Paper Citation Record · LEDGER

SAGE: Shape-Adapting Gated Experts for Adaptive Histopathology Image Segmentation

As of 10 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 1 inbound Pith citation observation for arXiv:2511.18493.

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

pith.paper-citation-record.v1
2511.18493 v4

Coverage vector

measured 45 of 45 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T20:49:18.880902Z

measured 46 of 46 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-19T16:16:38.731203Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-19T16:22:40.108179Z

Reference resolution

45 of 45 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c1033f8e-d2db-45d7-b5aa-5623d6352d17 · outbound

This paper cites Understanding of a convolutional neural network.

SAGE: Shape-Adapting Gated Experts for Adaptive Histopathology Image Segmentation Understanding of a convolutional neural network

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-03T20:49:14.775579Z

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Observation fa3e9e6f-8ce0-45fd-94b5-998aab8a6bd5 · outbound

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

SAGE: Shape-Adapting Gated Experts for Adaptive Histopathology Image Segmentation Swin-unet: Unet-like pure transformer for medical image segmentation

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-03T20:49:14.861935Z

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Observation 6604cccb-cd59-49fe-8307-873af5b66534 · outbound

This paper cites an unresolved cited work.

SAGE: Shape-Adapting Gated Experts for Adaptive Histopathology Image Segmentation Unresolved cited work

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-03T20:49:14.939480Z

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Observation 8895c26c-2e9f-4462-a2b8-5cee1d973564 · outbound

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

SAGE: Shape-Adapting Gated Experts for Adaptive Histopathology Image Segmentation TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

Reference 4

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Observation 60faec1d-0d54-4cfb-9fb1-bfe9172488bd · outbound

This paper cites Transunet: Rethinking the u-net architec- ture design for medical image segmentation through the lens of transformers.Medical Image Analysis, 97:103280, 2024.

SAGE: Shape-Adapting Gated Experts for Adaptive Histopathology Image Segmentation Transunet: Rethinking the u-net architec- ture design for medical image segmentation through the lens of transformers.Medical Image Analysis, 97:103280, 2024

Reference 5

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Observation 19748b0f-3dfe-42c8-b7a0-3c37ca5dcc30 · outbound

This paper cites Adamv-moe: Adaptive multi-task vision mixture-of- experts.

SAGE: Shape-Adapting Gated Experts for Adaptive Histopathology Image Segmentation Adamv-moe: Adaptive multi-task vision mixture-of- experts

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-03T20:49:15.225705Z

Source-reported events for the cited work

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Observation 7de6e25f-f680-4dd3-8533-61ccb6b47428 · outbound

This paper cites Metaxas, Hongsheng Li, Chaofu Wang, and Shaoting Zhang.

SAGE: Shape-Adapting Gated Experts for Adaptive Histopathology Image Segmentation Metaxas, Hongsheng Li, Chaofu Wang, and Shaoting Zhang

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-03T20:49:15.285212Z

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Observation 68f63067-aaa3-4171-a686-51c62d563d04 · outbound

This paper cites an unresolved cited work.

SAGE: Shape-Adapting Gated Experts for Adaptive Histopathology Image Segmentation Unresolved cited work

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-03T20:49:15.435551Z

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Observation 8734cd37-9f70-4e8c-b680-f08f34465c46 · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale, 2021.

SAGE: Shape-Adapting Gated Experts for Adaptive Histopathology Image Segmentation An image is worth 16x16 words: Transformers for image recognition at scale, 2021

Reference 9

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Observation 4cd11432-db84-424c-a0bf-5ac6ad66af24 · outbound

This paper cites Convunext: An efficient convolution neural network for medical im- age segmentation.Knowledge-based systems, 253:109512,.

SAGE: Shape-Adapting Gated Experts for Adaptive Histopathology Image Segmentation Convunext: An efficient convolution neural network for medical im- age segmentation.Knowledge-based systems, 253:109512,

Reference 10

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Observation b0fb5866-72bd-45d2-a190-4cca91d048cd · outbound

This paper cites Deep residual learning for image recognition, 2015.

SAGE: Shape-Adapting Gated Experts for Adaptive Histopathology Image Segmentation Deep residual learning for image recognition, 2015

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-03T20:49:15.847484Z

Source-reported events for the cited work

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Observation 46059811-5c62-4f18-8bc7-733bd0275624 · outbound

This paper cites Deep residual learning for image recognition.

SAGE: Shape-Adapting Gated Experts for Adaptive Histopathology Image Segmentation Deep residual learning for image recognition

Reference 12

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Observation b9f12489-8544-4d36-be13-59f85d329984 · outbound

This paper cites Universal language model fine-tuning for text classification, 2018.

SAGE: Shape-Adapting Gated Experts for Adaptive Histopathology Image Segmentation Universal language model fine-tuning for text classification, 2018

Reference 13

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Observation 9ee79c28-86a2-45dd-8382-16773c2afd2b · outbound

This paper cites Ebhi: A new enteroscope biopsy histopathological h&e image dataset for image classification evaluation.Physica Medica, 107:102534, 2023.

SAGE: Shape-Adapting Gated Experts for Adaptive Histopathology Image Segmentation Ebhi: A new enteroscope biopsy histopathological h&e image dataset for image classification evaluation.Physica Medica, 107:102534, 2023

Reference 14

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Observation 1c55aade-7737-4979-babd-9f9ea61548c8 · outbound

This paper cites Transformers in vision: A survey.ACM computing surveys (CSUR), 54(10s):1–41, 2022.

SAGE: Shape-Adapting Gated Experts for Adaptive Histopathology Image Segmentation Transformers in vision: A survey.ACM computing surveys (CSUR), 54(10s):1–41, 2022

Reference 15

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Observation ab05ecab-d5b7-4677-af74-7e6a6fee5ba4 · outbound

This paper cites Evit-unet: U-net like efficient vision trans- former for medical image segmentation on mobile and edge devices.

SAGE: Shape-Adapting Gated Experts for Adaptive Histopathology Image Segmentation Evit-unet: U-net like efficient vision trans- former for medical image segmentation on mobile and edge devices

Reference 16

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Observation 2d18a4a1-b115-4eeb-a8bf-e48b75445359 · outbound

This paper cites Ds-transunet: Dual swin transformer u-net for medical image segmentation.IEEE Transactions on Instrumentation and Measurement, 71:1– 15, 2022.

SAGE: Shape-Adapting Gated Experts for Adaptive Histopathology Image Segmentation Ds-transunet: Dual swin transformer u-net for medical image segmentation.IEEE Transactions on Instrumentation and Measurement, 71:1– 15, 2022

Reference 17

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Observation 6af3ecd1-2ca2-466a-a95e-a81ee0f38a0e · outbound

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

SAGE: Shape-Adapting Gated Experts for Adaptive Histopathology Image Segmentation Swin-umamba: Mamba-based unet with imagenet-based pretraining

Reference 18

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Observation 102cea1b-c5ef-4033-ab5f-e98cfd6f6284 · outbound

This paper cites A convnet for the 2020s.

SAGE: Shape-Adapting Gated Experts for Adaptive Histopathology Image Segmentation A convnet for the 2020s

Reference 19

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Observation 74394ad8-be84-4e0b-a5a8-11031464b5de · outbound

This paper cites Decoupled weight decay regularization, 2019.

SAGE: Shape-Adapting Gated Experts for Adaptive Histopathology Image Segmentation Decoupled weight decay regularization, 2019

Reference 20

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Observation 52581911-8c86-4c7f-acd9-09a5902bcabe · outbound

This paper cites Segment anything in medical images.Nature Communications, 15(1), 2024.

SAGE: Shape-Adapting Gated Experts for Adaptive Histopathology Image Segmentation Segment anything in medical images.Nature Communications, 15(1), 2024

Reference 21

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Observation b7f297f4-4272-42fe-9e6b-ae47d57554f9 · outbound

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

SAGE: Shape-Adapting Gated Experts for Adaptive Histopathology Image Segmentation U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-03T20:49:16.872282Z

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Observation 9a3f868a-40cd-405a-a9b7-917d521faa62 · outbound

This paper cites Choos- ing smartly: Adaptive multimodal fusion for object detection in changing environments.

SAGE: Shape-Adapting Gated Experts for Adaptive Histopathology Image Segmentation Choos- ing smartly: Adaptive multimodal fusion for object detection in changing environments

Reference 23

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Observation 50d255b4-6cf7-4aec-95fb-9cb7c5d626f9 · outbound

This paper cites Sigmoid gating is more sample efficient than softmax gating in mix- ture of experts, 2024.

SAGE: Shape-Adapting Gated Experts for Adaptive Histopathology Image Segmentation Sigmoid gating is more sample efficient than softmax gating in mix- ture of experts, 2024

Reference 24

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Observation b5030835-7b15-467a-9649-ddb10af333ae · outbound

This paper cites C2gmatch: Leveraging dual-view cross-guidance and co-guidance framework for semi-supervised cell segmentation.

SAGE: Shape-Adapting Gated Experts for Adaptive Histopathology Image Segmentation C2gmatch: Leveraging dual-view cross-guidance and co-guidance framework for semi-supervised cell segmentation

Reference 25

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Observation d70a73aa-66bd-47f0-b996-1b143be77825 · outbound

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

SAGE: Shape-Adapting Gated Experts for Adaptive Histopathology Image Segmentation Attention U-Net: Learning Where to Look for the Pancreas

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-03T20:49:17.178823Z

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Observation 70c26214-0d61-4ae1-b3af-3b8a363f88f0 · outbound

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

SAGE: Shape-Adapting Gated Experts for Adaptive Histopathology Image Segmentation U-net: Convolutional networks for biomedical image segmentation

Reference 27

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Observation 0d572fdc-eafe-4307-8147-77823bd8649c · outbound

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

SAGE: Shape-Adapting Gated Experts for Adaptive Histopathology Image Segmentation U-net: Convolutional networks for biomedical image segmentation,

Reference 28

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Observation 00371e0c-c48b-4e1f-80b8-637e02f26cb1 · outbound

This paper cites Outra- geously large neural networks: The sparsely-gated mixture- of-experts layer, 2017.

SAGE: Shape-Adapting Gated Experts for Adaptive Histopathology Image Segmentation Outra- geously large neural networks: The sparsely-gated mixture- of-experts layer, 2017

Reference 29

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Observation 683a403c-d134-4b5e-9b02-cc11eb1a3d60 · outbound

This paper cites an unresolved cited work.

SAGE: Shape-Adapting Gated Experts for Adaptive Histopathology Image Segmentation Unresolved cited work

Reference 30

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Observation a04020b2-91c2-4c5a-bc7c-c2e6cb910296 · outbound

This paper cites Medical image anal- ysis using improved sam-med2d: segmentation and classifi- cation perspectives.BMC Medical Imaging, 24:241, 2024.

SAGE: Shape-Adapting Gated Experts for Adaptive Histopathology Image Segmentation Medical image anal- ysis using improved sam-med2d: segmentation and classifi- cation perspectives.BMC Medical Imaging, 24:241, 2024

Reference 31

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Observation fa6ca883-3c81-4950-b147-da967b204566 · outbound

This paper cites EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks.

SAGE: Shape-Adapting Gated Experts for Adaptive Histopathology Image Segmentation EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

Reference 32

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Observation 32a54b4c-0796-4707-86f2-e69cef43a99c · outbound

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SAGE: Shape-Adapting Gated Experts for Adaptive Histopathology Image Segmentation Unresolved cited work

Reference 33

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Observation 37999623-0b1c-409a-824e-cb7d5470c483 · outbound

This paper cites an unresolved cited work.

SAGE: Shape-Adapting Gated Experts for Adaptive Histopathology Image Segmentation Unresolved cited work

Reference 34

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Observation 6e408cf3-4cf8-4c6e-a8d8-5242f88cb463 · outbound

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

SAGE: Shape-Adapting Gated Experts for Adaptive Histopathology Image Segmentation Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer

Reference 35

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Observation a8368feb-493e-47ce-b8be-8606d1ddfb7c · outbound

This paper cites Moe-nuseg: Enhancing nuclei segmen- tation in histology images with a two-stage mixture of ex- perts network.Alexandria Engineering Journal, 110:557– 566, 2025.

SAGE: Shape-Adapting Gated Experts for Adaptive Histopathology Image Segmentation Moe-nuseg: Enhancing nuclei segmen- tation in histology images with a two-stage mixture of ex- perts network.Alexandria Engineering Journal, 110:557– 566, 2025

Reference 36

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Observation 3f65d524-5251-47e9-88df-8043c467c138 · outbound

This paper cites Segformer: Simple and efficient design for semantic segmentation with transform- ers.Advances in neural information processing systems, 34: 12077–12090, 2021.

SAGE: Shape-Adapting Gated Experts for Adaptive Histopathology Image Segmentation Segformer: Simple and efficient design for semantic segmentation with transform- ers.Advances in neural information processing systems, 34: 12077–12090, 2021

Reference 37

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Observation 74902ead-3577-4d01-9a87-7f7862c4d1c6 · outbound

This paper cites Sigmoid Self-Attention has Lower Sample Complexity than Softmax Self-Attention: A Mixture-of-Experts Perspective.

SAGE: Shape-Adapting Gated Experts for Adaptive Histopathology Image Segmentation Sigmoid Self-Attention has Lower Sample Complexity than Softmax Self-Attention: A Mixture-of-Experts Perspective

Reference 38

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Observation a1f2106b-aba3-4696-8f5c-356464543a42 · outbound

This paper cites Multi-task dense prediction via mixture of low-rank experts, 2024.

SAGE: Shape-Adapting Gated Experts for Adaptive Histopathology Image Segmentation Multi-task dense prediction via mixture of low-rank experts, 2024

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-03T20:49:18.341576Z

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Observation 718dca36-c3c4-4261-a1d3-f0ecc642b7c2 · outbound

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

SAGE: Shape-Adapting Gated Experts for Adaptive Histopathology Image Segmentation Unet++: A nested u-net ar- chitecture for medical image segmentation

Reference 40

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Observation 346c7673-fb7e-483d-9777-89664f23941f · outbound

This paper cites Multi-level colonoscopy malignant tissue detection with adversarial cac-unet.Neurocomputing, 438:165–183, 2021.

SAGE: Shape-Adapting Gated Experts for Adaptive Histopathology Image Segmentation Multi-level colonoscopy malignant tissue detection with adversarial cac-unet.Neurocomputing, 438:165–183, 2021

Reference 41

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Observation e2ef7af4-e365-4888-ba08-eeaade979701 · outbound

This paper cites Selfreg- unet: Self-regularized unet for medical image segmenta- tion.

SAGE: Shape-Adapting Gated Experts for Adaptive Histopathology Image Segmentation Selfreg- unet: Self-regularized unet for medical image segmenta- tion

Reference 42

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Observation b5a84080-282d-4111-9446-2a0d651729e7 · outbound

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SAGE: Shape-Adapting Gated Experts for Adaptive Histopathology Image Segmentation Unresolved cited work

Reference 43

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Source-reported events for the cited work

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Observation 2da29182-1714-484d-9be6-a03d70de7083 · outbound

This paper cites Recall the hierarchical rout- ing mechanism from the main paper.

SAGE: Shape-Adapting Gated Experts for Adaptive Histopathology Image Segmentation Recall the hierarchical rout- ing mechanism from the main paper

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-03T20:49:18.732787Z

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Observation 189a0406-4df0-4e7f-a8ab-5df7b262e941 · outbound

This paper cites Gating Mechanism.Our initial evaluation focused on comparing the performance of sigmoid versus softmax gat- ing functions for the expert routing layer.

SAGE: Shape-Adapting Gated Experts for Adaptive Histopathology Image Segmentation Gating Mechanism.Our initial evaluation focused on comparing the performance of sigmoid versus softmax gat- ing functions for the expert routing layer

Reference 45

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Pith citing papers

Observation bb8a0a44-576a-48c1-9e11-aeacb6206471 · inbound

$\phi$-Balancing for Mixture-of-Experts Training cites this paper.

$\phi$-Balancing for Mixture-of-Experts Training SAGE: Shape-Adapting Gated Experts for Adaptive Histopathology Image Segmentation

Reference 37

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Source-reported events for the cited work

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