Paper Citation Record · LEDGER

FaRMamba: Frequency-based learning and Reconstruction aided Mamba for Medical Segmentation

As of 16 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 0 inbound Pith citation observations for arXiv:2507.20056.

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

pith.paper-citation-record.v1
2507.20056 v1

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measured 42 of 42 reference resolution

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Pith citing papers itemized under the disclosed page cap.

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Reference resolution

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Outbound references

Observation 21864900-3614-45c8-b608-109a4e75dd20 · outbound

This paper cites IEEE Transac- tionsonComputers C-23(1),90–93(1974).

FaRMamba: Frequency-based learning and Reconstruction aided Mamba for Medical Segmentation IEEE Transac- tionsonComputers C-23(1),90–93(1974)

Reference 1

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This paper cites In: Karlinsky, L., Michaeli, T., Nishino, K.

FaRMamba: Frequency-based learning and Reconstruction aided Mamba for Medical Segmentation In: Karlinsky, L., Michaeli, T., Nishino, K

Reference 2

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Observation 21af67c0-37b7-4b25-9692-af8adc6f1271 · outbound

This paper cites Machine Learning28(1), 41–75 (1997).

FaRMamba: Frequency-based learning and Reconstruction aided Mamba for Medical Segmentation Machine Learning28(1), 41–75 (1997)

Reference 3

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This paper cites In: Linguraru, M.G., Dou, Q., Feragen, A., Giannarou, S., Glocker, B., Lekadir, K., Schnabel, J.A.

FaRMamba: Frequency-based learning and Reconstruction aided Mamba for Medical Segmentation In: Linguraru, M.G., Dou, Q., Feragen, A., Giannarou, S., Glocker, B., Lekadir, K., Schnabel, J.A

Reference 4

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Observation b9e83d58-0de6-4c4a-af84-77f0f776958c · outbound

This paper cites TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation.

FaRMamba: Frequency-based learning and Reconstruction aided Mamba for Medical Segmentation TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

Reference 5

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Observation d01bdcd9-2672-49eb-b6b1-7a36f9a528d9 · outbound

This paper cites Medical Image Analysis58, 101539 (2019).https://doi.org/10.1016/j.media.

FaRMamba: Frequency-based learning and Reconstruction aided Mamba for Medical Segmentation Medical Image Analysis58, 101539 (2019).https://doi.org/10.1016/j.media

Reference 6

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This paper cites Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs.

FaRMamba: Frequency-based learning and Reconstruction aided Mamba for Medical Segmentation Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs

Reference 7

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This paper cites Domain Influence in MRI Medical Image Segmentation: spatial versus k-space inputs.

FaRMamba: Frequency-based learning and Reconstruction aided Mamba for Medical Segmentation Domain Influence in MRI Medical Image Segmentation: spatial versus k-space inputs

Reference 8

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FaRMamba: Frequency-based learning and Reconstruction aided Mamba for Medical Segmentation Unresolved cited work

Reference 9

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Observation 39a6f890-4438-44a3-a584-07898f7e32b9 · outbound

This paper cites Mamba: Linear-Time Sequence Modeling with Selective State Spaces.

FaRMamba: Frequency-based learning and Reconstruction aided Mamba for Medical Segmentation Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 10

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Observation 89ad5f44-5a20-40f0-85ef-faf8d3a5201c · outbound

This paper cites Efficiently Modeling Long Sequences with Structured State Spaces.

FaRMamba: Frequency-based learning and Reconstruction aided Mamba for Medical Segmentation Efficiently Modeling Long Sequences with Structured State Spaces

Reference 11

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Observation ffc3bb95-cb56-4fa8-a658-4d3fbe0a6a91 · outbound

This paper cites Medical and Biological En- gineering and Computing 42(2), 189–192 (2004).

FaRMamba: Frequency-based learning and Reconstruction aided Mamba for Medical Segmentation Medical and Biological En- gineering and Computing 42(2), 189–192 (2004)

Reference 12

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This paper cites In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

FaRMamba: Frequency-based learning and Reconstruction aided Mamba for Medical Segmentation In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Reference 13

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This paper cites MambaMIR: An Arbitrary-Masked Mamba for Joint Medical Image Reconstruction and Uncertainty Estimation.

FaRMamba: Frequency-based learning and Reconstruction aided Mamba for Medical Segmentation MambaMIR: An Arbitrary-Masked Mamba for Joint Medical Image Reconstruction and Uncertainty Estimation

Reference 14

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This paper cites Nature Methods 18(2), 203–211 (2021).

FaRMamba: Frequency-based learning and Reconstruction aided Mamba for Medical Segmentation Nature Methods 18(2), 203–211 (2021)

Reference 15

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This paper cites In: Ro, Y.M., Cheng, W.H., Kim, J., Chu, W.T., Cui, P., Choi, J.W., Hu, M.C., De Neve, W.

FaRMamba: Frequency-based learning and Reconstruction aided Mamba for Medical Segmentation In: Ro, Y.M., Cheng, W.H., Kim, J., Chu, W.T., Cui, P., Choi, J.W., Hu, M.C., De Neve, W

Reference 16

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This paper cites Physics in Medicine & Biology 69(9), 095022 (2024).

FaRMamba: Frequency-based learning and Reconstruction aided Mamba for Medical Segmentation Physics in Medicine & Biology 69(9), 095022 (2024)

Reference 17

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This paper cites Photoacoustics20, 100197 (2020).

FaRMamba: Frequency-based learning and Reconstruction aided Mamba for Medical Segmentation Photoacoustics20, 100197 (2020)

Reference 18

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This paper cites IEEE Transactions on Medical Imaging 38(9), 2198–2210 (2019).https://doi.org/10.1109/TMI.2019.2900516.

FaRMamba: Frequency-based learning and Reconstruction aided Mamba for Medical Segmentation IEEE Transactions on Medical Imaging 38(9), 2198–2210 (2019).https://doi.org/10.1109/TMI.2019.2900516

Reference 19

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This paper cites Proceedings of the AAAI Conference on Artificial Intelligence39(5), 4652–4660 (2025).

FaRMamba: Frequency-based learning and Reconstruction aided Mamba for Medical Segmentation Proceedings of the AAAI Conference on Artificial Intelligence39(5), 4652–4660 (2025)

Reference 20

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Observation 7a723bdf-e911-4b02-a83b-12e07a7c6ff6 · outbound

This paper cites Self-Supervised Alignment Learning for Medical Image Segmentation.

FaRMamba: Frequency-based learning and Reconstruction aided Mamba for Medical Segmentation Self-Supervised Alignment Learning for Medical Image Segmentation

Reference 21

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This paper cites Computers in Biology and Medicine164, 107290 (2023).

FaRMamba: Frequency-based learning and Reconstruction aided Mamba for Medical Segmentation Computers in Biology and Medicine164, 107290 (2023)

Reference 22

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FaRMamba: Frequency-based learning and Reconstruction aided Mamba for Medical Segmentation In: Farkaš, I., Masulli, P., Wermter, S

Reference 23

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This paper cites https://doi.org/10.1016/j.media.2021.102035.

FaRMamba: Frequency-based learning and Reconstruction aided Mamba for Medical Segmentation https://doi.org/10.1016/j.media.2021.102035

Reference 24

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FaRMamba: Frequency-based learning and Reconstruction aided Mamba for Medical Segmentation U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation

Reference 25

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FaRMamba: Frequency-based learning and Reconstruction aided Mamba for Medical Segmentation In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G

Reference 26

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FaRMamba: Frequency-based learning and Reconstruction aided Mamba for Medical Segmentation IEEE Transactions on Pattern Analysis and Machine Intelligence44(7), 3523–3542 (2022).https://doi.org/10

Reference 27

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This paper cites Fast multi-scale edge-detection in medical ultrasound signals.

FaRMamba: Frequency-based learning and Reconstruction aided Mamba for Medical Segmentation Fast multi-scale edge-detection in medical ultrasound signals

Reference 28

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This paper cites Magnetic Resonance in Medicine 42(5), 952– 962 (1999).

FaRMamba: Frequency-based learning and Reconstruction aided Mamba for Medical Segmentation Magnetic Resonance in Medicine 42(5), 952– 962 (1999)

Reference 30

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This paper cites An Overview of Multi-Task Learning in Deep Neural Networks.

FaRMamba: Frequency-based learning and Reconstruction aided Mamba for Medical Segmentation An Overview of Multi-Task Learning in Deep Neural Networks

Reference 31

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FaRMamba: Frequency-based learning and Reconstruction aided Mamba for Medical Segmentation In: Proceedings of the 31st Inter- national Conference on Neural Information Processing Systems

Reference 32

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FaRMamba: Frequency-based learning and Reconstruction aided Mamba for Medical Segmentation GlobalMamba: Global Image Serialization for Vision Mamba

Reference 33

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This paper cites ISPRS Journal of Photogrammetry and Remote Sensing 190, 196–214 (2022).https://doi.org/10.1016/j.isprsjprs.2022.06.008.

FaRMamba: Frequency-based learning and Reconstruction aided Mamba for Medical Segmentation ISPRS Journal of Photogrammetry and Remote Sensing 190, 196–214 (2022).https://doi.org/10.1016/j.isprsjprs.2022.06.008

Reference 34

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This paper cites Advances in Neural Information Processing Systems 36, 74021–74038 (2023).

FaRMamba: Frequency-based learning and Reconstruction aided Mamba for Medical Segmentation Advances in Neural Information Processing Systems 36, 74021–74038 (2023)

Reference 35

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This paper cites P-Mamba: Marrying Perona Malik Diffusion with Mamba for Efficient Pediatric Echocardiographic Left Ventricular Segmentation.

FaRMamba: Frequency-based learning and Reconstruction aided Mamba for Medical Segmentation P-Mamba: Marrying Perona Malik Diffusion with Mamba for Efficient Pediatric Echocardiographic Left Ventricular Segmentation

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FaRMamba: Frequency-based learning and Reconstruction aided Mamba for Medical Segmentation Tuning Frequency Bias of State Space Models

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FaRMamba: Frequency-based learning and Reconstruction aided Mamba for Medical Segmentation Discrete Cosine Transform Network for Guided Depth Map Super-Resolution

Reference 39

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FaRMamba: Frequency-based learning and Reconstruction aided Mamba for Medical Segmentation In: 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI)

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FaRMamba: Frequency-based learning and Reconstruction aided Mamba for Medical Segmentation In: Proceed- ings of the 41st International Conference on Machine Learning

Reference 41

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