Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T18:24:55.050376Z
Paper Citation Record · LEDGER
F3-Net: Foundation Model for Full Abnormality Segmentation of Medical Images with Flexible Input Modality Requirement
As of 9 August 2026, this Paper Citation Record lists 57 of 57 outbound references and 0 inbound Pith citation observations for arXiv:2507.08460.
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 2026-08-08T06:32:00.761636+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
57 of 57 outbound references displayed
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External citation measurements
No source-named external measurement is stored.
Outbound references
Observation e33320e6-a41b-491a-bf5e-e7d4cecae3f2 · outbound
This paper cites Clinical impact of deep learning reconstruction in mri,.
F3-Net: Foundation Model for Full Abnormality Segmentation of Medical Images with Flexible Input Modality Requirement Clinical impact of deep learning reconstruction in mri,
Reference 1
- Resolution
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Source-reported events for the cited work
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Observation 0703c6e1-1ac8-4721-8d29-c2e206957677 · outbound
This paper cites Deep learning for image en- hancement and correction in magnetic resonance imaging—state-of-the-art and challenges,.
F3-Net: Foundation Model for Full Abnormality Segmentation of Medical Images with Flexible Input Modality Requirement Deep learning for image en- hancement and correction in magnetic resonance imaging—state-of-the-art and challenges,
Reference 2
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Source-reported events for the cited work
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Observation b315f533-bc67-4cef-aed0-ae5adfd5efdf · outbound
This paper cites Narrative review of generative adversarial networks in medical and molecular imaging,.
F3-Net: Foundation Model for Full Abnormality Segmentation of Medical Images with Flexible Input Modality Requirement Narrative review of generative adversarial networks in medical and molecular imaging,
Reference 3
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation a83a2b3e-9b9c-4109-8acb-c44e5283ebad · outbound
This paper cites Foundation Models for Biomedical Image Segmentation: A Survey.
F3-Net: Foundation Model for Full Abnormality Segmentation of Medical Images with Flexible Input Modality Requirement Foundation Models for Biomedical Image Segmentation: A Survey
Reference 4
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Source-reported events for the cited work
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Observation f9ebc1c1-4629-4a9c-aecb-205ab6faab71 · outbound
This paper cites On the challenges and perspectives of foundation models for medical image analysis,.
F3-Net: Foundation Model for Full Abnormality Segmentation of Medical Images with Flexible Input Modality Requirement On the challenges and perspectives of foundation models for medical image analysis,
Reference 5
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation da2d279c-bf5b-4eb1-ac43-0d2ec5b3ba52 · outbound
This paper cites Multi-encoder nnU-Net outperforms transformer models with self-supervised pretraining.
F3-Net: Foundation Model for Full Abnormality Segmentation of Medical Images with Flexible Input Modality Requirement Multi-encoder nnU-Net outperforms transformer models with self-supervised pretraining
Reference 6
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 91e69941-6e95-467c-9d8c-6c6d3b3fb62c · outbound
This paper cites Medical SAM 2: Segment medical images as video via Segment Anything Model 2.
F3-Net: Foundation Model for Full Abnormality Segmentation of Medical Images with Flexible Input Modality Requirement Medical SAM 2: Segment medical images as video via Segment Anything Model 2
Reference 7
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation bf353d53-f788-4ef1-a9b3-83e921423cb9 · outbound
This paper cites Foundation AI Model for Medical Image Segmentation.
F3-Net: Foundation Model for Full Abnormality Segmentation of Medical Images with Flexible Input Modality Requirement Foundation AI Model for Medical Image Segmentation
Reference 8
- Resolution
- verified exact
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 1fc3a649-9b6f-486a-9a7d-a7f80349f7fa · outbound
This paper cites Generative Adversarial Networks (GAN) Powered Fast Magnetic Resonance Imaging -- Mini Review, Comparison and Perspectives.
F3-Net: Foundation Model for Full Abnormality Segmentation of Medical Images with Flexible Input Modality Requirement Generative Adversarial Networks (GAN) Powered Fast Magnetic Resonance Imaging -- Mini Review, Comparison and Perspectives
Reference 9
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation d86b01d1-130e-41bd-a1ec-c1846e2f23af · outbound
This paper cites GANs for Medical Image Synthesis: An Empirical Study.
F3-Net: Foundation Model for Full Abnormality Segmentation of Medical Images with Flexible Input Modality Requirement GANs for Medical Image Synthesis: An Empirical Study
Reference 10
- Resolution
- verified exact
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 1a857339-02d4-40f3-abf3-0e44d4530e21 · outbound
This paper cites Generative Adversarial Networks for Brain Images Synthesis: A Review.
F3-Net: Foundation Model for Full Abnormality Segmentation of Medical Images with Flexible Input Modality Requirement Generative Adversarial Networks for Brain Images Synthesis: A Review
Reference 11
- Resolution
- verified exact
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation e1d9e195-5c38-4e40-a892-7ee5ddfd105a · outbound
This paper cites Generative adversarial networks: A primer for radiologists,.
F3-Net: Foundation Model for Full Abnormality Segmentation of Medical Images with Flexible Input Modality Requirement Generative adversarial networks: A primer for radiologists,
Reference 12
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 58ee4bd3-8a82-4d87-b660-153f2488e537 · outbound
This paper cites VIS-MAE: An Efficient Self-supervised Learning Approach on Medical Image Segmentation and Classification.
F3-Net: Foundation Model for Full Abnormality Segmentation of Medical Images with Flexible Input Modality Requirement VIS-MAE: An Efficient Self-supervised Learning Approach on Medical Image Segmentation and Classification
Reference 13
- Resolution
- verified exact
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 8de5da93-f805-4709-86be-17ef29e0419c · outbound
This paper cites Ensembles of multiple models and architectures for robust brain tumour segmentation,.
F3-Net: Foundation Model for Full Abnormality Segmentation of Medical Images with Flexible Input Modality Requirement Ensembles of multiple models and architectures for robust brain tumour segmentation,
Reference 14
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation b8adaea9-80a1-4f9c-8595-de824381bc2d · outbound
This paper cites nnu-net: a self-configuring method for deep learning-based biomedical image segmentation,.
F3-Net: Foundation Model for Full Abnormality Segmentation of Medical Images with Flexible Input Modality Requirement nnu-net: a self-configuring method for deep learning-based biomedical image segmentation,
Reference 15
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation fc43496d-1dc7-49a4-93b9-131df2a3f3fa · outbound
This paper cites Unetr: Transformers for 3d medical image segmentation,.
F3-Net: Foundation Model for Full Abnormality Segmentation of Medical Images with Flexible Input Modality Requirement Unetr: Transformers for 3d medical image segmentation,
Reference 16
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation d64cb5de-cc4d-4f68-9c8f-03bda5bfe386 · outbound
This paper cites Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images.
F3-Net: Foundation Model for Full Abnormality Segmentation of Medical Images with Flexible Input Modality Requirement Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images
Reference 17
- Resolution
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Source-reported events for the cited work
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Observation 68308734-583b-4ceb-8585-e5af55f149de · outbound
This paper cites MedNeXt: Transformer-driven Scaling of ConvNets for Medical Image Segmentation.
F3-Net: Foundation Model for Full Abnormality Segmentation of Medical Images with Flexible Input Modality Requirement MedNeXt: Transformer-driven Scaling of ConvNets for Medical Image Segmentation
Reference 18
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 658ef207-49e7-4314-b4b0-2cc3bbd8a035 · outbound
This paper cites Brats challenge 2023 & 2024 results summary,.
F3-Net: Foundation Model for Full Abnormality Segmentation of Medical Images with Flexible Input Modality Requirement Brats challenge 2023 & 2024 results summary,
Reference 19
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 17af2453-a462-46cc-9993-5869014ba6dd · outbound
This paper cites Visual Language Pretrained Multiple Instance Zero-Shot Transfer for Histopathology Images.
F3-Net: Foundation Model for Full Abnormality Segmentation of Medical Images with Flexible Input Modality Requirement Visual Language Pretrained Multiple Instance Zero-Shot Transfer for Histopathology Images
Reference 20
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 4b222166-6037-423a-a546-001079e02e37 · outbound
This paper cites Learning to exploit temporal structure for biomedical vision-language processing,.
F3-Net: Foundation Model for Full Abnormality Segmentation of Medical Images with Flexible Input Modality Requirement Learning to exploit temporal structure for biomedical vision-language processing,
Reference 21
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation dea3c2af-4d00-42ba-87c3-9e7e3fd6c007 · outbound
This paper cites Segment anything,.
F3-Net: Foundation Model for Full Abnormality Segmentation of Medical Images with Flexible Input Modality Requirement Segment anything,
Reference 22
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 5d9ddc70-19cc-4665-883d-575d36c69551 · outbound
This paper cites Segment anything in medical images,.
F3-Net: Foundation Model for Full Abnormality Segmentation of Medical Images with Flexible Input Modality Requirement Segment anything in medical images,
Reference 23
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 2418fc29-6dbb-478e-bd96-75bff8c9f49f · outbound
This paper cites Medlsam: Localize and segment anything model for 3d medical images,.
F3-Net: Foundation Model for Full Abnormality Segmentation of Medical Images with Flexible Input Modality Requirement Medlsam: Localize and segment anything model for 3d medical images,
Reference 24
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 5dfd2e6b-12d3-4e24-90fa-d286dcd6dd6f · outbound
This paper cites Diffractive lensing of nano-Hertz gravitational waves emitted from supermassive binary black holes by intervening galaxies.
F3-Net: Foundation Model for Full Abnormality Segmentation of Medical Images with Flexible Input Modality Requirement Diffractive lensing of nano-Hertz gravitational waves emitted from supermassive binary black holes by intervening galaxies
Reference 25
- Resolution
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Source-reported events for the cited work
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Observation afed41a6-44c8-4e78-a9d3-4c5206b1d733 · outbound
This paper cites Sam- med3d: towards general-purpose segmentation models for volumetric medical images,.
F3-Net: Foundation Model for Full Abnormality Segmentation of Medical Images with Flexible Input Modality Requirement Sam- med3d: towards general-purpose segmentation models for volumetric medical images,
Reference 26
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 08da5e77-cc0c-4dd4-8d13-259373874b21 · outbound
This paper cites Modality-agnostic medical image segmentation via unified representation learning,.
F3-Net: Foundation Model for Full Abnormality Segmentation of Medical Images with Flexible Input Modality Requirement Modality-agnostic medical image segmentation via unified representation learning,
Reference 27
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation e81e941a-a3e9-4c38-bc8d-6d93d8fe3c2c · outbound
This paper cites Generalizable medical image segmentation via modality-invariant latent space,.
F3-Net: Foundation Model for Full Abnormality Segmentation of Medical Images with Flexible Input Modality Requirement Generalizable medical image segmentation via modality-invariant latent space,
Reference 28
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 03a74fe8-44d0-4ce1-a64a-d5748c967d48 · outbound
This paper cites Magnitude and magnitude homology of filtered set enriched categories.
F3-Net: Foundation Model for Full Abnormality Segmentation of Medical Images with Flexible Input Modality Requirement Magnitude and magnitude homology of filtered set enriched categories
Reference 29
- Resolution
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Source-reported events for the cited work
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Observation 73f897bc-2c22-4312-a316-4f251e7871dd · outbound
This paper cites The first step for neuroimaging data analysis: Dicom to nifti conversion,.
F3-Net: Foundation Model for Full Abnormality Segmentation of Medical Images with Flexible Input Modality Requirement The first step for neuroimaging data analysis: Dicom to nifti conversion,
Reference 31
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation e797f1a1-6cb1-4efc-ac0b-6dea5451eb7a · outbound
This paper cites Ensembles of densely-connected cnns with label-uncertainty for brain tumor segmentation,.
F3-Net: Foundation Model for Full Abnormality Segmentation of Medical Images with Flexible Input Modality Requirement Ensembles of densely-connected cnns with label-uncertainty for brain tumor segmentation,
Reference 32
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 738bf60f-c440-4004-becf-767affe57b5d · outbound
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F3-Net: Foundation Model for Full Abnormality Segmentation of Medical Images with Flexible Input Modality Requirement Multi-scale 3d convolutional neural networks for lesion segmentation in brain mri,
Reference 33
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
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F3-Net: Foundation Model for Full Abnormality Segmentation of Medical Images with Flexible Input Modality Requirement Efficient multi-scale 3d cnn with fully connected crf for accurate brain lesion segmentation,
Reference 34
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 14e9b84c-8def-41b2-8aad-c1dce68297e0 · outbound
This paper cites Simultaneous truth and performance level estimation (staple): an algorithm for the validation of image segmentation,.
F3-Net: Foundation Model for Full Abnormality Segmentation of Medical Images with Flexible Input Modality Requirement Simultaneous truth and performance level estimation (staple): an algorithm for the validation of image segmentation,
Reference 35
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation c64aaef4-3ff9-4da3-8dc5-5caa8ae09dc5 · outbound
This paper cites The 2024 Brain Tumor Segmentation (BraTS) Challenge: Glioma Segmentation on Post-treatment MRI.
F3-Net: Foundation Model for Full Abnormality Segmentation of Medical Images with Flexible Input Modality Requirement The 2024 Brain Tumor Segmentation (BraTS) Challenge: Glioma Segmentation on Post-treatment MRI
Reference 36
- Resolution
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Source-reported events for the cited work
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Observation 27657f65-5f3d-4ddc-8a09-def98b6b069b · outbound
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F3-Net: Foundation Model for Full Abnormality Segmentation of Medical Images with Flexible Input Modality Requirement Automated brain extraction of multisequence mri using artificial neural networks,
Reference 37
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3f3309de-5b3e-4265-b504-ee0a157e461a · outbound
This paper cites Isles 2022: A multi-center magnetic resonance imaging stroke lesion segmentation dataset,.
F3-Net: Foundation Model for Full Abnormality Segmentation of Medical Images with Flexible Input Modality Requirement Isles 2022: A multi-center magnetic resonance imaging stroke lesion segmentation dataset,
Reference 38
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 5105402c-867e-42ef-b003-5eb50ae80a10 · outbound
This paper cites Data of the White Matter Hyperintensity (WMH) Segmentation Challenge,.
F3-Net: Foundation Model for Full Abnormality Segmentation of Medical Images with Flexible Input Modality Requirement Data of the White Matter Hyperintensity (WMH) Segmentation Challenge,
Reference 39
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 2a69e86c-8a78-40ef-9bb8-eab939bd3b06 · outbound
This paper cites Shifts 2.0: Extending The Dataset of Real Distributional Shifts.
F3-Net: Foundation Model for Full Abnormality Segmentation of Medical Images with Flexible Input Modality Requirement Shifts 2.0: Extending The Dataset of Real Distributional Shifts
Reference 40
- Resolution
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Source-reported events for the cited work
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Observation 6f97d98c-2770-490b-bca7-22036bc6c83c · outbound
This paper cites Brain tumor segmentation on mri with missing modalities,.
F3-Net: Foundation Model for Full Abnormality Segmentation of Medical Images with Flexible Input Modality Requirement Brain tumor segmentation on mri with missing modalities,
Reference 41
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 75cc7a1e-6bb1-4e4c-a42f-170e6bb94752 · outbound
This paper cites Deep learning-based imaging data completion for improved brain disease diagnosis,.
F3-Net: Foundation Model for Full Abnormality Segmentation of Medical Images with Flexible Input Modality Requirement Deep learning-based imaging data completion for improved brain disease diagnosis,
Reference 42
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 6185e499-eb50-4af4-b572-f698e24e3ae0 · outbound
This paper cites Brain tumor segmentation with deep neural networks,.
F3-Net: Foundation Model for Full Abnormality Segmentation of Medical Images with Flexible Input Modality Requirement Brain tumor segmentation with deep neural networks,
Reference 43
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 1b6d34ca-2532-445e-99f6-46a2c5f6bd1c · outbound
This paper cites M3AE: Multimodal Representation Learning for Brain Tumor Segmentation with Missing Modalities.
F3-Net: Foundation Model for Full Abnormality Segmentation of Medical Images with Flexible Input Modality Requirement M3AE: Multimodal Representation Learning for Brain Tumor Segmentation with Missing Modalities
Reference 44
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T18:24:55.441799Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 2cdd8653-d80e-42dc-b79e-4cfb6d1f3aff · outbound
This paper cites Unified Multi-Modal Image Synthesis for Missing Modality Imputation.
F3-Net: Foundation Model for Full Abnormality Segmentation of Medical Images with Flexible Input Modality Requirement Unified Multi-Modal Image Synthesis for Missing Modality Imputation
Reference 45
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 75214cef-2aab-4b38-a41f-14934c2380c8 · outbound
This paper cites MRI Scan Synthesis Methods based on Clustering and Pix2Pix.
F3-Net: Foundation Model for Full Abnormality Segmentation of Medical Images with Flexible Input Modality Requirement MRI Scan Synthesis Methods based on Clustering and Pix2Pix
Reference 46
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T18:24:55.289354Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 0b3cd968-4bb6-45f3-b133-7c970b0d602a · outbound
This paper cites Multi-modal modality-masked diffusion network for brain mri synthesis with random modality missing,.
F3-Net: Foundation Model for Full Abnormality Segmentation of Medical Images with Flexible Input Modality Requirement Multi-modal modality-masked diffusion network for brain mri synthesis with random modality missing,
Reference 47
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:25:19.357156Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 37182a9a-f0fc-4474-ba1d-e9943bd06ba4 · outbound
This paper cites Highest resolution in vivo human brain mri using prospective motion correction,.
F3-Net: Foundation Model for Full Abnormality Segmentation of Medical Images with Flexible Input Modality Requirement Highest resolution in vivo human brain mri using prospective motion correction,
Reference 48
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:24:57.618042Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 62c70b99-1a2a-4d20-aae5-984b8caf8671 · outbound
This paper cites Swinbts: A method for 3d multimodal brain tumor segmentation using swin transformer,.
F3-Net: Foundation Model for Full Abnormality Segmentation of Medical Images with Flexible Input Modality Requirement Swinbts: A method for 3d multimodal brain tumor segmentation using swin transformer,
Reference 49
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:24:57.317355Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 945366ce-8de1-431c-91e6-fa8f6a8fe444 · outbound
This paper cites Seresu-net for multimodal brain tumor segmentation,.
F3-Net: Foundation Model for Full Abnormality Segmentation of Medical Images with Flexible Input Modality Requirement Seresu-net for multimodal brain tumor segmentation,
Reference 50
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:24:57.197455Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 660cec1f-2cc1-45ef-a033-c497593ed24c · outbound
This paper cites Factorised spatial representation learning: Application in semi-supervised my- ocardial segmentation,.
F3-Net: Foundation Model for Full Abnormality Segmentation of Medical Images with Flexible Input Modality Requirement Factorised spatial representation learning: Application in semi-supervised my- ocardial segmentation,
Reference 51
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:24:57.069848Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 48afd825-ef2d-46c9-9632-4544e62cf6ea · outbound
This paper cites Self-supervised 3D Patient Modeling with Multi-modal Attentive Fusion.
F3-Net: Foundation Model for Full Abnormality Segmentation of Medical Images with Flexible Input Modality Requirement Self-supervised 3D Patient Modeling with Multi-modal Attentive Fusion
Reference 52
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T18:24:55.206873Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation c6ce9426-913b-4b3c-a92e-2873231dd6ab · outbound
This paper cites Synthseg: Segmentation of brain mri scans of any contrast and resolution without retraining,.
F3-Net: Foundation Model for Full Abnormality Segmentation of Medical Images with Flexible Input Modality Requirement Synthseg: Segmentation of brain mri scans of any contrast and resolution without retraining,
Reference 53
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:24:56.933319Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation a4ddba44-ae36-4ada-84bb-b034ca1f0ee5 · outbound
This paper cites Deep generative adversarial networks: applications in muscu- loskeletal imaging,.
F3-Net: Foundation Model for Full Abnormality Segmentation of Medical Images with Flexible Input Modality Requirement Deep generative adversarial networks: applications in muscu- loskeletal imaging,
Reference 54
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:24:56.831419Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 4da10388-3b0b-40be-af49-03a653c76eb9 · outbound
This paper cites Medical image synthesis for data augmentation and anonymization using gans,.
F3-Net: Foundation Model for Full Abnormality Segmentation of Medical Images with Flexible Input Modality Requirement Medical image synthesis for data augmentation and anonymization using gans,
Reference 55
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:24:56.717917Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 523a0e7f-61f9-485c-ae7f-987dbf76f303 · outbound
This paper cites The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification.
F3-Net: Foundation Model for Full Abnormality Segmentation of Medical Images with Flexible Input Modality Requirement The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification
Reference 56
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T18:24:55.022082Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation db2fe2e7-2d75-4cd4-9d3a-7cdb1dcdefb2 · outbound
This paper cites Domain adaptation for medical image analysis: a survey,.
F3-Net: Foundation Model for Full Abnormality Segmentation of Medical Images with Flexible Input Modality Requirement Domain adaptation for medical image analysis: a survey,
Reference 57
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:24:56.602138Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 2d6ce8cc-b5c8-4a2b-b8f4-7ed935a6d2e3 · outbound
This paper cites Available: https://doi.org/10.34894/AECRSD.
F3-Net: Foundation Model for Full Abnormality Segmentation of Medical Images with Flexible Input Modality Requirement Available: https://doi.org/10.34894/AECRSD
Reference 2022
- Resolution
- malformed identifier
- no resolver link, observed 2026-08-06T18:24:53.825498Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Pith citing papers
No inbound Pith citation observations are available.