Paper Citation Record · LEDGER

HCMA-UNet: A Hybrid CNN-Mamba UNet with Axial Self-Attention for Efficient Breast Cancer Segmentation

As of 19 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 1 inbound Pith citation observation for arXiv:2501.00751.

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

pith.paper-citation-record.v1
2501.00751 v2

Coverage vector

measured 22 of 22 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T22:48:24.057887Z

measured 23 of 23 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T15:49:15.625749Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-15T15:49:16.448217Z

Reference resolution

22 of 22 outbound references displayed

verified exact0
verified fuzzy15
unresolved7
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 2b693078-253e-4a4b-8975-61a4ebd0dfab · outbound

This paper cites Key steps for effective breast cancer prevention,.

HCMA-UNet: A Hybrid CNN-Mamba UNet with Axial Self-Attention for Efficient Breast Cancer Segmentation Key steps for effective breast cancer prevention,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:48:25.163484Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 126179d3-f4b2-4dfb-9b3a-82c0d75d4aea · outbound

This paper cites Early diagnosis and detection of breast cancer,.

HCMA-UNet: A Hybrid CNN-Mamba UNet with Axial Self-Attention for Efficient Breast Cancer Segmentation Early diagnosis and detection of breast cancer,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:48:25.146823Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b09c8122-ed69-478a-9211-310c4c3d3595 · outbound

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

HCMA-UNet: A Hybrid CNN-Mamba UNet with Axial Self-Attention for Efficient Breast Cancer Segmentation U-net: Con- volutional networks for biomedical image segmentation,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-10T22:48:23.801156Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 296a9078-7c85-4ede-a0f3-4913f097c803 · outbound

This paper cites Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images,.

HCMA-UNet: A Hybrid CNN-Mamba UNet with Axial Self-Attention for Efficient Breast Cancer Segmentation Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:48:25.115629Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 20984ce5-d7c5-46c2-b9cf-2bfe6daa2871 · outbound

This paper cites Unetr++: delving into efficient and accurate 3d medical image segmentation,.

HCMA-UNet: A Hybrid CNN-Mamba UNet with Axial Self-Attention for Efficient Breast Cancer Segmentation Unetr++: delving into efficient and accurate 3d medical image segmentation,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:48:24.982858Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b9a0dc39-6dae-4eef-a974-7d663beee6de · outbound

This paper cites Swinhr: Hemodynamic-powered hierarchical vision transformer for breast tumor segmentation,.

HCMA-UNet: A Hybrid CNN-Mamba UNet with Axial Self-Attention for Efficient Breast Cancer Segmentation Swinhr: Hemodynamic-powered hierarchical vision transformer for breast tumor segmentation,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:48:24.876351Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 69e466f4-7278-4b39-ae28-1ffbf863a575 · outbound

This paper cites Msa-vnet: Multi- scale attention-based v-net for dce-mri lesion segmentation,.

HCMA-UNet: A Hybrid CNN-Mamba UNet with Axial Self-Attention for Efficient Breast Cancer Segmentation Msa-vnet: Multi- scale attention-based v-net for dce-mri lesion segmentation,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:48:24.861127Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 35968a63-6939-42a2-ba5f-b8d20bc277b2 · outbound

This paper cites Prototype learning guided hybrid network for breast tumor segmenta- tion in dce-mri,.

HCMA-UNet: A Hybrid CNN-Mamba UNet with Axial Self-Attention for Efficient Breast Cancer Segmentation Prototype learning guided hybrid network for breast tumor segmenta- tion in dce-mri,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:48:24.763456Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7bdcb12b-1322-48c8-9f5c-55affb87fa28 · outbound

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

HCMA-UNet: A Hybrid CNN-Mamba UNet with Axial Self-Attention for Efficient Breast Cancer Segmentation Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-10T22:48:23.960916Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4ef9fc0c-4a3f-43e7-9c36-17c29f315d3a · outbound

This paper cites U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation.

HCMA-UNet: A Hybrid CNN-Mamba UNet with Axial Self-Attention for Efficient Breast Cancer Segmentation U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-10T22:48:23.966441Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 51b1e910-1033-495d-b806-d5086134a210 · outbound

This paper cites Lkm-unet: Large kernel vision mamba unet for medical image segmentation,.

HCMA-UNet: A Hybrid CNN-Mamba UNet with Axial Self-Attention for Efficient Breast Cancer Segmentation Lkm-unet: Large kernel vision mamba unet for medical image segmentation,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:48:24.605539Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c87b59d4-11a6-4c77-85a3-3c4287fc0e1c · outbound

This paper cites MambaClinix: Hierarchical Gated Convolution and Mamba-Based U-Net for Enhanced 3D Medical Image Segmentation.

HCMA-UNet: A Hybrid CNN-Mamba UNet with Axial Self-Attention for Efficient Breast Cancer Segmentation MambaClinix: Hierarchical Gated Convolution and Mamba-Based U-Net for Enhanced 3D Medical Image Segmentation

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-10T22:48:23.978309Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 922eca96-e625-4225-859e-cb02557ba0f5 · outbound

This paper cites Vmamba: Visual state space model,.

HCMA-UNet: A Hybrid CNN-Mamba UNet with Axial Self-Attention for Efficient Breast Cancer Segmentation Vmamba: Visual state space model,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:48:24.588218Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation cb008165-92a3-41c4-8075-7be22fa2daf6 · outbound

This paper cites Mobilenetv2: Inverted residuals and linear bottlenecks,.

HCMA-UNet: A Hybrid CNN-Mamba UNet with Axial Self-Attention for Efficient Breast Cancer Segmentation Mobilenetv2: Inverted residuals and linear bottlenecks,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-10T22:48:23.987715Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 771b856f-9d2e-4bbe-b4fb-c7d831f69f8d · outbound

This paper cites Bag of tricks for image classification with convolutional neural networks,.

HCMA-UNet: A Hybrid CNN-Mamba UNet with Axial Self-Attention for Efficient Breast Cancer Segmentation Bag of tricks for image classification with convolutional neural networks,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:48:24.457379Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3d613fd2-c62d-476f-b1ff-c96efbffd655 · outbound

This paper cites Densenets reloaded: paradigm shift beyond resnets and vits,.

HCMA-UNet: A Hybrid CNN-Mamba UNet with Axial Self-Attention for Efficient Breast Cancer Segmentation Densenets reloaded: paradigm shift beyond resnets and vits,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:48:24.345450Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a5071713-5a9e-4219-bdeb-2a62e76187c4 · outbound

This paper cites Attention gated networks: Learning to leverage salient regions in medical images,.

HCMA-UNet: A Hybrid CNN-Mamba UNet with Axial Self-Attention for Efficient Breast Cancer Segmentation Attention gated networks: Learning to leverage salient regions in medical images,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:48:24.329984Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7c8d1e0d-050c-4b2d-8ea1-2932c11e9e08 · outbound

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

HCMA-UNet: A Hybrid CNN-Mamba UNet with Axial Self-Attention for Efficient Breast Cancer Segmentation nnu-net: a self-configuring method for deep learning-based biomedical image segmentation,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:48:24.313552Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2dee8b61-59fe-4a90-8167-3355eb7a9587 · outbound

This paper cites Mednext: transformer-driven scaling of convnets for medical image segmentation,.

HCMA-UNet: A Hybrid CNN-Mamba UNet with Axial Self-Attention for Efficient Breast Cancer Segmentation Mednext: transformer-driven scaling of convnets for medical image segmentation,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:48:24.297932Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 62a02441-1f86-40e3-8cef-c73be5d4e27f · outbound

This paper cites 3D UX-Net: A Large Kernel Volumetric ConvNet Modernizing Hierarchical Transformer for Medical Image Segmentation.

HCMA-UNet: A Hybrid CNN-Mamba UNet with Axial Self-Attention for Efficient Breast Cancer Segmentation 3D UX-Net: A Large Kernel Volumetric ConvNet Modernizing Hierarchical Transformer for Medical Image Segmentation

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-10T22:48:24.014576Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4ab60686-2da3-4dcc-8f85-b4c9f7eb6a50 · outbound

This paper cites A large-scale multicenter breast cancer DCE-MRI benchmark dataset with expert segmentations.

HCMA-UNet: A Hybrid CNN-Mamba UNet with Axial Self-Attention for Efficient Breast Cancer Segmentation A large-scale multicenter breast cancer DCE-MRI benchmark dataset with expert segmentations

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-10T22:48:24.019816Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 93499eb1-7974-4784-a52a-ca9b79b4c462 · outbound

This paper cites A robust and efficient ai assistant for breast tumor segmentation from dce-mri via a spatial-temporal framework,.

HCMA-UNet: A Hybrid CNN-Mamba UNet with Axial Self-Attention for Efficient Breast Cancer Segmentation A robust and efficient ai assistant for breast tumor segmentation from dce-mri via a spatial-temporal framework,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:48:24.281567Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Pith citing papers

Observation 6ad22a35-c684-4c94-812e-1ab3bbe88cbc · inbound

Johnson-Lindenstrauss Lemma Guided Network for Efficient 3D Medical Segmentation cites this paper.

Johnson-Lindenstrauss Lemma Guided Network for Efficient 3D Medical Segmentation HCMA-UNet: A Hybrid CNN-Mamba UNet with Axial Self-Attention for Efficient Breast Cancer Segmentation

Reference 2025

Resolution: verified exact; local_arxiv, observed 2026-08-15T15:49:16.454924Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.