ASM-UNet: Adaptive Scan Mamba Integrating Group Commonalities and Individual Variations for Fine-Grained Segmentation | Paper Citation Record

Observation 4c4ef5b7-6473-4f48-babb-5dc5f65c7575 · outbound

This paper cites Prostate-specific membrane antigen-targeted surgery in prostate cancer: accurate identification, real-time diagnosis, and precise resection,.

ASM-UNet: Adaptive Scan Mamba Integrating Group Commonalities and Individual Variations for Fine-Grained Segmentation Prostate-specific membrane antigen-targeted surgery in prostate cancer: accurate identification, real-time diagnosis, and precise resection,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:19:14.368125Z

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Observation 04da365c-ee85-41c0-ba98-ae670eb6beae · outbound

This paper cites Nir-ii aiegens nanosystem for fluorescence and chemiluminescence synergistic imaging-guided precise resection in osteosarcoma surgery,.

ASM-UNet: Adaptive Scan Mamba Integrating Group Commonalities and Individual Variations for Fine-Grained Segmentation Nir-ii aiegens nanosystem for fluorescence and chemiluminescence synergistic imaging-guided precise resection in osteosarcoma surgery,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:19:14.227509Z

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Observation 3c7270ca-35b1-44a9-afd3-de821f225e49 · outbound

This paper cites Medical image segmentation review: The success of u-net,.

ASM-UNet: Adaptive Scan Mamba Integrating Group Commonalities and Individual Variations for Fine-Grained Segmentation Medical image segmentation review: The success of u-net,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:19:14.056067Z

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Observation e37a1c3e-9301-492a-9f6d-2bf38f650926 · outbound

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

ASM-UNet: Adaptive Scan Mamba Integrating Group Commonalities and Individual Variations for Fine-Grained Segmentation Unetr++: delving into efficient and accurate 3d medical image segmentation,

Reference 4

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Observation b5b81b8c-0b65-4cb8-9c70-83a285c45620 · outbound

This paper cites Fsvs-net: A few-shot semi-supervised vessel segmentation network for multiple organs based on feature distillation and bidirectional weighted fusion,.

ASM-UNet: Adaptive Scan Mamba Integrating Group Commonalities and Individual Variations for Fine-Grained Segmentation Fsvs-net: A few-shot semi-supervised vessel segmentation network for multiple organs based on feature distillation and bidirectional weighted fusion,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:19:13.902212Z

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Observation bccd8794-d9e3-4773-845d-7b963e1352a5 · outbound

This paper cites Multi-organ segmentation over partially labeled datasets with multi-scale feature abstraction,.

ASM-UNet: Adaptive Scan Mamba Integrating Group Commonalities and Individual Variations for Fine-Grained Segmentation Multi-organ segmentation over partially labeled datasets with multi-scale feature abstraction,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:19:13.711281Z

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Observation 5de17b2b-9031-46e9-ad63-fda8e36a1a8f · outbound

This paper cites Abdomenct-1k: Is abdominal organ segmentation a solved problem?.

ASM-UNet: Adaptive Scan Mamba Integrating Group Commonalities and Individual Variations for Fine-Grained Segmentation Abdomenct-1k: Is abdominal organ segmentation a solved problem?

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:19:13.557046Z

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Observation a6b71722-5f9b-4564-9436-4f472c45338e · outbound

This paper cites Cosst: Multi-organ segmentation with partially labeled datasets using comprehensive supervisions and self-training,.

ASM-UNet: Adaptive Scan Mamba Integrating Group Commonalities and Individual Variations for Fine-Grained Segmentation Cosst: Multi-organ segmentation with partially labeled datasets using comprehensive supervisions and self-training,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:19:13.396296Z

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Observation 84aaa2ea-e397-4318-8821-c48ccb9ee7c2 · outbound

This paper cites Anatomical variations and surgical strategies in right lobe living donor liver transplantation: lessons from 120 cases1,.

ASM-UNet: Adaptive Scan Mamba Integrating Group Commonalities and Individual Variations for Fine-Grained Segmentation Anatomical variations and surgical strategies in right lobe living donor liver transplantation: lessons from 120 cases1,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:19:13.230830Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 525110ce-e6b6-4d94-9ec4-c44804171a77 · outbound

This paper cites Anatomic variations in right liver living donors1,.

ASM-UNet: Adaptive Scan Mamba Integrating Group Commonalities and Individual Variations for Fine-Grained Segmentation Anatomic variations in right liver living donors1,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:19:13.009650Z

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Observation 59ae5ca8-4ef3-4a17-86ef-88a1e840531c · outbound

This paper cites CNN-based Segmentation of Medical Imaging Data.

ASM-UNet: Adaptive Scan Mamba Integrating Group Commonalities and Individual Variations for Fine-Grained Segmentation CNN-based Segmentation of Medical Imaging Data

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-08-05T22:19:07.208809Z

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Observation 786893f7-a82a-488f-bba4-e7019c201e6a · outbound

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

ASM-UNet: Adaptive Scan Mamba Integrating Group Commonalities and Individual Variations for Fine-Grained Segmentation U-net: Convolutional networks for biomedical image segmentation,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:19:12.859720Z

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Observation e09c9e64-c89b-41be-a951-4472d160e408 · outbound

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

ASM-UNet: Adaptive Scan Mamba Integrating Group Commonalities and Individual Variations for Fine-Grained Segmentation Unet++: A nested u-net architecture for medical image segmentation,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:19:12.644751Z

Source-reported events for the cited work

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Observation 05268ad9-5d98-4dfc-97de-c8179a969fdc · outbound

This paper cites Unet 3+: A full-scale connected unet for medical image segmentation,.

ASM-UNet: Adaptive Scan Mamba Integrating Group Commonalities and Individual Variations for Fine-Grained Segmentation Unet 3+: A full-scale connected unet for medical image segmentation,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:19:12.449513Z

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Observation bd9e335d-64db-4d13-a13c-c7ea1c19d46e · outbound

This paper cites Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer,.

ASM-UNet: Adaptive Scan Mamba Integrating Group Commonalities and Individual Variations for Fine-Grained Segmentation Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:19:12.261848Z

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Observation 6b1cfdb0-dfac-4271-8575-6d70fb6e351d · outbound

This paper cites Attention is all you need,.

ASM-UNet: Adaptive Scan Mamba Integrating Group Commonalities and Individual Variations for Fine-Grained Segmentation Attention is all you need,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:19:12.141569Z

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Observation 8f84544e-3464-4590-a5a0-40f989ea3036 · outbound

This paper cites Attention U-Net: Learning Where to Look for the Pancreas.

ASM-UNet: Adaptive Scan Mamba Integrating Group Commonalities and Individual Variations for Fine-Grained Segmentation Attention U-Net: Learning Where to Look for the Pancreas

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-05T22:19:02.449872Z

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Observation 00611117-5cbd-4030-b7e1-245ccf5fa57a · outbound

This paper cites Missformer: An effective transformer for 2d medical image segmentation,.

ASM-UNet: Adaptive Scan Mamba Integrating Group Commonalities and Individual Variations for Fine-Grained Segmentation Missformer: An effective transformer for 2d medical image segmentation,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:19:11.961491Z

Source-reported events for the cited work

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Observation 5e5b2132-b354-4d4a-bffa-b3b58ea089e9 · outbound

This paper cites nnformer: volumetric medical image segmentation via a 3d transformer,.

ASM-UNet: Adaptive Scan Mamba Integrating Group Commonalities and Individual Variations for Fine-Grained Segmentation nnformer: volumetric medical image segmentation via a 3d transformer,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:19:11.816445Z

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Observation b3a589c1-1a3e-4f49-961b-c2653755e881 · outbound

This paper cites BiCLIP-nnFormer: A Virtual Multimodal Instrument for Efficient and Accurate Medical Image Segmentation,.

ASM-UNet: Adaptive Scan Mamba Integrating Group Commonalities and Individual Variations for Fine-Grained Segmentation BiCLIP-nnFormer: A Virtual Multimodal Instrument for Efficient and Accurate Medical Image Segmentation,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:19:11.634238Z

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Observation 3cd48c93-bee2-4c32-9ec2-d7ffe99069a5 · outbound

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

ASM-UNet: Adaptive Scan Mamba Integrating Group Commonalities and Individual Variations for Fine-Grained Segmentation Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 21

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Observation 1ab08c2a-e031-48b0-9708-a31b48d9d9c2 · outbound

This paper cites nnmamba: 3d biomedical image segmentation, classification and landmark detection with state space model,.

ASM-UNet: Adaptive Scan Mamba Integrating Group Commonalities and Individual Variations for Fine-Grained Segmentation nnmamba: 3d biomedical image segmentation, classification and landmark detection with state space model,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:19:11.490241Z

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Observation 90f9c8d3-59cd-4dd7-8961-181075fcbd20 · outbound

This paper cites Segmamba: Long-range sequential modeling mamba for 3d medical image segmentation,.

ASM-UNet: Adaptive Scan Mamba Integrating Group Commonalities and Individual Variations for Fine-Grained Segmentation Segmamba: Long-range sequential modeling mamba for 3d medical image segmentation,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:19:11.352674Z

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Observation e37c0dd1-6539-4a24-b454-3861657533af · outbound

This paper cites Swin-umamba†: Adapting mamba-based vision foundation models for medical image segmentation,.

ASM-UNet: Adaptive Scan Mamba Integrating Group Commonalities and Individual Variations for Fine-Grained Segmentation Swin-umamba†: Adapting mamba-based vision foundation models for medical image segmentation,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:19:11.204962Z

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Observation b2e51894-cb3d-4d1a-a336-83dff4bfba2e · outbound

This paper cites Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model.

ASM-UNet: Adaptive Scan Mamba Integrating Group Commonalities and Individual Variations for Fine-Grained Segmentation Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-05T22:19:03.587834Z

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Observation 11404a8f-35f1-4f52-b4bf-8ff70172df3a · outbound

This paper cites LocalMamba: Visual State Space Model with Windowed Selective Scan.

ASM-UNet: Adaptive Scan Mamba Integrating Group Commonalities and Individual Variations for Fine-Grained Segmentation LocalMamba: Visual State Space Model with Windowed Selective Scan

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-05T22:19:03.706720Z

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Observation e38d0c33-cb6e-49a0-a505-b9d96a800bfe · outbound

This paper cites MambaCSR: Dual-Interleaved Scanning for Compressed Image Super-Resolution With SSMs.

ASM-UNet: Adaptive Scan Mamba Integrating Group Commonalities and Individual Variations for Fine-Grained Segmentation MambaCSR: Dual-Interleaved Scanning for Compressed Image Super-Resolution With SSMs

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-05T22:19:03.907117Z

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Observation c4502f80-90a1-42dc-80cb-d28d6a0f124a · outbound

This paper cites Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge,.

ASM-UNet: Adaptive Scan Mamba Integrating Group Commonalities and Individual Variations for Fine-Grained Segmentation Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:19:11.048659Z

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Observation bb3513ea-656d-4f26-851d-4d9c3d8d0b7a · outbound

This paper cites Deep learning techniques for automatic mri cardiac multi-structures segmentation and diagnosis: is the problem solved?.

ASM-UNet: Adaptive Scan Mamba Integrating Group Commonalities and Individual Variations for Fine-Grained Segmentation Deep learning techniques for automatic mri cardiac multi-structures segmentation and diagnosis: is the problem solved?

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:19:10.858221Z

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Observation d08f172f-3aa0-419f-b01f-30395a1534ab · outbound

This paper cites Fully convolutional networks for semantic segmentation,.

ASM-UNet: Adaptive Scan Mamba Integrating Group Commonalities and Individual Variations for Fine-Grained Segmentation Fully convolutional networks for semantic segmentation,

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-05T22:19:04.284407Z

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Observation 85ba4499-d7ad-40b9-88c1-dcb222a426bf · outbound

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

ASM-UNet: Adaptive Scan Mamba Integrating Group Commonalities and Individual Variations for Fine-Grained Segmentation nnu-net: a self-configuring method for deep learning-based biomedical image segmentation,

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-05T22:19:04.425849Z

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Observation 2732e300-6b7c-4d10-99db-559374f843eb · outbound

This paper cites A convnet for the 2020s,.

ASM-UNet: Adaptive Scan Mamba Integrating Group Commonalities and Individual Variations for Fine-Grained Segmentation A convnet for the 2020s,

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-05T22:19:04.589597Z

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Observation 244b7c79-6b9a-42c2-bedf-00dbd8ba883c · outbound

This paper cites Cotr: Efficiently bridging cnn and transformer for 3d medical image segmentation,.

ASM-UNet: Adaptive Scan Mamba Integrating Group Commonalities and Individual Variations for Fine-Grained Segmentation Cotr: Efficiently bridging cnn and transformer for 3d medical image segmentation,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:19:10.632377Z

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Observation 9ef353b0-9924-480c-9618-3481ce803cec · outbound

This paper cites Unetr: Transformers for 3d medical image segmentation,.

ASM-UNet: Adaptive Scan Mamba Integrating Group Commonalities and Individual Variations for Fine-Grained Segmentation Unetr: Transformers for 3d medical image segmentation,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:19:10.330175Z

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Observation a4b75cbe-c443-4153-b0d6-b3e639e1d83a · outbound

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

ASM-UNet: Adaptive Scan Mamba Integrating Group Commonalities and Individual Variations for Fine-Grained Segmentation Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:19:10.039670Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f1351424-0be7-4b82-aaa4-410695004ac5 · outbound

This paper cites VM-UNet: Vision Mamba UNet for Medical Image Segmentation.

ASM-UNet: Adaptive Scan Mamba Integrating Group Commonalities and Individual Variations for Fine-Grained Segmentation VM-UNet: Vision Mamba UNet for Medical Image Segmentation

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-05T22:19:05.092898Z

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Observation c81ac371-3e66-4be8-9e50-7e0f86d06b58 · outbound

This paper cites Swin-umamba: Mamba-based unet with imagenet-based pretraining,.

ASM-UNet: Adaptive Scan Mamba Integrating Group Commonalities and Individual Variations for Fine-Grained Segmentation Swin-umamba: Mamba-based unet with imagenet-based pretraining,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:19:09.774145Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 6f9357ba-53db-4b73-a174-67da4ad89b42 · outbound

This paper cites Vivim: a Video Vision Mamba for Medical Video Segmentation.

ASM-UNet: Adaptive Scan Mamba Integrating Group Commonalities and Individual Variations for Fine-Grained Segmentation Vivim: a Video Vision Mamba for Medical Video Segmentation

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-05T22:19:05.370579Z

Source-reported events for the cited work

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Observation fb708930-2f13-4b52-a3e5-e3c6f36009fd · outbound

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

ASM-UNet: Adaptive Scan Mamba Integrating Group Commonalities and Individual Variations for Fine-Grained Segmentation U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-05T22:19:05.565298Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0c1c8106-a398-4e6e-8695-c6209f375685 · outbound

This paper cites nnMamba: 3D Biomedical Image Segmentation, Classification and Landmark Detection with State Space Model.

ASM-UNet: Adaptive Scan Mamba Integrating Group Commonalities and Individual Variations for Fine-Grained Segmentation nnMamba: 3D Biomedical Image Segmentation, Classification and Landmark Detection with State Space Model

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-05T22:19:05.743273Z

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Observation 3b7e684d-6c3f-4f40-ba90-55e992a825bd · outbound

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

ASM-UNet: Adaptive Scan Mamba Integrating Group Commonalities and Individual Variations for Fine-Grained Segmentation Lkm-unet: Large kernel vision mamba unet for medical image segmentation,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:19:09.455460Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 52b32162-b831-4a9e-9531-f7a9e55d3e48 · outbound

This paper cites 3d image reconstruction for comparison of algorithm database,.

ASM-UNet: Adaptive Scan Mamba Integrating Group Commonalities and Individual Variations for Fine-Grained Segmentation 3d image reconstruction for comparison of algorithm database,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:19:09.149383Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation cc1f01f7-fd06-4930-806e-88f1b37fec2d · outbound

This paper cites Word: A large scale dataset, benchmark and clinical applicable study for abdominal organ segmentation from ct image,.

ASM-UNet: Adaptive Scan Mamba Integrating Group Commonalities and Individual Variations for Fine-Grained Segmentation Word: A large scale dataset, benchmark and clinical applicable study for abdominal organ segmentation from ct image,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:19:08.847126Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a778d876-4d00-46ff-b477-27ca5fc8e9ff · outbound

This paper cites Transunet: Rethinking the u-net architecture design for medical image segmentation through the lens of transformers,.

ASM-UNet: Adaptive Scan Mamba Integrating Group Commonalities and Individual Variations for Fine-Grained Segmentation Transunet: Rethinking the u-net architecture design for medical image segmentation through the lens of transformers,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:19:08.434442Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation df4274ef-087c-4e9f-9921-b0629e4b7791 · outbound

This paper cites Swin-unet: Unet-like pure transformer for medical image segmentation,.

ASM-UNet: Adaptive Scan Mamba Integrating Group Commonalities and Individual Variations for Fine-Grained Segmentation Swin-unet: Unet-like pure transformer for medical image segmentation,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:19:08.133053Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 4cf33194-fb88-4c06-b686-e2898c2bf315 · outbound

This paper cites Levit-unet: Make faster encoders with transformer for medical image segmentation,.

ASM-UNet: Adaptive Scan Mamba Integrating Group Commonalities and Individual Variations for Fine-Grained Segmentation Levit-unet: Make faster encoders with transformer for medical image segmentation,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:19:07.837612Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 97c1e608-64a4-4c48-b4da-31b3afbc4c16 · outbound

This paper cites Segformer3d: an efficient transformer for 3d medical image segmentation,.

ASM-UNet: Adaptive Scan Mamba Integrating Group Commonalities and Individual Variations for Fine-Grained Segmentation Segformer3d: an efficient transformer for 3d medical image segmentation,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:19:07.574015Z

Source-reported events for the cited work

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