Paper Citation Record · LEDGER

Adaptive Interactive Segmentation for Multimodal Medical Imaging via Selection Engine

As of 21 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 2 inbound Pith citation observations for arXiv:2411.19447.

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

pith.paper-citation-record.v1
2411.19447 v1

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T10:14:24.357489Z

measured 41 of 41 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:21:37.304759Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T23:21:39.634755Z

Reference resolution

39 of 39 outbound references displayed

verified exact0
verified fuzzy25
unresolved14
parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1802777f-7360-4df5-9f0d-a1d7b01edfe6 · outbound

This paper cites nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation,.

Adaptive Interactive Segmentation for Multimodal Medical Imaging via Selection Engine nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-12T10:14:24.213612Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d4cdd1c6-66c2-4bcb-9f49-33bd21dc0b51 · outbound

This paper cites Clinically applicable deep learning for diagnosis and referral in retinal disease,.

Adaptive Interactive Segmentation for Multimodal Medical Imaging via Selection Engine Clinically applicable deep learning for diagnosis and referral in retinal disease,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:14:24.855068Z

Source-reported events for the cited work

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

Observation 56fedac1-04e6-4aae-9127-2bb8b3d317e1 · outbound

This paper cites Video-based AI for beat-to-beat assessment of cardiac function,.

Adaptive Interactive Segmentation for Multimodal Medical Imaging via Selection Engine Video-based AI for beat-to-beat assessment of cardiac function,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:14:24.844337Z

Source-reported events for the cited work

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

Observation aac44abe-95ef-4944-b786-504d0a633c81 · outbound

This paper cites DeepIGeoS: a deep interactive geodesic framework for medical image segmentation,.

Adaptive Interactive Segmentation for Multimodal Medical Imaging via Selection Engine DeepIGeoS: a deep interactive geodesic framework for medical image segmentation,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:14:24.833102Z

Source-reported events for the cited work

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Observation bb6213c3-f139-451c-88c6-f66e1a30a675 · outbound

This paper cites Segment anything,.

Adaptive Interactive Segmentation for Multimodal Medical Imaging via Selection Engine Segment anything,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:14:24.821339Z

Source-reported events for the cited work

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

Observation 44f561d4-05c4-459d-b0f5-5e0e6949194d · outbound

This paper cites Interactive medical image segmentation using deep learning with image-specific fine tuning,.

Adaptive Interactive Segmentation for Multimodal Medical Imaging via Selection Engine Interactive medical image segmentation using deep learning with image-specific fine tuning,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:14:24.809692Z

Source-reported events for the cited work

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

Observation ffe761d9-eef7-4a7d-91b4-308daf1e4e0b · outbound

This paper cites V ol- umetric memory network for interactive medical image segmentation,.

Adaptive Interactive Segmentation for Multimodal Medical Imaging via Selection Engine V ol- umetric memory network for interactive medical image segmentation,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:14:24.797033Z

Source-reported events for the cited work

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

Observation 25c4df40-2dbb-4587-a066-64de28d23ce1 · outbound

This paper cites MIDeepSeg: Minimally interactive seg- mentation of unseen objects from medical images using deep learning,.

Adaptive Interactive Segmentation for Multimodal Medical Imaging via Selection Engine MIDeepSeg: Minimally interactive seg- mentation of unseen objects from medical images using deep learning,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:14:24.784953Z

Source-reported events for the cited work

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Observation f11ea0de-2f7a-4a1a-b9d0-80b0ba927580 · outbound

This paper cites Segment anything in medical images,.

Adaptive Interactive Segmentation for Multimodal Medical Imaging via Selection Engine Segment anything in medical images,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:14:24.772465Z

Source-reported events for the cited work

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

Observation 83304bb9-0243-40a7-a65c-891fbc4a493f · outbound

This paper cites When SAM Meets Medical Images: An Investigation of Segment Anything Model (SAM) on Multi-phase Liver Tumor Segmentation.

Adaptive Interactive Segmentation for Multimodal Medical Imaging via Selection Engine When SAM Meets Medical Images: An Investigation of Segment Anything Model (SAM) on Multi-phase Liver Tumor Segmentation

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-12T10:14:24.247072Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 97553010-9f7a-4b60-8e40-a6dfc5f3d673 · outbound

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

Adaptive Interactive Segmentation for Multimodal Medical Imaging via Selection Engine SAM 2: Segment Anything in Images and Videos

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-12T10:14:24.250898Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 70b5bde7-4e36-4eb4-93cf-b88583fdc159 · outbound

This paper cites SAM on Medical Images: A Comprehensive Study on Three Prompt Modes.

Adaptive Interactive Segmentation for Multimodal Medical Imaging via Selection Engine SAM on Medical Images: A Comprehensive Study on Three Prompt Modes

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-12T10:14:24.254394Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a281be0d-ca68-42a5-a16e-251673f9170d · outbound

This paper cites Segment Anything Model (SAM) for Digital Pathology: Assess Zero-shot Segmentation on Whole Slide Imaging.

Adaptive Interactive Segmentation for Multimodal Medical Imaging via Selection Engine Segment Anything Model (SAM) for Digital Pathology: Assess Zero-shot Segmentation on Whole Slide Imaging

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-12T10:14:24.258706Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 450eeaf9-cecd-4d3c-bc09-e7054fb80cb8 · outbound

This paper cites SAM.MD: Zero-shot medical image segmentation capabilities of the Segment Anything Model.

Adaptive Interactive Segmentation for Multimodal Medical Imaging via Selection Engine SAM.MD: Zero-shot medical image segmentation capabilities of the Segment Anything Model

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-12T10:14:24.263360Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a545aea9-bdfe-4786-a084-af8c90aa5dce · outbound

This paper cites Ladder Fine-tuning approach for SAM integrating complementary network.

Adaptive Interactive Segmentation for Multimodal Medical Imaging via Selection Engine Ladder Fine-tuning approach for SAM integrating complementary network

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-12T10:14:24.267223Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6229ef8f-a0f9-42d1-8f5d-5303d1337028 · outbound

This paper cites Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation.

Adaptive Interactive Segmentation for Multimodal Medical Imaging via Selection Engine Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-12T10:14:24.271846Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 91e3bd76-b67f-485a-9dfc-1e7d55664dea · outbound

This paper cites A review of deep learning based methods for medical image multi-organ segmentation,.

Adaptive Interactive Segmentation for Multimodal Medical Imaging via Selection Engine A review of deep learning based methods for medical image multi-organ segmentation,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:14:24.760116Z

Source-reported events for the cited work

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

Observation 887db700-385f-4ac2-8261-1ae32c4a93cd · outbound

This paper cites Abdominal multi-organ segmentation with organ-attention networks and statistical fusion,.

Adaptive Interactive Segmentation for Multimodal Medical Imaging via Selection Engine Abdominal multi-organ segmentation with organ-attention networks and statistical fusion,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:14:24.747504Z

Source-reported events for the cited work

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

Observation 01b63972-495d-45cd-9b43-6c325e4bec59 · outbound

This paper cites Amos: A large-scale abdominal multi-organ benchmark for versatile medical image segmentation,.

Adaptive Interactive Segmentation for Multimodal Medical Imaging via Selection Engine Amos: A large-scale abdominal multi-organ benchmark for versatile medical image segmentation,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:14:24.733805Z

Source-reported events for the cited work

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

Observation 6bec1964-22fd-4c75-a289-2b99839e7e1f · outbound

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

Adaptive Interactive Segmentation for Multimodal Medical Imaging via Selection Engine U-net: Convolutional networks for biomedical image segmentation,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:14:24.722029Z

Source-reported events for the cited work

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Observation 0d59b261-e52e-4b9a-83a3-47db8b37b5ee · outbound

This paper cites ResUNet-a: A deep learning framework for semantic segmentation of remotely sensed data,.

Adaptive Interactive Segmentation for Multimodal Medical Imaging via Selection Engine ResUNet-a: A deep learning framework for semantic segmentation of remotely sensed data,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:14:24.710432Z

Source-reported events for the cited work

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

Observation ade5731e-95fe-4a6b-b24c-3f9b277f6262 · outbound

This paper cites nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation.

Adaptive Interactive Segmentation for Multimodal Medical Imaging via Selection Engine nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-12T10:14:24.294463Z

Source-reported events for the cited work

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Observation 5864cf79-5912-4530-9b0a-1d09d8e6234c · outbound

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

Adaptive Interactive Segmentation for Multimodal Medical Imaging via Selection Engine Unet++: A nested u-net architecture for medical image segmentation,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:14:24.698684Z

Source-reported events for the cited work

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

Observation be5c3dd3-891d-4efe-9bed-3f9eae63a987 · outbound

This paper cites Deep learning based multi- modal biomedical data fusion: An overview and comparative review,.

Adaptive Interactive Segmentation for Multimodal Medical Imaging via Selection Engine Deep learning based multi- modal biomedical data fusion: An overview and comparative review,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:14:24.685735Z

Source-reported events for the cited work

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

Observation be567b4b-31c0-428a-87ba-cacf3b641041 · outbound

This paper cites Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models.

Adaptive Interactive Segmentation for Multimodal Medical Imaging via Selection Engine Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-12T10:14:24.305597Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d499b621-d5e5-4747-bab0-5e9cf04f1424 · outbound

This paper cites One-prompt to segment all medical images,.

Adaptive Interactive Segmentation for Multimodal Medical Imaging via Selection Engine One-prompt to segment all medical images,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:14:24.672367Z

Source-reported events for the cited work

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

Observation b1175eff-4634-46c1-bb97-520af829dc68 · outbound

This paper cites Medical SAM 2: Segment medical images as video via Segment Anything Model 2.

Adaptive Interactive Segmentation for Multimodal Medical Imaging via Selection Engine Medical SAM 2: Segment medical images as video via Segment Anything Model 2

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-12T10:14:24.312364Z

Source-reported events for the cited work

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Observation 11466cb5-f733-4df3-abd2-275400bb0361 · outbound

This paper cites Segment Anything in Medical Images and Videos: Benchmark and Deployment.

Adaptive Interactive Segmentation for Multimodal Medical Imaging via Selection Engine Segment Anything in Medical Images and Videos: Benchmark and Deployment

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-12T10:14:24.316236Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3cb02a5b-47d9-485b-b44e-aafc8d9d6bce · outbound

This paper cites PAPILA: Dataset with fundus images and clinical data of both eyes of the same patient for glaucoma assessment,.

Adaptive Interactive Segmentation for Multimodal Medical Imaging via Selection Engine PAPILA: Dataset with fundus images and clinical data of both eyes of the same patient for glaucoma assessment,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:14:24.659413Z

Source-reported events for the cited work

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

Observation 5f49c039-2fd6-4d6d-8c6f-f4611202f09b · outbound

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

Adaptive Interactive Segmentation for Multimodal Medical Imaging via Selection Engine BUS-BRA: A breast ultrasound dataset for assessing computer-aided diagnosis systems,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:14:24.646667Z

Source-reported events for the cited work

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

Observation a2a6cdcd-e420-436e-85a6-aa7790626ff2 · outbound

This paper cites Kvasir-SEG: A Segmented Polyp Dataset,.

Adaptive Interactive Segmentation for Multimodal Medical Imaging via Selection Engine Kvasir-SEG: A Segmented Polyp Dataset,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:14:24.634819Z

Source-reported events for the cited work

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

Observation 41a4d665-79e3-4e8a-9e8d-6e959d1783a7 · outbound

This paper cites Deep Learning Framework Design for Diabetic Retinopathy Abnormalities Classification,.

Adaptive Interactive Segmentation for Multimodal Medical Imaging via Selection Engine Deep Learning Framework Design for Diabetic Retinopathy Abnormalities Classification,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:14:24.621749Z

Source-reported events for the cited work

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

Observation 0a3c7309-b3f6-48b1-9775-3d6c83fdb541 · outbound

This paper cites Deep-Learning Ensembles for Skin-Lesion Segmentation, Analysis, Classification: RECOD Titans at ISIC Challenge 2018,.

Adaptive Interactive Segmentation for Multimodal Medical Imaging via Selection Engine Deep-Learning Ensembles for Skin-Lesion Segmentation, Analysis, Classification: RECOD Titans at ISIC Challenge 2018,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:14:24.608414Z

Source-reported events for the cited work

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

Observation bb90cb52-ace0-4cea-a3de-0b2d0ce6ced7 · outbound

This paper cites an unresolved cited work.

Adaptive Interactive Segmentation for Multimodal Medical Imaging via Selection Engine Unresolved cited work

Reference 34

Resolution: unresolved; raw_fallback, observed 2026-08-12T10:14:24.594475Z

Source-reported events for the cited work

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

Observation dc5d5902-18b4-49d7-b62b-578237316364 · outbound

This paper cites Categorized contrast enhanced mammography dataset for diagnostic and artificial intelligence research,.

Adaptive Interactive Segmentation for Multimodal Medical Imaging via Selection Engine Categorized contrast enhanced mammography dataset for diagnostic and artificial intelligence research,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:14:24.580433Z

Source-reported events for the cited work

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

Observation 74783bfe-9513-4318-b3c0-2556c2d66a0d · outbound

This paper cites CheXmask: a large- scale dataset of anatomical segmentation masks for multi-center chest x-ray images,.

Adaptive Interactive Segmentation for Multimodal Medical Imaging via Selection Engine CheXmask: a large- scale dataset of anatomical segmentation masks for multi-center chest x-ray images,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:14:24.566409Z

Source-reported events for the cited work

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

Observation c483674c-291b-4e1d-9e68-efd0f2d45a3f · outbound

This paper cites Automated measurement of fetal head circumference using 2D ultrasound images,.

Adaptive Interactive Segmentation for Multimodal Medical Imaging via Selection Engine Automated measurement of fetal head circumference using 2D ultrasound images,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:14:24.553386Z

Source-reported events for the cited work

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

Observation 6887054e-8dee-4546-a99d-08d061e0cc86 · outbound

This paper cites m2caiSeg: Semantic Segmentation of Laparoscopic Images using Convolutional Neural Networks.

Adaptive Interactive Segmentation for Multimodal Medical Imaging via Selection Engine m2caiSeg: Semantic Segmentation of Laparoscopic Images using Convolutional Neural Networks

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-12T10:14:24.353196Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3bfaa0ff-d9f8-4446-889e-99950878b5b8 · outbound

This paper cites Scikit-learn: Machine Learning in Python,.

Adaptive Interactive Segmentation for Multimodal Medical Imaging via Selection Engine Scikit-learn: Machine Learning in Python,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:14:24.539456Z

Source-reported events for the cited work

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

Pith citing papers

Observation 81a2854e-03f5-4ed9-a75a-d7132ac0e2a8 · inbound

SafeClick: Error-Tolerant Interactive Segmentation of Any Medical Volumes via Hierarchical Expert Consensus cites this paper.

SafeClick: Error-Tolerant Interactive Segmentation of Any Medical Volumes via Hierarchical Expert Consensus Adaptive Interactive Segmentation for Multimodal Medical Imaging via Selection Engine

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-08-06T23:21:39.704742Z

Source-reported events for the cited work

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

Observation 64890cdb-ad72-497d-9cd5-91185254053a · inbound

Implicit Shape-Prior for Few-Shot Assisted 3D Segmentation cites this paper.

Implicit Shape-Prior for Few-Shot Assisted 3D Segmentation Adaptive Interactive Segmentation for Multimodal Medical Imaging via Selection Engine

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-04T20:28:24.441970Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.