Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-07T15:28:41.479862Z
Paper Citation Record · LEDGER
TAGS: 3D Tumor-Adaptive Guidance for SAM
As of 18 August 2026, this Paper Citation Record lists 66 of 66 outbound references and 0 inbound Pith citation observations for arXiv:2505.17096.
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 2026-08-18T06:34:40.430872+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
66 of 66 outbound references displayed
- verified exact2
- verified fuzzy39
- unresolved24
- parse uncertain0
- malformed identifier1
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 22eb0f48-b7a4-436d-86b4-1bac1f33283d · outbound
This paper cites Syn- thetic boost: Leveraging synthetic data for enhanced vision- language segmentation in echocardiography.
TAGS: 3D Tumor-Adaptive Guidance for SAM Syn- thetic boost: Leveraging synthetic data for enhanced vision- language segmentation in echocardiography
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T15:28:52.301945Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 273c7684-5595-44cb-8ea6-73e5095270ed · outbound
This paper cites The medical segmentation decathlon.Nature communications, 13(1):4128, 2022.
TAGS: 3D Tumor-Adaptive Guidance for SAM The medical segmentation decathlon.Nature communications, 13(1):4128, 2022
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T15:28:51.987322Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation d814d936-aa77-4c6c-af24-ea71ef24b7a9 · outbound
This paper cites The liver tumor segmentation benchmark (lits).
TAGS: 3D Tumor-Adaptive Guidance for SAM The liver tumor segmentation benchmark (lits)
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T15:28:51.687441Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation a70c84af-4c2d-4226-ac99-feb05fc6c834 · outbound
This paper cites Sam3d: Segment anything model in volumetric medical images.
TAGS: 3D Tumor-Adaptive Guidance for SAM Sam3d: Segment anything model in volumetric medical images
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T15:28:51.438445Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation a82d52e2-2f84-456b-908d-43a932a722ae · outbound
This paper cites MONAI: An open-source framework for deep learning in healthcare.
TAGS: 3D Tumor-Adaptive Guidance for SAM MONAI: An open-source framework for deep learning in healthcare
Reference 5
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T15:28:33.551571Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 749c5dcd-8938-47f4-bd85-d2d5f0831fc2 · outbound
This paper cites Ma-sam: Modality-agnostic sam adap- tation for 3d medical image segmentation.
TAGS: 3D Tumor-Adaptive Guidance for SAM Ma-sam: Modality-agnostic sam adap- tation for 3d medical image segmentation
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T15:28:51.132949Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation cc40dc72-cb8e-49ee-aba3-f04d76d9a855 · outbound
This paper cites Transunet: Rethinking the u-net architec- ture design for medical image segmentation through the lens of transformers.
TAGS: 3D Tumor-Adaptive Guidance for SAM Transunet: Rethinking the u-net architec- ture design for medical image segmentation through the lens of transformers
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T15:28:50.866038Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation e30660cd-a076-40ba-89cf-c8e894089cc3 · outbound
This paper cites SAM-Med2D.
Reference 9
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T15:28:34.079785Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 39021afd-5637-40b1-a934-02295feffa95 · outbound
This paper cites Orgunetr: Utilizing organ information and squeeze and exci- tation block for improved tumor segmentation.IEEE Access,.
TAGS: 3D Tumor-Adaptive Guidance for SAM Orgunetr: Utilizing organ information and squeeze and exci- tation block for improved tumor segmentation.IEEE Access,
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T15:28:50.696655Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 5ad61655-1585-4822-827e-e78b2c3e11a9 · outbound
This paper cites 3d u-net: learn- ing dense volumetric segmentation from sparse annota- tion.
TAGS: 3D Tumor-Adaptive Guidance for SAM 3d u-net: learn- ing dense volumetric segmentation from sparse annota- tion
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T15:28:50.471270Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 2603aeb8-7ba2-475e-a5a2-a718b63cc248 · outbound
This paper cites Clip-art: Contrastive pre-training for fine-grained art classification.
TAGS: 3D Tumor-Adaptive Guidance for SAM Clip-art: Contrastive pre-training for fine-grained art classification
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T15:28:50.165414Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation e65c1a70-d689-40cf-8bf2-951a8149cd6c · outbound
This paper cites Segment Anything Model (SAM) for Digital Pathology: Assess Zero-shot Segmentation on Whole Slide Imaging.
TAGS: 3D Tumor-Adaptive Guidance for SAM Segment Anything Model (SAM) for Digital Pathology: Assess Zero-shot Segmentation on Whole Slide Imaging
Reference 13
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T15:28:34.598403Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 86495708-2f57-416e-81a9-10a6bb6e5acb · outbound
This paper cites Bert: Pre-training of deep bidirectional trans- formers for language understanding.
TAGS: 3D Tumor-Adaptive Guidance for SAM Bert: Pre-training of deep bidirectional trans- formers for language understanding
Reference 14
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T15:28:34.738206Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5da5aec9-82d3-44a0-9b87-b99c694d53a5 · outbound
This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.
TAGS: 3D Tumor-Adaptive Guidance for SAM An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Reference 15
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T15:28:34.828700Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b0729514-09da-412c-b7ca-22cccf8fcaab · outbound
This paper cites SegVol: Universal and Interactive Volumetric Medical Image Segmentation.
TAGS: 3D Tumor-Adaptive Guidance for SAM SegVol: Universal and Interactive Volumetric Medical Image Segmentation
Reference 16
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T15:28:34.979625Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a9c0e815-e8a8-47dd-831c-2cfcead13f37 · outbound
This paper cites 3dsam-adapter: Holistic adaptation of sam from 2d to 3d for promptable tumor segmentation.
TAGS: 3D Tumor-Adaptive Guidance for SAM 3dsam-adapter: Holistic adaptation of sam from 2d to 3d for promptable tumor segmentation
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T15:28:49.897628Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 7fac231f-0750-4c29-9dab-f00bb4e6ed29 · outbound
This paper cites A foundation model utilizing chest ct volumes and radiology reports for supervised-level zero- shot detection of abnormalities.
TAGS: 3D Tumor-Adaptive Guidance for SAM A foundation model utilizing chest ct volumes and radiology reports for supervised-level zero- shot detection of abnormalities
Reference 18
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T15:28:35.265324Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7264bfa2-b4d9-41be-9986-bff4cb5666d1 · outbound
This paper cites Unetr: Transformers for 3d med- ical image segmentation.
TAGS: 3D Tumor-Adaptive Guidance for SAM Unetr: Transformers for 3d med- ical image segmentation
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T15:28:49.598958Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 539fc36a-d882-4961-8cae-b30768358737 · outbound
This paper cites The state of the art in kidney and kidney tumor segmentation in contrast-enhanced ct imaging: Results of the kits19 challenge.
TAGS: 3D Tumor-Adaptive Guidance for SAM The state of the art in kidney and kidney tumor segmentation in contrast-enhanced ct imaging: Results of the kits19 challenge
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T15:28:49.359522Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 45539148-4988-4bd7-81aa-b5ed78b11f85 · outbound
This paper cites When SAM Meets Medical Images: An Investigation of Segment Anything Model (SAM) on Multi-phase Liver Tumor Segmentation.
TAGS: 3D Tumor-Adaptive Guidance for SAM When SAM Meets Medical Images: An Investigation of Segment Anything Model (SAM) on Multi-phase Liver Tumor Segmentation
Reference 21
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T15:28:35.656427Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 27a31df9-7a0c-4160-8697-28a6cbaa820f · outbound
This paper cites Adapting visual-language models for generalizable anomaly detection in medical im- ages.
TAGS: 3D Tumor-Adaptive Guidance for SAM Adapting visual-language models for generalizable anomaly detection in medical im- ages
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T15:28:49.150986Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 6524dbf2-08a3-4bbe-8eed-afb8a714949c · outbound
This paper cites nnu-net: a self-configuring method for deep learning-based biomedical image segmen- tation.
TAGS: 3D Tumor-Adaptive Guidance for SAM nnu-net: a self-configuring method for deep learning-based biomedical image segmen- tation
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T15:28:49.008094Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation e9b0f0f3-8ba8-4ba6-8747-904656e42ef6 · outbound
This paper cites Winclip: Zero- /few-shot anomaly classification and segmentation.
TAGS: 3D Tumor-Adaptive Guidance for SAM Winclip: Zero- /few-shot anomaly classification and segmentation
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T15:28:48.844933Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation fa6fb4f9-1624-4bfc-a923-6b9600a1bb88 · outbound
This paper cites an unresolved cited work.
TAGS: 3D Tumor-Adaptive Guidance for SAM Unresolved cited work
Reference 25
- Resolution
- unresolved
- raw_fallback, observed 2026-08-07T15:28:48.632860Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation c53d82eb-c74d-4872-80be-13f18ef6a42b · outbound
This paper cites Segment any- thing.
TAGS: 3D Tumor-Adaptive Guidance for SAM Segment any- thing
Reference 26
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T15:28:36.335153Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation de87fd7b-7539-4bb0-bd78-1968bc584d29 · outbound
This paper cites MedCLIP-SAM: Bridging Text and Image Towards Universal Medical Image Segmentation.
TAGS: 3D Tumor-Adaptive Guidance for SAM MedCLIP-SAM: Bridging Text and Image Towards Universal Medical Image Segmentation
Reference 27
- Resolution
- verified exact
- local_arxiv, observed 2026-08-07T15:28:41.976865Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation a326f3f4-899b-4972-8dd7-2b538d1362fd · outbound
This paper cites 3D UX-Net: A Large Kernel Volumetric ConvNet Modernizing Hierarchical Transformer for Medical Image Segmentation.
TAGS: 3D Tumor-Adaptive Guidance for SAM 3D UX-Net: A Large Kernel Volumetric ConvNet Modernizing Hierarchical Transformer for Medical Image Segmentation
Reference 28
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T15:28:36.550194Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f0bde4b3-b6e6-4c62-94a4-278229ae5196 · outbound
This paper cites Medlsam: Localize and segment anything model for 3d ct images.
TAGS: 3D Tumor-Adaptive Guidance for SAM Medlsam: Localize and segment anything model for 3d ct images
Reference 29
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T15:28:48.433039Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 8f76acdc-9e90-419c-bd70-d3c7f24f29cd · outbound
This paper cites ClipSAM: CLIP and SAM Collaboration for Zero-Shot Anomaly Segmentation.
TAGS: 3D Tumor-Adaptive Guidance for SAM ClipSAM: CLIP and SAM Collaboration for Zero-Shot Anomaly Segmentation
Reference 31
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T15:28:36.790692Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9056c7c8-9605-4a3f-927b-7220cbfa036c · outbound
This paper cites Text-guided foundation model adaptation for long- tailed medical image classification.
TAGS: 3D Tumor-Adaptive Guidance for SAM Text-guided foundation model adaptation for long- tailed medical image classification
Reference 32
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T15:28:48.284937Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 05d4e9e4-3ff0-4f0a-8dfd-fa56046fd6bf · outbound
This paper cites Focal loss for dense object detection.
TAGS: 3D Tumor-Adaptive Guidance for SAM Focal loss for dense object detection
Reference 33
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T15:28:48.121421Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 88d8c9c0-d60b-46f8-8826-e9ac03d9ad07 · outbound
This paper cites Clip-driven universal model for organ segmentation and tumor detection.
TAGS: 3D Tumor-Adaptive Guidance for SAM Clip-driven universal model for organ segmentation and tumor detection
Reference 34
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T15:28:47.908853Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation c4d7a311-23d0-4dd2-83b1-3b7531e0a175 · outbound
This paper cites Segment anything in medical images.
TAGS: 3D Tumor-Adaptive Guidance for SAM Segment anything in medical images
Reference 35
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T15:28:47.647371Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 24943323-8874-439c-858b-080cc015f2aa · outbound
This paper cites Crepe: Can vision-language foundation models reason compositionally? In Proceedings of the IEEE/CVF Conference on Computer Vision and Pat- tern Recognition, pages 10910–10921, 2023.
TAGS: 3D Tumor-Adaptive Guidance for SAM Crepe: Can vision-language foundation models reason compositionally? In Proceedings of the IEEE/CVF Conference on Computer Vision and Pat- tern Recognition, pages 10910–10921, 2023
Reference 36
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T15:28:47.376092Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation de56b015-f20c-4553-8cfe-15cbe2860b8d · outbound
This paper cites Segment anything model for medical image analysis: an experimental study.
TAGS: 3D Tumor-Adaptive Guidance for SAM Segment anything model for medical image analysis: an experimental study
Reference 37
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T15:28:37.418407Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a9a72de3-1b9b-4fcf-86a8-4c82f6231b95 · outbound
This paper cites V-net: Fully convolutional neural networks for volumetric medical image segmentation.
TAGS: 3D Tumor-Adaptive Guidance for SAM V-net: Fully convolutional neural networks for volumetric medical image segmentation
Reference 38
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T15:28:47.168627Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation c9484394-e55b-4cc4-8999-0b084a7323b7 · outbound
This paper cites A guide to combat har- monization of imaging biomarkers in multicenter studies.
TAGS: 3D Tumor-Adaptive Guidance for SAM A guide to combat har- monization of imaging biomarkers in multicenter studies
Reference 39
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T15:28:46.954247Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 6369cd25-933a-4bff-950d-036b67bf6d30 · outbound
This paper cites Optimizing synthetic data for enhanced pan- creatic tumor segmentation.
TAGS: 3D Tumor-Adaptive Guidance for SAM Optimizing synthetic data for enhanced pan- creatic tumor segmentation
Reference 40
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T15:28:46.778873Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation f2bc9388-e38d-4cee-b682-adc494cbf879 · outbound
This paper cites Exploring Transfer Learning in Medical Image Segmentation using Vision-Language Models.
TAGS: 3D Tumor-Adaptive Guidance for SAM Exploring Transfer Learning in Medical Image Segmentation using Vision-Language Models
Reference 41
- Resolution
- verified exact
- local_arxiv, observed 2026-08-07T15:28:41.751269Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 9c7a14c5-998b-478f-86c9-1431667d8e4f · outbound
This paper cites Learning transferable visual models from natural language supervi- sion.
TAGS: 3D Tumor-Adaptive Guidance for SAM Learning transferable visual models from natural language supervi- sion
Reference 42
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T15:28:38.002996Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7087c95d-b7be-4677-a315-ca43224e25a2 · outbound
This paper cites SAM 2: Segment Anything in Images and Videos.
TAGS: 3D Tumor-Adaptive Guidance for SAM SAM 2: Segment Anything in Images and Videos
Reference 43
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T15:28:38.152174Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ee01fa03-a12d-4382-bbbd-43742591a09a · outbound
This paper cites U- net: Convolutional networks for biomedical image segmen- tation.
TAGS: 3D Tumor-Adaptive Guidance for SAM U- net: Convolutional networks for biomedical image segmen- tation
Reference 44
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T15:28:38.293847Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1df0db1c-0323-40f3-8fb0-86e1d16404fa · outbound
This paper cites Laion-5b: An open large-scale dataset for training next generation image-text models.
TAGS: 3D Tumor-Adaptive Guidance for SAM Laion-5b: An open large-scale dataset for training next generation image-text models
Reference 45
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T15:28:38.461908Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6f1fe0e4-59e0-4d7f-a821-47c023314fdd · outbound
This paper cites Is SAM 2 Better than SAM in Medical Image Segmentation?.
TAGS: 3D Tumor-Adaptive Guidance for SAM Is SAM 2 Better than SAM in Medical Image Segmentation?
Reference 46
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T15:28:38.574414Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation adf9f013-97c1-42ae-b90f-1f75579def3e · outbound
This paper cites Unetr++: delving into efficient and accurate 3d medical image segmentation.
TAGS: 3D Tumor-Adaptive Guidance for SAM Unetr++: delving into efficient and accurate 3d medical image segmentation
Reference 47
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T15:28:46.462843Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 6a0bb027-50d0-47a5-a5b3-446eca0b8dfb · outbound
This paper cites Self-supervised pre-training of swin trans- formers for 3d medical image analysis.
TAGS: 3D Tumor-Adaptive Guidance for SAM Self-supervised pre-training of swin trans- formers for 3d medical image analysis
Reference 48
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T15:28:46.191559Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 9f041a53-7f5d-4e19-861a-a590edcf9809 · outbound
This paper cites Yfcc100m: The new data in multimedia research.
TAGS: 3D Tumor-Adaptive Guidance for SAM Yfcc100m: The new data in multimedia research
Reference 49
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T15:28:39.039534Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9660b1e3-66b3-49f4-9528-fb1f0ad76d5a · outbound
This paper cites Attention is all you need.
TAGS: 3D Tumor-Adaptive Guidance for SAM Attention is all you need
Reference 50
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T15:28:39.237958Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation dea6bf84-559a-4527-b1ed-336701c201c3 · outbound
This paper cites Integrated treatment planning in percutaneous microwave ablation of lung tumors.
TAGS: 3D Tumor-Adaptive Guidance for SAM Integrated treatment planning in percutaneous microwave ablation of lung tumors
Reference 51
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T15:28:45.880127Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 081c92a8-7204-4508-bcd5-e0df120b33ee · outbound
This paper cites Sam-med3d: Towards general-purpose seg- mentation models for volumetric medical images, 2024.
TAGS: 3D Tumor-Adaptive Guidance for SAM Sam-med3d: Towards general-purpose seg- mentation models for volumetric medical images, 2024
Reference 52
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T15:28:45.585466Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation dda1f367-fdf6-4951-8f12-3b941c7a7dda · outbound
This paper cites Joint learning of 3d lesion segmentation and classifica- tion for explainable covid-19 diagnosis.
TAGS: 3D Tumor-Adaptive Guidance for SAM Joint learning of 3d lesion segmentation and classifica- tion for explainable covid-19 diagnosis
Reference 53
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T15:28:45.326228Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 59ecfdcd-2490-442f-8fd7-23197a2773fc · outbound
This paper cites Medclip: Contrastive learning from unpaired medical images and text, 2022.
TAGS: 3D Tumor-Adaptive Guidance for SAM Medclip: Contrastive learning from unpaired medical images and text, 2022
Reference 54
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T15:28:45.026608Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 1aedc398-d3f2-4814-898b-6a4d719a5e2c · outbound
This paper cites To- talsegmentator: robust segmentation of 104 anatomic struc- tures in ct images.
TAGS: 3D Tumor-Adaptive Guidance for SAM To- talsegmentator: robust segmentation of 104 anatomic struc- tures in ct images
Reference 55
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T15:28:39.894988Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 528dd570-f869-456f-9ffc-20dc64cbf566 · outbound
This paper cites Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation.
TAGS: 3D Tumor-Adaptive Guidance for SAM Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation
Reference 56
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T15:28:40.023093Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 47446912-71b4-48d3-a9b9-b5a7a13eb870 · outbound
This paper cites Cotr: Efficiently bridging cnn and transformer for 3d medi- cal image segmentation.
TAGS: 3D Tumor-Adaptive Guidance for SAM Cotr: Efficiently bridging cnn and transformer for 3d medi- cal image segmentation
Reference 57
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T15:28:44.758373Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation bf1eefcd-93af-4b65-b57a-22b39a074c61 · outbound
This paper cites Uniseg: A prompt-driven universal segmenta- tion model as well as a strong representation learner.
TAGS: 3D Tumor-Adaptive Guidance for SAM Uniseg: A prompt-driven universal segmenta- tion model as well as a strong representation learner
Reference 58
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T15:28:44.478459Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 1330ee84-dbf9-4cf5-a05f-2828e0a05ca2 · outbound
This paper cites Florence: A New Foundation Model for Computer Vision.
TAGS: 3D Tumor-Adaptive Guidance for SAM Florence: A New Foundation Model for Computer Vision
Reference 59
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T15:28:40.444132Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 2d932f83-3949-490b-b35a-76f89176ecad · outbound
This paper cites Scaling vision transformers.
TAGS: 3D Tumor-Adaptive Guidance for SAM Scaling vision transformers
Reference 60
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T15:28:44.263762Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 77411d22-045a-4633-8a15-3609a11c4f7d · outbound
This paper cites Large-Scale Multi-Center CT and MRI Segmentation of Pancreas with Deep Learning.
TAGS: 3D Tumor-Adaptive Guidance for SAM Large-Scale Multi-Center CT and MRI Segmentation of Pancreas with Deep Learning
Reference 61
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T15:28:40.673458Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ff2f6e1a-31a6-41c7-9956-450d73f70ad1 · outbound
This paper cites Torr, and Li Zhang.
TAGS: 3D Tumor-Adaptive Guidance for SAM Torr, and Li Zhang
Reference 62
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T15:28:44.053311Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 008a3de3-cf81-45bf-b1bf-da9c0cce6212 · outbound
This paper cites Zegclip: Towards adapting clip for zero-shot se- mantic segmentation.
TAGS: 3D Tumor-Adaptive Guidance for SAM Zegclip: Towards adapting clip for zero-shot se- mantic segmentation
Reference 63
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T15:28:43.735225Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation fbef250f-b3e9-40d4-b684-9940dd294601 · outbound
This paper cites Segment everything everywhere all at once.
TAGS: 3D Tumor-Adaptive Guidance for SAM Segment everything everywhere all at once
Reference 64
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T15:28:43.391928Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation b6e6cf6f-7f30-4912-9a76-813dda2de8b8 · outbound
This paper cites The KiTS dataset [20] originates from the MICCAI 2021 Kid- ney and Kidney Tumor Segmentation Challenge.
TAGS: 3D Tumor-Adaptive Guidance for SAM The KiTS dataset [20] originates from the MICCAI 2021 Kid- ney and Kidney Tumor Segmentation Challenge
Reference 65
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T15:28:43.134936Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 86be9c95-47fb-44ab-9abc-c124ba2a6c5a · outbound
This paper cites Our pre-processing pipeline follows the approach in [17].
TAGS: 3D Tumor-Adaptive Guidance for SAM Our pre-processing pipeline follows the approach in [17]
Reference 66
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T15:28:42.764452Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 19ec40af-3572-48d1-a921-3955d6525191 · outbound
This paper cites As a supplement to Fig.
TAGS: 3D Tumor-Adaptive Guidance for SAM As a supplement to Fig
Reference 67
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T15:28:42.562928Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 616afd4b-d7b3-4b95-a49d-c929a432f0fc · outbound
This paper cites sin- gle alignment adapter ablation experiment.
TAGS: 3D Tumor-Adaptive Guidance for SAM sin- gle alignment adapter ablation experiment
Reference 68
- Resolution
- malformed identifier
- raw_fallback, observed 2026-08-07T15:28:42.250515Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Pith citing papers
No inbound Pith citation observations are available.