TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation | Paper Citation Record

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This paper cites https : / / www.

TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation https : / / www

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Observation aadb64a6-74d2-4184-accf-5ec308edafb0 · outbound

This paper cites Dataset of breast ultrasound images.

TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation Dataset of breast ultrasound images

Reference 2

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Observation cda0f546-c6fc-4035-a08d-2c67d3a79ca6 · outbound

This paper cites Segmentation out- side the cranial vault challenge.

TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation Segmentation out- side the cranial vault challenge

Reference 3

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This paper cites Wm-dova maps for accurate polyp highlighting in colonoscopy: Validation vs.

TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation Wm-dova maps for accurate polyp highlighting in colonoscopy: Validation vs

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Observation b1b84e37-b6ec-4e08-adf0-ffa358a315f0 · outbound

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

TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation Swin-unet: Unet-like pure transformer for medical image segmentation

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Observation 135d1ecb-bf4a-48ea-9d81-b976bd31e856 · outbound

This paper cites A Generalized Surface Loss for Reducing the Hausdorff Distance in Medical Imaging Segmentation.

TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation A Generalized Surface Loss for Reducing the Hausdorff Distance in Medical Imaging Segmentation

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

TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

Reference 7

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Observation 270c2bc5-194e-42e4-8f07-4d4c2889df83 · outbound

This paper cites Lesion-inspired denoising network: Connecting medical image denoising and lesion detection.

TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation Lesion-inspired denoising network: Connecting medical image denoising and lesion detection

Reference 8

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Observation 39459233-b461-4642-9122-e859bd20be68 · outbound

This paper cites Tailoring therapies—improving the management of early breast cancer: St gallen international expert consensus on the primary therapy of early breast cancer 2015.

TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation Tailoring therapies—improving the management of early breast cancer: St gallen international expert consensus on the primary therapy of early breast cancer 2015

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Observation b23b1a77-d88e-45d4-ba8a-dab1fe72041b · outbound

This paper cites Polyp-PVT: Polyp Segmentation with Pyramid Vision Transformers.

TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation Polyp-PVT: Polyp Segmentation with Pyramid Vision Transformers

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Observation 6b258fab-bea2-4019-9bc8-9d14b0b07c35 · outbound

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

TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation An image is worth 16x16 words: Transformers for image recognition at scale

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Observation 41523765-b69f-4239-8c0e-b0968ac2f0f0 · outbound

This paper cites 3d dental mesh segmentation using semantics-based feature learning with graph-transformer.

TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation 3d dental mesh segmentation using semantics-based feature learning with graph-transformer

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Observation f113f498-45aa-4e6d-ab71-88e921a1ac91 · outbound

This paper cites Structure-measure: A new way to evaluate foreground maps.

TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation Structure-measure: A new way to evaluate foreground maps

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Observation b84d60c0-d08b-4506-9625-511a8b92ead8 · outbound

This paper cites Enhanced-alignment Measure for Binary Foreground Map Evaluation.

TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation Enhanced-alignment Measure for Binary Foreground Map Evaluation

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This paper cites Pranet: Parallel reverse attention network for polyp segmentation.

TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation Pranet: Parallel reverse attention network for polyp segmentation

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This paper cites Res2net: A new multi-scale backbone architecture.

TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation Res2net: A new multi-scale backbone architecture

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This paper cites Utnet: a hybrid transformer architecture for medical image segmen- tation.

TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation Utnet: a hybrid transformer architecture for medical image segmen- tation

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Observation 4b558c09-059b-422f-965c-aefe730f50c6 · outbound

This paper cites Do as ai say: susceptibility in deployment of clinical decision-aids.

TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation Do as ai say: susceptibility in deployment of clinical decision-aids

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Observation 6f09c619-eb84-4382-99ea-7c73287a1654 · outbound

This paper cites Ce-net: Context encoder network for 2d medical image segmentation.

TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation Ce-net: Context encoder network for 2d medical image segmentation

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Observation 1a53c745-8fa4-4c76-803c-9cb68ca89a23 · outbound

This paper cites Skin Lesion Analysis toward Melanoma Detection: A Challenge at the International Symposium on Biomedical Imaging (ISBI) 2016, hosted by the International Skin Imaging Collaboration (ISIC).

TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation Skin Lesion Analysis toward Melanoma Detection: A Challenge at the International Symposium on Biomedical Imaging (ISBI) 2016, hosted by the International Skin Imaging Collaboration (ISIC)

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Observation 73853aaa-0491-4e77-8988-67df481e5aff · outbound

This paper cites Vision gnn: An image is worth graph of nodes.

TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation Vision gnn: An image is worth graph of nodes

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This paper cites Vision hgnn: An image is more than a graph of nodes.

TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation Vision hgnn: An image is more than a graph of nodes

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This paper cites Image analysis using mathematical morphology.

TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation Image analysis using mathematical morphology

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Observation 78cbe6ff-42d0-4e31-82a7-6922632e974e · outbound

This paper cites Unetr: Transformers for 3d med- ical image segmentation.

TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation Unetr: Transformers for 3d med- ical image segmentation

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Observation 99c572c5-c88a-4fd3-b3fe-fde91e03de46 · outbound

This paper cites Deep residual learning for image recognition.

TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation Deep residual learning for image recognition

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This paper cites Hiformer: Hierarchical multi-scale representations using transformers for medical image seg- mentation.

TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation Hiformer: Hierarchical multi-scale representations using transformers for medical image seg- mentation

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Observation aabcd80f-f2bf-451e-9c86-58f8496d71e5 · outbound

This paper cites Kvasir-seg: A segmented polyp dataset.

TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation Kvasir-seg: A segmented polyp dataset

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Observation 09823cc8-3ae0-4f6a-a54e-67d5663483d1 · outbound

This paper cites Amos: A large-scale abdominal multi-organ benchmark for versatile medical image segmentation.

TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation Amos: A large-scale abdominal multi-organ benchmark for versatile medical image segmentation

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This paper cites Vig-unet: vision graph neural networks for medical image segmentation.

TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation Vig-unet: vision graph neural networks for medical image segmentation

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This paper cites COVID-19 CT Lung and Infection Segmenta- tion Dataset.

TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation COVID-19 CT Lung and Infection Segmenta- tion Dataset

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This paper cites Design of an image edge detection filter using the sobel operator.

TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation Design of an image edge detection filter using the sobel operator

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This paper cites Snakes: Active contour models.

TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation Snakes: Active contour models

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This paper cites Semi-Supervised Classification with Graph Convolutional Networks.

TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation Semi-Supervised Classification with Graph Convolutional Networks

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This paper cites Deepgcns: Can gcns go as deep as cnns? In Proceedings of the IEEE/CVF international conference on computer vision, pages 9267–9276, 2019.

TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation Deepgcns: Can gcns go as deep as cnns? In Proceedings of the IEEE/CVF international conference on computer vision, pages 9267–9276, 2019

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This paper cites Deeper insights into graph convolutional networks for semi-supervised learn- ing.

TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation Deeper insights into graph convolutional networks for semi-supervised learn- ing

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This paper cites Gse-nets: Global structure enhancement de- coder for thyroid nodule segmentation.

TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation Gse-nets: Global structure enhancement de- coder for thyroid nodule segmentation

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This paper cites Cafe-net: Cross- attention and feature exploration network for polyp segmen- tation.

TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation Cafe-net: Cross- attention and feature exploration network for polyp segmen- tation

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This paper cites Covsegnet: A multi encoder– decoder architecture for improved lesion segmentation of covid-19 chest ct scans.

TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation Covsegnet: A multi encoder– decoder architecture for improved lesion segmentation of covid-19 chest ct scans

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This paper cites Simple learned weighted sums of inferior temporal neuronal firing rates accurately predict human core object recognition performance.

TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation Simple learned weighted sums of inferior temporal neuronal firing rates accurately predict human core object recognition performance

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This paper cites How to evaluate foreground maps? In Proceedings of the IEEE con- ference on computer vision and pattern recognition , pages 248–255, 2014.

TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation How to evaluate foreground maps? In Proceedings of the IEEE con- ference on computer vision and pattern recognition , pages 248–255, 2014

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This paper cites Vision: A computational investigation into the human representation and processing of visual information.

TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation Vision: A computational investigation into the human representation and processing of visual information

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This paper cites Ph 2-a dermoscopic image database for research and benchmarking.

TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation Ph 2-a dermoscopic image database for research and benchmarking

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T20:07:25.215087Z

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This paper cites V-net: Fully convolutional neural networks for volumetric medical image segmentation.

TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation V-net: Fully convolutional neural networks for volumetric medical image segmentation

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This paper cites M3fpolypsegnet: Segmentation network with multi-frequency feature fusion for polyp localization in colonoscopy images.

TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation M3fpolypsegnet: Segmentation network with multi-frequency feature fusion for polyp localization in colonoscopy images

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This paper cites Modality-agnostic domain generalizable medical image segmentation by multi-frequency in multi- scale attention.

TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation Modality-agnostic domain generalizable medical image segmentation by multi-frequency in multi- scale attention

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This paper cites Graph neural networks expo- nentially lose expressive power for node classification.

TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation Graph neural networks expo- nentially lose expressive power for node classification

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This paper cites A threshold selection method from gray- level histograms.

TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation A threshold selection method from gray- level histograms

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This paper cites Vision science: Photons to phenomenol- ogy.

TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation Vision science: Photons to phenomenol- ogy

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This paper cites The attention system of the human brain.

TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation The attention system of the human brain

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Observation 4838be88-f436-4968-91ee-3526210a8f1c · outbound

This paper cites G-cascade: Efficient cascaded graph convolutional decoding for 2d med- ical image segmentation.

TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation G-cascade: Efficient cascaded graph convolutional decoding for 2d med- ical image segmentation

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This paper cites A new unsupervised approach for segment- ing and counting cells in high-throughput microscopy image sets.

TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation A new unsupervised approach for segment- ing and counting cells in high-throughput microscopy image sets

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Observation 30ecd8af-4fec-46ff-b51f-5f00b60d97fe · outbound

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

TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation U- net: Convolutional networks for biomedical image segmen- tation

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T20:07:25.065201Z

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 130c64fe-3ce4-45b6-9ba3-c7c0daad9526 · outbound

This paper cites A CT Image Denoising Method with Residual Encoder-Decoder Network.

TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation A CT Image Denoising Method with Residual Encoder-Decoder Network

Reference 53

Resolution: verified exact; local_arxiv, observed 2026-08-07T20:07:24.486589Z

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 24bbf8f7-4de3-4372-8f6c-b53a1fc18fa2 · outbound

This paper cites Toward embedded detection of polyps in wce images for early diagnosis of colorectal can- cer.

TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation Toward embedded detection of polyps in wce images for early diagnosis of colorectal can- cer

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T20:07:25.050874Z

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 b3488c4f-3ebd-4e91-81b2-d7b813dce720 · outbound

This paper cites Msrf-net: a multi- scale residual fusion network for biomedical image segmen- tation.

TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation Msrf-net: a multi- scale residual fusion network for biomedical image segmen- tation

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T20:07:25.036278Z

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 a6f9c169-64bc-4951-84ab-619e114f997c · outbound

This paper cites Automated polyp detection in colonoscopy videos using shape and context information.

TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation Automated polyp detection in colonoscopy videos using shape and context information

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T20:07:25.021086Z

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 9c9b109d-ead5-4124-bd94-a2f324d65ed0 · outbound

This paper cites Image thresholding using type ii fuzzy sets.

TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation Image thresholding using type ii fuzzy sets

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T20:07:25.006353Z

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 4026d6cf-ea1f-4ae7-af4e-3b8020906aff · outbound

This paper cites The fully convolutional trans- former for medical image segmentation.

TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation The fully convolutional trans- former for medical image segmentation

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T20:07:24.991206Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation da4e31b1-1be5-4fda-ae66-0ae9ce42bc1d · outbound

This paper cites Neural correlates of attention in primate visual cortex.

TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation Neural correlates of attention in primate visual cortex

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T20:07:24.976792Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 6293237f-a123-4537-9f43-77b74007f2bd · outbound

This paper cites A benchmark for endoluminal scene segmentation of colonoscopy images.

TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation A benchmark for endoluminal scene segmentation of colonoscopy images

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T20:07:24.962318Z

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 ec3a0c96-6eef-42d3-adef-8e41fd53dec3 · outbound

This paper cites Advent: Adversarial entropy min- imization for domain adaptation in semantic segmentation.

TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation Advent: Adversarial entropy min- imization for domain adaptation in semantic segmentation

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T20:07:24.948478Z

Source-reported events for the cited work

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Observation 098b6490-5e10-4429-975f-e07b320353bf · outbound

This paper cites Gazegnn: A gaze- guided graph neural network for chest x-ray classification.

TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation Gazegnn: A gaze- guided graph neural network for chest x-ray classification

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T20:07:24.934420Z

Source-reported events for the cited work

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Observation a2947de0-4ebe-44fd-84df-8b6c6264cf2a · outbound

This paper cites Cfatran- sunet: Channel-wise cross fusion attention and transformer for 2d medical image segmentation.

TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation Cfatran- sunet: Channel-wise cross fusion attention and transformer for 2d medical image segmentation

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T20:07:24.920793Z

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 dfc6c53a-447f-4e26-8aa7-54b94d459fcb · outbound

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

TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T20:07:24.908101Z

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 ce5cd246-8ee9-47a7-88ad-28c2c8ce3a3d · outbound

This paper cites Eca-net: Efficient channel at- tention for deep convolutional neural networks.

TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation Eca-net: Efficient channel at- tention for deep convolutional neural networks

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T20:07:24.895650Z

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 23dc8185-63f8-46a7-8a29-cb3b5083ddbc · outbound

This paper cites Boundary and entropy-driven ad- versarial learning for fundus image segmentation.

TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation Boundary and entropy-driven ad- versarial learning for fundus image segmentation

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T20:07:24.882462Z

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 fb060d0b-7774-46df-9b27-921ade2988fa · outbound

This paper cites Pvt v2: Improved baselines with pyramid vision transformer.

TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation Pvt v2: Improved baselines with pyramid vision transformer

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T20:07:24.869089Z

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 b7ac1136-f1bf-40c0-b654-c91fc6831cda · outbound

This paper cites Dynamic graph learning with content-guided spatial- frequency relation reasoning for deepfake detection.

TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation Dynamic graph learning with content-guided spatial- frequency relation reasoning for deepfake detection

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T20:07:24.855242Z

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 9d80f5f3-1b01-494d-ba5f-101d5629336b · outbound

This paper cites Multi-scale group agent attention- based graph convolutional decoding networks for 2d medi- cal image segmentation.

TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation Multi-scale group agent attention- based graph convolutional decoding networks for 2d medi- cal image segmentation

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T20:07:24.841769Z

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 bda400bd-3b56-4ea4-8c32-0195cc65a7ab · outbound

This paper cites P2t: Pyramid pooling transformer for scene understanding.

TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation P2t: Pyramid pooling transformer for scene understanding

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T20:07:24.828106Z

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 f7bf6b8f-5ffc-409f-9f0a-4a059a73b7e8 · outbound

This paper cites Dcsau- net: A deeper and more compact split-attention u-net for medical image segmentation.

TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation Dcsau- net: A deeper and more compact split-attention u-net for medical image segmentation

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T20:07:24.814517Z

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 dfb57a13-f6c3-41f9-a5c1-670be2d86c07 · outbound

This paper cites Graph-and transformer-guided boundary aware network for medical im- age segmentation.

TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation Graph-and transformer-guided boundary aware network for medical im- age segmentation

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T20:07:24.800970Z

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 558411b4-a771-4cb7-ae10-f77d26e72f53 · outbound

This paper cites Graph convolu- tional neural networks for web-scale recommender systems.

TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation Graph convolu- tional neural networks for web-scale recommender systems

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T20:07:24.787529Z

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 eb15e0e8-462f-4e23-8745-dd9602e47874 · outbound

This paper cites Resnest: Split-attention networks.

TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation Resnest: Split-attention networks

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T20:07:24.773426Z

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 fb63ac92-81aa-421d-b8a8-06bb834fbb66 · outbound

This paper cites Transgraphnet: A novel network for medical im- age segmentation based on transformer and graph convo- lution.

TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation Transgraphnet: A novel network for medical im- age segmentation based on transformer and graph convo- lution

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T20:07:24.759860Z

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 cd65fd28-3902-43e6-9530-bf0574704875 · outbound

This paper cites Hsnet: A hybrid semantic network for polyp segmentation.

TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation Hsnet: A hybrid semantic network for polyp segmentation

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T20:07:24.745415Z

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 aa8d39c2-1912-4505-85de-1eb0383e9992 · outbound

This paper cites Auto- matic polyp segmentation via multi-scale subtraction net- work.

TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation Auto- matic polyp segmentation via multi-scale subtraction net- work

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T20:07:24.731712Z

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 b8370a35-7f3e-4a31-8049-bbea57060618 · outbound

This paper cites M$^{2}$SNet: Multi-scale in Multi-scale Subtraction Network for Medical Image Segmentation.

TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation M$^{2}$SNet: Multi-scale in Multi-scale Subtraction Network for Medical Image Segmentation

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-07T20:07:24.382068Z

Source-reported events for the cited work

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Observation 6d01e367-2b85-4d1f-8a37-6f1e5b9dbb69 · outbound

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

TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation Unet++: A nested u-net ar- chitecture for medical image segmentation

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T20:07:24.718350Z

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 869b7b90-0b49-4ca0-87f5-c5df7d42d8c9 · outbound

This paper cites An rdau-net model for lesion segmentation in breast ultrasound images.

TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation An rdau-net model for lesion segmentation in breast ultrasound images

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T20:07:24.704897Z

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 acf755df-04a1-4d0a-abaf-9537205c83a2 · outbound

This paper cites Details of the medical segmentation seen clinical settings used in our experiments.

TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation Details of the medical segmentation seen clinical settings used in our experiments

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T20:07:24.691643Z

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 9dfa1f4f-81ab-41df-8aaf-ce8c755ccae3 · outbound

This paper cites In contrast, the STU dataset [80] includes only 42 breast ultrasound images collected by Shantou University.

TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation In contrast, the STU dataset [80] includes only 42 breast ultrasound images collected by Shantou University

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T20:07:24.677060Z

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 4dd5a271-a2dd-4dd5-8293-edb6b7d67478 · outbound

This paper cites To address this, we transform cross-scale feature maps into a graph and apply efficient node-level at- tention.

TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation To address this, we transform cross-scale feature maps into a graph and apply efficient node-level at- tention

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T20:07:24.661701Z

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 fd675014-9815-467c-a290-54d9f89f2dff · outbound

This paper cites Like our approach, PVT-GCASCADE utilizes GNN; however, it does not consider cross-scale information, which is limited to medical images with more diverse lesion sizes.

TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation Like our approach, PVT-GCASCADE utilizes GNN; however, it does not consider cross-scale information, which is limited to medical images with more diverse lesion sizes

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T20:07:24.645452Z

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 419b4b97-c155-4df1-9bf5-981aa9391f2a · outbound

This paper cites an unresolved cited work.

TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation Unresolved cited work

Reference 86

Resolution: malformed identifier; raw_fallback, observed 2026-08-07T20:07:24.630267Z

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 335d980b-2779-4e3a-8a43-eb221985833b · outbound

This paper cites an unresolved cited work.

TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation Unresolved cited work

Reference 87

Resolution: malformed identifier; raw_fallback, observed 2026-08-07T20:07:24.615959Z

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 ae8241be-799c-455f-ab14-18c4e4096641 · outbound

This paper cites For convenience, we denote T P, F P, and F Nas the num- ber of samples of true positive, false positive, and false neg- ative between two binary masks A and B.

TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation For convenience, we denote T P, F P, and F Nas the num- ber of samples of true positive, false positive, and false neg- ative between two binary masks A and B

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T20:07:24.601348Z

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 d21ac382-9ce4-41d1-8be5-b63de055ba31 · outbound

This paper cites 12, 13, 14, 15 for binary segmenta- tion.

TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation 12, 13, 14, 15 for binary segmenta- tion

Reference 89

Resolution: malformed identifier; raw_fallback, observed 2026-08-07T20:07:24.586604Z

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 2e4a0aca-e74d-462a-b14d-b45f193e41cf · outbound

This paper cites 6, 1, 5, 9.

TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation 6, 1, 5, 9

Reference 5440

Resolution: parse uncertain; raw_fallback, observed 2026-08-07T20:07:25.200793Z

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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Outbound references

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