Paper Citation Record · LEDGER

Deep Ensemble approach for Enhancing Brain Tumor Segmentation in Resource-Limited Settings

As of 12 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 0 inbound Pith citation observations for arXiv:2502.02179.

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

pith.paper-citation-record.v1
2502.02179 v1

Coverage vector

measured 32 of 32 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T13:09:08.854762Z

measured 32 of 32 standing notices

One-hop event checks from named stored sources.

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

32 of 32 outbound references displayed

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External citation measurements

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Outbound references

Observation 8f40c1f2-5de8-4a5e-a304-74332aa2feb6 · outbound

This paper cites Brain tumor segmentation with deep neural networks.

Deep Ensemble approach for Enhancing Brain Tumor Segmentation in Resource-Limited Settings Brain tumor segmentation with deep neural networks

Reference 1

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Observation c4eddad6-cb02-4256-b56a-0da8eb0641b3 · outbound

This paper cites Review of MRI-based brain tumor image segmentation using deep learning methods.

Deep Ensemble approach for Enhancing Brain Tumor Segmentation in Resource-Limited Settings Review of MRI-based brain tumor image segmentation using deep learning methods

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T13:09:09.207671Z

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Observation 9df63628-9c69-448c-b2ac-27419325aec0 · outbound

This paper cites Deep Learning for Brain Tumor Segmentation: A Survey of State-of- the-Art.

Deep Ensemble approach for Enhancing Brain Tumor Segmentation in Resource-Limited Settings Deep Learning for Brain Tumor Segmentation: A Survey of State-of- the-Art

Reference 3

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Observation 14b1e2b0-44a2-4d23-ab49-eac2b7952e65 · outbound

This paper cites Drrnet: dense residual refine networks for automatic brain tumor segmentation.

Deep Ensemble approach for Enhancing Brain Tumor Segmentation in Resource-Limited Settings Drrnet: dense residual refine networks for automatic brain tumor segmentation

Reference 4

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Observation 927b53e7-45a6-4e4d-b14a-4852f285903e · outbound

This paper cites Adult brain tumors in Sub-Saharan africa: A scoping review.

Deep Ensemble approach for Enhancing Brain Tumor Segmentation in Resource-Limited Settings Adult brain tumors in Sub-Saharan africa: A scoping review

Reference 5

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Observation b3af9306-3f89-4035-9dad-545e23995194 · outbound

This paper cites Status of magnetic resonance imaging systems and quality control programs in nigeria.

Deep Ensemble approach for Enhancing Brain Tumor Segmentation in Resource-Limited Settings Status of magnetic resonance imaging systems and quality control programs in nigeria

Reference 6

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Observation a6edf1f1-efb8-4c68-84ce-8092df8c0cd9 · outbound

This paper cites Ai for population and global health in radiology, 2022.

Deep Ensemble approach for Enhancing Brain Tumor Segmentation in Resource-Limited Settings Ai for population and global health in radiology, 2022

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T13:09:09.152927Z

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Observation 7f22b059-d647-4c4e-8d90-d177acf44bb2 · outbound

This paper cites Automatic brain-tumor diagnosis using cascaded deep convolutional neural networks with symmetric u-net and asymmetric residual-blocks.

Deep Ensemble approach for Enhancing Brain Tumor Segmentation in Resource-Limited Settings Automatic brain-tumor diagnosis using cascaded deep convolutional neural networks with symmetric u-net and asymmetric residual-blocks

Reference 8

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Observation 13eb5a8d-83a6-4cbb-a84f-478cadec338e · outbound

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

Deep Ensemble approach for Enhancing Brain Tumor Segmentation in Resource-Limited Settings U-net: Convolutional networks for biomedical image segmentation

Reference 9

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Observation 7f22fef0-bdec-4a70-98bc-de7d635dd0e3 · outbound

This paper cites Somasundaram and R.

Deep Ensemble approach for Enhancing Brain Tumor Segmentation in Resource-Limited Settings Somasundaram and R

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T13:09:09.125515Z

Source-reported events for the cited work

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Observation ef80d660-87cd-464d-bae8-ae52447a5194 · outbound

This paper cites Segment anything model for medical images? Medical Image Analysis, 92:103061, 2024.

Deep Ensemble approach for Enhancing Brain Tumor Segmentation in Resource-Limited Settings Segment anything model for medical images? Medical Image Analysis, 92:103061, 2024

Reference 11

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Observation 35b78ef3-2d45-474b-b65b-e0ae0646c290 · outbound

This paper cites an unresolved cited work.

Deep Ensemble approach for Enhancing Brain Tumor Segmentation in Resource-Limited Settings Unresolved cited work

Reference 13

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Observation 85b3d7e2-3046-4007-a6f0-3cd3d4375236 · outbound

This paper cites Optimized U-Net for Brain Tumor Segmentation.

Deep Ensemble approach for Enhancing Brain Tumor Segmentation in Resource-Limited Settings Optimized U-Net for Brain Tumor Segmentation

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-08-09T13:09:08.919190Z

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Observation 9fa23c6e-ca34-4272-98db-ca1dce9b03b7 · outbound

This paper cites Towards SAMBA: Segment Anything Model for Brain Tumor Segmentation in Sub-Sharan African Populations.

Deep Ensemble approach for Enhancing Brain Tumor Segmentation in Resource-Limited Settings Towards SAMBA: Segment Anything Model for Brain Tumor Segmentation in Sub-Sharan African Populations

Reference 15

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Observation e5e9301d-3777-435f-842a-1e3ceb52ddb0 · outbound

This paper cites Bringing mri to low-and middle-income countries: directions, challenges and potential solutions.

Deep Ensemble approach for Enhancing Brain Tumor Segmentation in Resource-Limited Settings Bringing mri to low-and middle-income countries: directions, challenges and potential solutions

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T13:09:09.095757Z

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Observation bdcdb3f4-ca36-4814-9bdc-3772ceff56f0 · outbound

This paper cites Efficient brain tumor segmentation with multiscale two-pathway-group conventional neural networks.

Deep Ensemble approach for Enhancing Brain Tumor Segmentation in Resource-Limited Settings Efficient brain tumor segmentation with multiscale two-pathway-group conventional neural networks

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T13:09:09.084623Z

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Observation fbbffd0b-5ff0-4cdc-8717-5b354ce043e4 · outbound

This paper cites The emerging roles of artificial intelligence in cancer drug development and precision therapy.

Deep Ensemble approach for Enhancing Brain Tumor Segmentation in Resource-Limited Settings The emerging roles of artificial intelligence in cancer drug development and precision therapy

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T13:09:09.074278Z

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Observation aedf5f20-369f-4557-b1bd-66ec1a079e13 · outbound

This paper cites Segmentation of organs-at-risks in head and neck ct images using convolutional neural networks.

Deep Ensemble approach for Enhancing Brain Tumor Segmentation in Resource-Limited Settings Segmentation of organs-at-risks in head and neck ct images using convolutional neural networks

Reference 19

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Observation 53b85497-64bd-4e94-aa08-7559050d2e1d · outbound

This paper cites Clinical evaluation of atlas and deep learning based automatic contouring for lung cancer.

Deep Ensemble approach for Enhancing Brain Tumor Segmentation in Resource-Limited Settings Clinical evaluation of atlas and deep learning based automatic contouring for lung cancer

Reference 20

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Observation 146bf274-668f-4364-903a-1a040430015a · outbound

This paper cites Deep learning renal segmentation for fully automated radiation dose estimation in unsealed source therapy.

Deep Ensemble approach for Enhancing Brain Tumor Segmentation in Resource-Limited Settings Deep learning renal segmentation for fully automated radiation dose estimation in unsealed source therapy

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T13:09:09.043043Z

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Observation 1baa4205-5424-47f1-bb26-2407db675563 · outbound

This paper cites Automatic 3d liver segmentation based on deep learning and globally optimized surface evolution.

Deep Ensemble approach for Enhancing Brain Tumor Segmentation in Resource-Limited Settings Automatic 3d liver segmentation based on deep learning and globally optimized surface evolution

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T13:09:09.032033Z

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Observation dac0a8d6-1c74-4537-bb35-d472f4568ff3 · outbound

This paper cites Combining deep learning with anatomical analysis for segmentation of the portal vein for liver sbrt planning.

Deep Ensemble approach for Enhancing Brain Tumor Segmentation in Resource-Limited Settings Combining deep learning with anatomical analysis for segmentation of the portal vein for liver sbrt planning

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T13:09:09.020818Z

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Observation 91a85e1d-e5ee-485d-8e5b-3a3c3063b3eb · outbound

This paper cites A hybrid attention-based residual unet for semantic segmentation of brain tumor.

Deep Ensemble approach for Enhancing Brain Tumor Segmentation in Resource-Limited Settings A hybrid attention-based residual unet for semantic segmentation of brain tumor

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T13:09:09.010338Z

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Observation b8e65473-54b0-4ae5-a2d6-d8ad7673fef2 · outbound

This paper cites Automated Ensemble-Based Segmentation of Adult Brain Tumors: A Novel Approach Using the BraTS AFRICA Challenge Data.

Deep Ensemble approach for Enhancing Brain Tumor Segmentation in Resource-Limited Settings Automated Ensemble-Based Segmentation of Adult Brain Tumors: A Novel Approach Using the BraTS AFRICA Challenge Data

Reference 25

Resolution: verified exact; local_arxiv, observed 2026-08-09T13:09:08.905720Z

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Observation a6e54e0e-e778-4a9a-a391-094d721e7fd6 · outbound

This paper cites An Optimization Framework for Processing and Transfer Learning for the Brain Tumor Segmentation.

Deep Ensemble approach for Enhancing Brain Tumor Segmentation in Resource-Limited Settings An Optimization Framework for Processing and Transfer Learning for the Brain Tumor Segmentation

Reference 26

Resolution: verified exact; local_arxiv, observed 2026-08-09T13:09:08.889981Z

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Observation c7612836-fd22-4378-90e9-64f1216198e5 · outbound

This paper cites Extending nn-unet for brain tumor segmentation, 2021.

Deep Ensemble approach for Enhancing Brain Tumor Segmentation in Resource-Limited Settings Extending nn-unet for brain tumor segmentation, 2021

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T13:09:09.000156Z

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Observation 7d1f7f4a-09f4-4904-bf75-5b05ac0ec2e5 · outbound

This paper cites Lienkamp, Thomas Brox, and Olaf Ronneberger.

Deep Ensemble approach for Enhancing Brain Tumor Segmentation in Resource-Limited Settings Lienkamp, Thomas Brox, and Olaf Ronneberger

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T13:09:08.989266Z

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Observation 01902f9b-bab1-43a6-913c-16cc64b0b707 · outbound

This paper cites V-net: Fully convolutional neural networks for volumetric medical image segmentation, 2016.

Deep Ensemble approach for Enhancing Brain Tumor Segmentation in Resource-Limited Settings V-net: Fully convolutional neural networks for volumetric medical image segmentation, 2016

Reference 29

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Observation d3bce903-d34e-4493-9864-91db2c8385cd · outbound

This paper cites MSA-VNet: Multi-scale Attention-based V-Net for DCE-MRI Lesion Segmentation.

Deep Ensemble approach for Enhancing Brain Tumor Segmentation in Resource-Limited Settings MSA-VNet: Multi-scale Attention-based V-Net for DCE-MRI Lesion Segmentation

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T13:09:08.971035Z

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Observation f996f837-6b4f-49f5-b8d7-fbc766440d1e · outbound

This paper cites Review of MRI-based Brain Tumor Image Segmentation Using Deep Learning Methods.

Deep Ensemble approach for Enhancing Brain Tumor Segmentation in Resource-Limited Settings Review of MRI-based Brain Tumor Image Segmentation Using Deep Learning Methods

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T13:09:08.961095Z

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Observation ca4fe429-190b-463c-84e6-2044385d16cf · outbound

This paper cites Brain tumor segmentation in multi-parametric magnetic resonance imaging using model ensembling and super-resolution, 07 2022.

Deep Ensemble approach for Enhancing Brain Tumor Segmentation in Resource-Limited Settings Brain tumor segmentation in multi-parametric magnetic resonance imaging using model ensembling and super-resolution, 07 2022

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T13:09:08.950729Z

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Observation d439c5ee-7a59-44fa-bd3a-05a9fed61cab · outbound

This paper cites Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation.

Deep Ensemble approach for Enhancing Brain Tumor Segmentation in Resource-Limited Settings Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T13:09:08.938954Z

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Pith citing papers

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