sistency with segment anything model for semi-supervised medical image segmentation
Reference 29
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T16:48:04.330118Z
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 07bcee42-1391-4560-8da3-fe8b576a2eb8 · outbound
This paper cites Attention U-Net: Learning Where to Look for the Pancreas.
Learning from Noisy Prompts: Saliency-Guided Prompt Distillation for Robust Segmentation with SAM Attention U-Net: Learning Where to Look for the Pancreas
Reference 30
- Resolution
- verified exact
- arxiv_id, observed 2026-05-12T21:17:43.376137Z
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 5066fcef-0586-4af1-b9f8-afe67d764750 · outbound
This paper cites Medical image segmentation with limited supervision: A review of deep network models.
Learning from Noisy Prompts: Saliency-Guided Prompt Distillation for Robust Segmentation with SAM Medical image segmentation with limited supervision: A review of deep network models
Reference 31
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T16:48:04.344726Z
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 506ebb3c-512b-4f73-bb8f-5aab8eeb1815 · outbound
This paper cites Benchmarking hu- man and automated prompting in the segment anything model.
Learning from Noisy Prompts: Saliency-Guided Prompt Distillation for Robust Segmentation with SAM Benchmarking hu- man and automated prompting in the segment anything model
Reference 32
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T16:48:04.353852Z
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 be0f9b45-86ec-4c4d-b6f2-9921aa00a9ac · outbound
This paper cites Learning transferable visual models from natural language supervi- sion.
Learning from Noisy Prompts: Saliency-Guided Prompt Distillation for Robust Segmentation with SAM Learning transferable visual models from natural language supervi- sion
Reference 33
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T16:48:04.361609Z
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 fa20931a-ea13-430d-93ea-6acb55317c09 · outbound
This paper cites SAM 2: Segment Anything in Images and Videos.
Learning from Noisy Prompts: Saliency-Guided Prompt Distillation for Robust Segmentation with SAM SAM 2: Segment Anything in Images and Videos
Reference 34
- Resolution
- verified exact
- local_arxiv, observed 2026-05-11T20:31:14.661783Z
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 61a480ff-9958-4d37-bbd3-331b23db390b · outbound
This paper cites U-net: Convolutional networks for biomedical image segmentation.
Learning from Noisy Prompts: Saliency-Guided Prompt Distillation for Robust Segmentation with SAM U-net: Convolutional networks for biomedical image segmentation
Reference 35
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T16:48:04.387741Z
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 66085296-d406-471e-b7b6-069394a3ac09 · outbound
This paper cites VM-UNet: Vision Mamba UNet for Medical Image Segmentation.
Learning from Noisy Prompts: Saliency-Guided Prompt Distillation for Robust Segmentation with SAM VM-UNet: Vision Mamba UNet for Medical Image Segmentation
Reference 36
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T20:31:14.677976Z
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 34eb52df-da0b-4df2-b430-bb4adba18b82 · outbound
This paper cites Autosam: Adapting sam to medical images by overloading the prompt encoder.
Learning from Noisy Prompts: Saliency-Guided Prompt Distillation for Robust Segmentation with SAM Autosam: Adapting sam to medical images by overloading the prompt encoder
Reference 37
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T16:48:04.300153Z
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 2b1c34d9-7bc0-4df4-b56c-901c835ec9d7 · outbound
This paper cites Learning from noisy labels with deep neural networks: A survey.IEEE transactions on neural networks and learning systems, 34(11):8135–8153.
Learning from Noisy Prompts: Saliency-Guided Prompt Distillation for Robust Segmentation with SAM Learning from noisy labels with deep neural networks: A survey.IEEE transactions on neural networks and learning systems, 34(11):8135–8153
Reference 38
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T16:48:04.304095Z
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 cd536ba1-c254-49e6-8cfa-333f25f44759 · outbound
This paper cites Few-shot medical image segmentation with high-fidelity prototypes.Medical Image Analysis, 100:103412.
Learning from Noisy Prompts: Saliency-Guided Prompt Distillation for Robust Segmentation with SAM Few-shot medical image segmentation with high-fidelity prototypes.Medical Image Analysis, 100:103412
Reference 39
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T16:48:04.315426Z
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 a96421f8-ad9d-483f-8b45-66c5f033a52a · outbound
This paper cites Surface-GCN: Learning interaction experi- ence for organ segmentation in 3D medical images.Medical Physics, 50(8):5030–5044.
Learning from Noisy Prompts: Saliency-Guided Prompt Distillation for Robust Segmentation with SAM Surface-GCN: Learning interaction experi- ence for organ segmentation in 3D medical images.Medical Physics, 50(8):5030–5044
Reference 40
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T16:48:04.358343Z
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 776c7593-47fd-4690-b674-9c4fb7f95b79 · outbound
This paper cites Navigating data scarcity using foundation models: A benchmark of few- shot and zero-shot learning approaches in medical imaging.
Learning from Noisy Prompts: Saliency-Guided Prompt Distillation for Robust Segmentation with SAM Navigating data scarcity using foundation models: A benchmark of few- shot and zero-shot learning approaches in medical imaging
Reference 41
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T16:48:04.386769Z
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 1b6910e5-3451-4f06-a4f5-2c3003ee9cf3 · outbound
This paper cites Medical sam adapter: Adapting seg- ment anything model for medical image segmentation.
Learning from Noisy Prompts: Saliency-Guided Prompt Distillation for Robust Segmentation with SAM Medical sam adapter: Adapting seg- ment anything model for medical image segmentation
Reference 42
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T16:48:04.368958Z
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 26554bdb-b043-4fa5-842a-c9e48f1321d2 · outbound
This paper cites Self-prompting large vision models for few-shot medical image segmenta- tion.
Learning from Noisy Prompts: Saliency-Guided Prompt Distillation for Robust Segmentation with SAM Self-prompting large vision models for few-shot medical image segmenta- tion
Reference 43
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T16:48:04.351877Z
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 cc9e12d2-4b47-4a5c-a83f-3ab9f7d82aa8 · outbound
This paper cites MambaVesselNet++: A Hybrid CNN-Mamba Architecture for Medical Image Segmentation.
Learning from Noisy Prompts: Saliency-Guided Prompt Distillation for Robust Segmentation with SAM MambaVesselNet++: A Hybrid CNN-Mamba Architecture for Medical Image Segmentation
Reference 44
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T20:31:14.714669Z
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 9b3ee078-a690-40a6-80dd-48325d15f17c · outbound
This paper cites Keypoint-augmented self-supervised learn- ing for medical image segmentation with limited annota- 10 tion.Advances in Neural Information Processing Systems, 36:60724–60747.
Learning from Noisy Prompts: Saliency-Guided Prompt Distillation for Robust Segmentation with SAM Keypoint-augmented self-supervised learn- ing for medical image segmentation with limited annota- 10 tion.Advances in Neural Information Processing Systems, 36:60724–60747
Reference 45
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T16:48:04.344432Z
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 3a46fc5d-967e-44bf-84fb-72d90eabedd3 · outbound
This paper cites Characterizing label errors: confident learning for noisy-labeled image segmentation.
Learning from Noisy Prompts: Saliency-Guided Prompt Distillation for Robust Segmentation with SAM Characterizing label errors: confident learning for noisy-labeled image segmentation
Reference 46
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T16:48:04.348658Z
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 50b4cc31-e4f0-411d-8047-7768750f7570 · outbound
This paper cites Unet++: A nested u-net architecture for medical image segmentation.
Learning from Noisy Prompts: Saliency-Guided Prompt Distillation for Robust Segmentation with SAM Unet++: A nested u-net architecture for medical image segmentation
Reference 47
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T16:48:04.355115Z
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 64c59820-f420-4a48-8c3a-4d06d0a89c2f · outbound
This paper cites Models genesis: Generic autodidactic models for 3d medical image analysis.
Learning from Noisy Prompts: Saliency-Guided Prompt Distillation for Robust Segmentation with SAM Models genesis: Generic autodidactic models for 3d medical image analysis
Reference 48
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T16:48:04.365327Z
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 65cb9f3b-5ebe-4324-be75-82bce5dd643d · outbound
This paper cites Local Label Propagation for Large-Scale Semi-Supervised Learning.
Learning from Noisy Prompts: Saliency-Guided Prompt Distillation for Robust Segmentation with SAM Local Label Propagation for Large-Scale Semi-Supervised Learning
Reference 49
- Resolution
- verified exact
- arxiv_id, observed 2026-07-04T23:40:03.141604Z
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
No inbound Pith citation observations are available.