Paper Citation Record · LEDGER

A Novel Convolutional-Free Method for 3D Medical Imaging Segmentation

As of 9 August 2026, this Paper Citation Record lists 62 of 62 outbound references and 1 inbound Pith citation observation for arXiv:2502.05396.

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

pith.paper-citation-record.v1
2502.05396 v1

Coverage vector

measured 62 of 62 reference resolution

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measured 63 of 63 standing notices

One-hop event checks from named stored sources.

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-04T23:02:50.218186Z

Reference resolution

62 of 62 outbound references displayed

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External citation measurements

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Outbound references

Observation 548a6c98-a787-42a7-b429-f934f48c58c0 · outbound

This paper cites Retrospective reconstruction of three-dimensional radiotherapy treatment plans of the thorax from two dimensional planning data.

A Novel Convolutional-Free Method for 3D Medical Imaging Segmentation Retrospective reconstruction of three-dimensional radiotherapy treatment plans of the thorax from two dimensional planning data

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:33:23.842026Z

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Observation 0650ff6e-56e2-44c0-b702-3a3c708f3934 · outbound

This paper cites Antonelli, A.

A Novel Convolutional-Free Method for 3D Medical Imaging Segmentation Antonelli, A

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:33:23.712914Z

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Observation 9bd4c49d-31b8-4e01-9eba-b63120a9f7a3 · outbound

This paper cites Badrinarayanan, A.

A Novel Convolutional-Free Method for 3D Medical Imaging Segmentation Badrinarayanan, A

Reference 3

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Observation d8555cff-6378-49b1-8f8e-cbbe80364160 · outbound

This paper cites Medical Artificial Intelligence for Early Detection of Lung Cancer: A Survey.

A Novel Convolutional-Free Method for 3D Medical Imaging Segmentation Medical Artificial Intelligence for Early Detection of Lung Cancer: A Survey

Reference 4

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This paper cites MSDet: Receptive Field Enhanced Multiscale Detection for Tiny Pulmonary Nodule.

A Novel Convolutional-Free Method for 3D Medical Imaging Segmentation MSDet: Receptive Field Enhanced Multiscale Detection for Tiny Pulmonary Nodule

Reference 5

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Observation 815d7b1e-86bd-4350-96cf-7616d2e779e6 · outbound

This paper cites End-to-end object detection with transformers.

A Novel Convolutional-Free Method for 3D Medical Imaging Segmentation End-to-end object detection with transformers

Reference 6

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Observation 30b6c718-c211-44b6-9ee7-4c16624ec8d8 · outbound

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

A Novel Convolutional-Free Method for 3D Medical Imaging Segmentation TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

Reference 7

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Observation e6fa6498-2734-45ae-b37e-3b7f51d81a3c · outbound

This paper cites Schwing, Alexander Kirillov, and Rohit Girdhar.

A Novel Convolutional-Free Method for 3D Medical Imaging Segmentation Schwing, Alexander Kirillov, and Rohit Girdhar

Reference 8

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A Novel Convolutional-Free Method for 3D Medical Imaging Segmentation Unresolved cited work

Reference 9

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Observation f5ac06ab-e5bb-468b-80b5-03da1f0469b2 · outbound

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

A Novel Convolutional-Free Method for 3D Medical Imaging Segmentation An image is worth 16x16 words: Transform- ers for image recognition at scale

Reference 10

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A Novel Convolutional-Free Method for 3D Medical Imaging Segmentation Unresolved cited work

Reference 12

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Observation d1feccaa-40b0-4394-b403-7c0646b2da79 · outbound

This paper cites ESA: Annotation-Efficient Active Learning for Semantic Segmentation.

A Novel Convolutional-Free Method for 3D Medical Imaging Segmentation ESA: Annotation-Efficient Active Learning for Semantic Segmentation

Reference 13

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This paper cites Non-contrast computed tomography in acute ischaemic stroke: A pictorial review.

A Novel Convolutional-Free Method for 3D Medical Imaging Segmentation Non-contrast computed tomography in acute ischaemic stroke: A pictorial review

Reference 14

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This paper cites Masked autoencoders are scalable vision learners.

A Novel Convolutional-Free Method for 3D Medical Imaging Segmentation Masked autoencoders are scalable vision learners

Reference 15

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Observation 5b27f528-99c6-4d24-b3d4-ea9ef528e838 · outbound

This paper cites Momentum contrast for unsupervised visual representation learning.

A Novel Convolutional-Free Method for 3D Medical Imaging Segmentation Momentum contrast for unsupervised visual representation learning

Reference 16

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Observation 41c5475b-c1ae-4e64-af47-e7f407daba80 · outbound

This paper cites Deep residual learning for image recognition.

A Novel Convolutional-Free Method for 3D Medical Imaging Segmentation Deep residual learning for image recognition

Reference 17

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Observation 7db626b8-1269-4cf8-b3fc-caeaacd57b7c · outbound

This paper cites Can rotational thromboe- lastometry rapidly identify theragnostic targets in isolated traumatic brain injury? Emergency Medicine Australasia, 37(1):e14480, 2025.

A Novel Convolutional-Free Method for 3D Medical Imaging Segmentation Can rotational thromboe- lastometry rapidly identify theragnostic targets in isolated traumatic brain injury? Emergency Medicine Australasia, 37(1):e14480, 2025

Reference 18

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Observation 18f129b0-4b2f-4b14-a2bb-0463fbdab21b · outbound

This paper cites Jaeger, Simon A.

A Novel Convolutional-Free Method for 3D Medical Imaging Segmentation Jaeger, Simon A

Reference 19

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Observation 04d5c1d5-9ad3-4ad3-883c-bf5b4d74d0ec · outbound

This paper cites Efficient Learning With Sine-Activated Low-rank Matrices.

A Novel Convolutional-Free Method for 3D Medical Imaging Segmentation Efficient Learning With Sine-Activated Low-rank Matrices

Reference 20

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This paper cites Impact of slice thickness, pixel size, and ct dose on the performance of automatic contouring algorithms.

A Novel Convolutional-Free Method for 3D Medical Imaging Segmentation Impact of slice thickness, pixel size, and ct dose on the performance of automatic contouring algorithms

Reference 21

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Observation 7a3a20b0-9394-4f70-b5cd-25c08c5bf9ed · outbound

This paper cites Convolution-free medical image segmenta- tion using transformer networks.

A Novel Convolutional-Free Method for 3D Medical Imaging Segmentation Convolution-free medical image segmenta- tion using transformer networks

Reference 22

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Observation 7b0eead4-19d2-4c71-9066-cd3d0198dd12 · outbound

This paper cites Quantifying Translation-Invariance in Convolutional Neural Networks.

A Novel Convolutional-Free Method for 3D Medical Imaging Segmentation Quantifying Translation-Invariance in Convolutional Neural Networks

Reference 23

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A Novel Convolutional-Free Method for 3D Medical Imaging Segmentation Unresolved cited work

Reference 24

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Observation 0dd3db29-41d7-4064-84a9-87006d8e210d · outbound

This paper cites Deep Learning for Medical Image Segmentation.

A Novel Convolutional-Free Method for 3D Medical Imaging Segmentation Deep Learning for Medical Image Segmentation

Reference 25

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This paper cites Lecun, L.

A Novel Convolutional-Free Method for 3D Medical Imaging Segmentation Lecun, L

Reference 26

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This paper cites Superhuman Accuracy on the SNEMI3D Connectomics Challenge.

A Novel Convolutional-Free Method for 3D Medical Imaging Segmentation Superhuman Accuracy on the SNEMI3D Connectomics Challenge

Reference 27

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This paper cites Downsampling for binary classification with a highly imbalanced dataset using active learning.

A Novel Convolutional-Free Method for 3D Medical Imaging Segmentation Downsampling for binary classification with a highly imbalanced dataset using active learning

Reference 28

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This paper cites Swin transformer: Hierarchical vision transformer using shifted windows.

A Novel Convolutional-Free Method for 3D Medical Imaging Segmentation Swin transformer: Hierarchical vision transformer using shifted windows

Reference 29

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This paper cites Hough-cnn: Deep learning for segmentation of deep brain regions in mri and ultrasound.

A Novel Convolutional-Free Method for 3D Medical Imaging Segmentation Hough-cnn: Deep learning for segmentation of deep brain regions in mri and ultrasound

Reference 30

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Observation 539dff5b-5e61-4d84-9b21-e178fd3e2974 · outbound

This paper cites ProjectedEx: Enhancing Generation in Explainable AI for Prostate Cancer.

A Novel Convolutional-Free Method for 3D Medical Imaging Segmentation ProjectedEx: Enhancing Generation in Explainable AI for Prostate Cancer

Reference 31

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This paper cites MedConv: Convolutions Beat Transformers on Long-Tailed Bone Density Prediction.

A Novel Convolutional-Free Method for 3D Medical Imaging Segmentation MedConv: Convolutions Beat Transformers on Long-Tailed Bone Density Prediction

Reference 32

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This paper cites Stand-alone self-attention in vision models.

A Novel Convolutional-Free Method for 3D Medical Imaging Segmentation Stand-alone self-attention in vision models

Reference 33

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This paper cites Rau, S.C.

A Novel Convolutional-Free Method for 3D Medical Imaging Segmentation Rau, S.C

Reference 34

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This paper cites Redmon, S.

A Novel Convolutional-Free Method for 3D Medical Imaging Segmentation Redmon, S

Reference 35

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

A Novel Convolutional-Free Method for 3D Medical Imaging Segmentation U-net: Convolutional networks for biomedical image segmentation

Reference 36

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A Novel Convolutional-Free Method for 3D Medical Imaging Segmentation Unresolved cited work

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This paper cites Shirai, K.

A Novel Convolutional-Free Method for 3D Medical Imaging Segmentation Shirai, K

Reference 38

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This paper cites Very deep convolutional networks for large-scale image recogni- tion.

A Novel Convolutional-Free Method for 3D Medical Imaging Segmentation Very deep convolutional networks for large-scale image recogni- tion

Reference 39

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This paper cites SegKAN: High-Resolution Medical Image Segmentation with Long-Distance Dependencies.

A Novel Convolutional-Free Method for 3D Medical Imaging Segmentation SegKAN: High-Resolution Medical Image Segmentation with Long-Distance Dependencies

Reference 40

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This paper cites SegStitch: Multidimensional Transformer for Robust and Efficient Medical Imaging Segmentation.

A Novel Convolutional-Free Method for 3D Medical Imaging Segmentation SegStitch: Multidimensional Transformer for Robust and Efficient Medical Imaging Segmentation

Reference 41

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This paper cites Performance evaluation of deep learning networks for semantic segmentation of traffic stereo-pair images.

A Novel Convolutional-Free Method for 3D Medical Imaging Segmentation Performance evaluation of deep learning networks for semantic segmentation of traffic stereo-pair images

Reference 42

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This paper cites A Survey of Semantic Segmentation.

A Novel Convolutional-Free Method for 3D Medical Imaging Segmentation A Survey of Semantic Segmentation

Reference 43

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This paper cites Attention is all you need.

A Novel Convolutional-Free Method for 3D Medical Imaging Segmentation Attention is all you need

Reference 44

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This paper cites Learning to model the tail.

A Novel Convolutional-Free Method for 3D Medical Imaging Segmentation Learning to model the tail

Reference 45

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This paper cites Bhsd: A 3d multi-class brain hemorrhage segmentation dataset.

A Novel Convolutional-Free Method for 3D Medical Imaging Segmentation Bhsd: A 3d multi-class brain hemorrhage segmentation dataset

Reference 46

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This paper cites MMCLIP: Cross-modal Attention Masked Modelling for Medical Language-Image Pre-Training.

A Novel Convolutional-Free Method for 3D Medical Imaging Segmentation MMCLIP: Cross-modal Attention Masked Modelling for Medical Language-Image Pre-Training

Reference 47

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This paper cites GAMED-Snake: Gradient-aware Adaptive Momentum Evolution Deep Snake Model for Multi-organ Segmentation.

A Novel Convolutional-Free Method for 3D Medical Imaging Segmentation GAMED-Snake: Gradient-aware Adaptive Momentum Evolution Deep Snake Model for Multi-organ Segmentation

Reference 48

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This paper cites Deep Long-Tailed Learning: A Survey.

A Novel Convolutional-Free Method for 3D Medical Imaging Segmentation Deep Long-Tailed Learning: A Survey

Reference 49

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This paper cites A deep learning approach to diabetes diagnosis.

A Novel Convolutional-Free Method for 3D Medical Imaging Segmentation A deep learning approach to diabetes diagnosis

Reference 50

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Observation 913bc36f-c13a-472f-978b-4f05e36fc2d0 · outbound

This paper cites KMM: Key Frame Mask Mamba for Extended Motion Generation.

A Novel Convolutional-Free Method for 3D Medical Imaging Segmentation KMM: Key Frame Mask Mamba for Extended Motion Generation

Reference 51

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This paper cites InfiniMotion: Mamba Boosts Memory in Transformer for Arbitrary Long Motion Generation.

A Novel Convolutional-Free Method for 3D Medical Imaging Segmentation InfiniMotion: Mamba Boosts Memory in Transformer for Arbitrary Long Motion Generation

Reference 52

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This paper cites Motion mamba: Efficient and long sequence motion generation.

A Novel Convolutional-Free Method for 3D Medical Imaging Segmentation Motion mamba: Efficient and long sequence motion generation

Reference 53

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Observation 0fd7faa0-60a3-4df0-93f1-3c7878768a96 · outbound

This paper cites Jointvit: Modeling oxygen saturation levels with joint supervision on long-tailed octa.

A Novel Convolutional-Free Method for 3D Medical Imaging Segmentation Jointvit: Modeling oxygen saturation levels with joint supervision on long-tailed octa

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:33:21.855045Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 10f9fa02-2d78-41b7-a6f7-32e59f8bd2c6 · outbound

This paper cites Segreg: Segmenting oars by registering mr images and ct annotations.

A Novel Convolutional-Free Method for 3D Medical Imaging Segmentation Segreg: Segmenting oars by registering mr images and ct annotations

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:33:21.846500Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ab86ea1b-1d87-4b66-a7f4-4b90dfbd88db · outbound

This paper cites Motion Avatar: Generate Human and Animal Avatars with Arbitrary Motion.

A Novel Convolutional-Free Method for 3D Medical Imaging Segmentation Motion Avatar: Generate Human and Animal Avatars with Arbitrary Motion

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-08T19:33:21.555487Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 06484411-88f3-4987-91fd-27e472f1b0be · outbound

This paper cites Meddet: Generative adversarial distillation for efficient cervical disc herniation detection.

A Novel Convolutional-Free Method for 3D Medical Imaging Segmentation Meddet: Generative adversarial distillation for efficient cervical disc herniation detection

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:33:21.837541Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f66905e9-c81d-49bc-81d8-9b7e358804e1 · outbound

This paper cites Thin-thick adapter: Segmenting thin scans using thick annotations.

A Novel Convolutional-Free Method for 3D Medical Imaging Segmentation Thin-thick adapter: Segmenting thin scans using thick annotations

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:33:21.827776Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3ce89231-f4a1-454c-96d5-f7018485d7bd · outbound

This paper cites A landmark-based approach for instability prediction in distal radius fractures.

A Novel Convolutional-Free Method for 3D Medical Imaging Segmentation A landmark-based approach for instability prediction in distal radius fractures

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:33:21.819005Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7682fcdb-5edf-4048-8e5a-8b9982faa9c3 · outbound

This paper cites nnFormer: Interleaved Transformer for Volumetric Segmentation.

A Novel Convolutional-Free Method for 3D Medical Imaging Segmentation nnFormer: Interleaved Transformer for Volumetric Segmentation

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-08T19:33:21.567784Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3ff098b5-f510-4f49-b742-a3a0f269647e · outbound

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

A Novel Convolutional-Free Method for 3D Medical Imaging Segmentation Unet++: A nested u-net architecture for medical image segmentation

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:33:21.810076Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 115fced7-c8d4-4d0f-bbb1-714cc18f3bbc · outbound

This paper cites Zou, S.K.

A Novel Convolutional-Free Method for 3D Medical Imaging Segmentation Zou, S.K

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:33:21.800415Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 39a900a7-2ad3-4823-94ab-83af19be3934 · outbound

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

A Novel Convolutional-Free Method for 3D Medical Imaging Segmentation Lienkamp, Thomas Brox, and Olaf Ronneberger

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:33:21.791186Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Pith citing papers

Observation d4264993-daeb-439b-b39b-3b2fe8363c78 · inbound

Barlow-Swin: Toward a novel siamese-based segmentation architecture using Swin-Transformers cites this paper.

Barlow-Swin: Toward a novel siamese-based segmentation architecture using Swin-Transformers A Novel Convolutional-Free Method for 3D Medical Imaging Segmentation

Reference 2025

Resolution: metadata mismatch; local_arxiv, observed 2026-08-04T23:02:50.223335Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.