Paper Citation Record · LEDGER

Kidney tumor segmentation using an ensembling multi-stage deep learning approach. A contribution to the KiTS19 challenge

As of 14 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 0 inbound Pith citation observations for arXiv:1909.00735.

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

pith.paper-citation-record.v1
1909.00735 v1

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T05:43:29.279888Z

measured 31 of 31 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

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

31 of 31 outbound references displayed

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verified fuzzy29
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a9b09db8-55c5-44f1-ac4d-7e941c8394d1 · outbound

This paper cites CA: a cancer journal for clinicians 68(6) (2018) 394–424.

Kidney tumor segmentation using an ensembling multi-stage deep learning approach. A contribution to the KiTS19 challenge CA: a cancer journal for clinicians 68(6) (2018) 394–424

Reference 1

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Observation 3085565f-95ae-4a8f-82ac-afda225d02ab · outbound

This paper cites Journal of Clinical Oncology 36(36) (2018) 3574–3581 10 G.Santini et al.

Kidney tumor segmentation using an ensembling multi-stage deep learning approach. A contribution to the KiTS19 challenge Journal of Clinical Oncology 36(36) (2018) 3574–3581 10 G.Santini et al

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:43:31.530853Z

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Observation 355bc8f5-2315-46b3-9d0a-b96fee65ebfe · outbound

This paper cites The Journal of urology 176(6) (2006) 2397–2400.

Kidney tumor segmentation using an ensembling multi-stage deep learning approach. A contribution to the KiTS19 challenge The Journal of urology 176(6) (2006) 2397–2400

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:43:31.516361Z

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Observation 2ea06724-9289-4bfb-a5af-56c205108d59 · outbound

This paper cites Annals of surgical oncology 19(7) (2012) 2380–2387.

Kidney tumor segmentation using an ensembling multi-stage deep learning approach. A contribution to the KiTS19 challenge Annals of surgical oncology 19(7) (2012) 2380–2387

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:43:31.404232Z

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Observation 269b4423-de57-451c-9610-2fd2c0158729 · outbound

This paper cites Journal of minimal access surgery 7(4) (2011) 205.

Kidney tumor segmentation using an ensembling multi-stage deep learning approach. A contribution to the KiTS19 challenge Journal of minimal access surgery 7(4) (2011) 205

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:43:31.330833Z

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Observation fa0a8a76-da7d-48a4-9f44-f33a125647f1 · outbound

This paper cites European urology 56(5) (2009) 786–793.

Kidney tumor segmentation using an ensembling multi-stage deep learning approach. A contribution to the KiTS19 challenge European urology 56(5) (2009) 786–793

Reference 6

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Observation 093c421a-c276-42c7-9e2b-133ee1be7485 · outbound

This paper cites The Journal of urology 182(3) (2009) 844–853.

Kidney tumor segmentation using an ensembling multi-stage deep learning approach. A contribution to the KiTS19 challenge The Journal of urology 182(3) (2009) 844–853

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:43:31.291744Z

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Observation e99cc47e-052d-4618-adb1-1b18d08a5924 · outbound

This paper cites The Journal of urology 183(5) (2010) 1708–1713.

Kidney tumor segmentation using an ensembling multi-stage deep learning approach. A contribution to the KiTS19 challenge The Journal of urology 183(5) (2010) 1708–1713

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:43:31.195240Z

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Observation ac7662f2-f392-44b8-9e3f-2dab88f70c6b · outbound

This paper cites Computer methods and programs in biomedicine 157 (2018) 49–67.

Kidney tumor segmentation using an ensembling multi-stage deep learning approach. A contribution to the KiTS19 challenge Computer methods and programs in biomedicine 157 (2018) 49–67

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:43:31.146883Z

Source-reported events for the cited work

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

Observation d29205d0-9850-48f8-bcd7-0e704924700e · outbound

This paper cites Medical image analysis 42 (2017) 60–88.

Kidney tumor segmentation using an ensembling multi-stage deep learning approach. A contribution to the KiTS19 challenge Medical image analysis 42 (2017) 60–88

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:43:31.114389Z

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

Observation aea70011-7205-4543-ad1a-48f12173d240 · outbound

This paper cites Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization 6(3) (2018) 277–282.

Kidney tumor segmentation using an ensembling multi-stage deep learning approach. A contribution to the KiTS19 challenge Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization 6(3) (2018) 277–282

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:43:30.978057Z

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

Observation 7ebbdc8a-cbe1-41f1-b12e-e0e8e75c51b5 · outbound

This paper cites In: Deep Learning and Convolutional Neural Networks for Medical Image Computing.

Kidney tumor segmentation using an ensembling multi-stage deep learning approach. A contribution to the KiTS19 challenge In: Deep Learning and Convolutional Neural Networks for Medical Image Computing

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:43:30.867503Z

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Observation 18ea1517-dedb-4438-91a5-59eadbe39705 · outbound

This paper cites Frontiers in oncology 8 (2018) 215.

Kidney tumor segmentation using an ensembling multi-stage deep learning approach. A contribution to the KiTS19 challenge Frontiers in oncology 8 (2018) 215

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:43:30.740421Z

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Observation 40d72de3-18f3-47f5-a306-9ed817322604 · outbound

This paper cites Scientiﬁc reports 7(1) (2017) 2049.

Kidney tumor segmentation using an ensembling multi-stage deep learning approach. A contribution to the KiTS19 challenge Scientiﬁc reports 7(1) (2017) 2049

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:43:30.725469Z

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Observation 48892f0b-c466-48c8-8658-fa3777601144 · outbound

This paper cites Radiology 158(1) (1986) 1–10.

Kidney tumor segmentation using an ensembling multi-stage deep learning approach. A contribution to the KiTS19 challenge Radiology 158(1) (1986) 1–10

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:43:30.579720Z

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Observation a144c1e6-0a32-461a-aebb-996ca8f57be3 · outbound

This paper cites Acta radiologica 45(7) (2004) 791–795.

Kidney tumor segmentation using an ensembling multi-stage deep learning approach. A contribution to the KiTS19 challenge Acta radiologica 45(7) (2004) 791–795

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:43:30.483162Z

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

Observation c7609af5-186e-41f0-b3cc-7981c157a9f3 · outbound

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Kidney tumor segmentation using an ensembling multi-stage deep learning approach. A contribution to the KiTS19 challenge Unresolved cited work

Reference 17

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Observation 9e6c81ac-ee87-46b8-9416-ddac0b424a22 · outbound

This paper cites The KiTS19 Challenge Data: 300 Kidney Tumor Cases with Clinical Context, CT Semantic Segmentations, and Surgical Outcomes.

Kidney tumor segmentation using an ensembling multi-stage deep learning approach. A contribution to the KiTS19 challenge The KiTS19 Challenge Data: 300 Kidney Tumor Cases with Clinical Context, CT Semantic Segmentations, and Surgical Outcomes

Reference 18

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Observation ba9b759c-6854-4954-8e4d-fa995a634108 · outbound

This paper cites In: International Conference on Medical image computing and computer-assisted intervention, Springer (2015) 234–241.

Kidney tumor segmentation using an ensembling multi-stage deep learning approach. A contribution to the KiTS19 challenge In: International Conference on Medical image computing and computer-assisted intervention, Springer (2015) 234–241

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:43:30.268233Z

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

Observation f4af8f6c-9500-4ed3-bb8e-028bdf194090 · outbound

This paper cites In: European conference on computer vision, Springer (2016) 630–645.

Kidney tumor segmentation using an ensembling multi-stage deep learning approach. A contribution to the KiTS19 challenge In: European conference on computer vision, Springer (2016) 630–645

Reference 20

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

Observation 6960e889-9c14-4270-beef-b04bbe8dffe2 · outbound

This paper cites In: Interna- tional conference on medical image computing and computer-assisted intervention, Springer (2014) 520–527.

Kidney tumor segmentation using an ensembling multi-stage deep learning approach. A contribution to the KiTS19 challenge In: Interna- tional conference on medical image computing and computer-assisted intervention, Springer (2014) 520–527

Reference 21

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

Observation e9703199-4996-402b-ab09-5778cedcd63d · outbound

This paper cites Medical image analysis 34 (2016) 123–136.

Kidney tumor segmentation using an ensembling multi-stage deep learning approach. A contribution to the KiTS19 challenge Medical image analysis 34 (2016) 123–136

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:43:30.076635Z

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

Observation 671868d9-4048-464c-8add-d9e350a8a61f · outbound

This paper cites IEEE transactions on medical imaging 35(5) (2016) 1160–1169.

Kidney tumor segmentation using an ensembling multi-stage deep learning approach. A contribution to the KiTS19 challenge IEEE transactions on medical imaging 35(5) (2016) 1160–1169

Reference 23

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Observation d43436bc-3c70-4a8a-ac3f-4d756e21e848 · outbound

This paper cites In: Medical Imaging 2016: Computer-Aided Diagnosis.

Kidney tumor segmentation using an ensembling multi-stage deep learning approach. A contribution to the KiTS19 challenge In: Medical Imaging 2016: Computer-Aided Diagnosis

Reference 24

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Observation b00220e8-85f2-4d9e-885a-f2d495f60220 · outbound

This paper cites In: European conference on computer vision, Springer (2016) 694–711.

Kidney tumor segmentation using an ensembling multi-stage deep learning approach. A contribution to the KiTS19 challenge In: European conference on computer vision, Springer (2016) 694–711

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:43:29.788417Z

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Observation 30160b53-bf29-4b28-9e0e-b9cbb77ef5ac · outbound

This paper cites In: SegTHOR@ ISBI.

Kidney tumor segmentation using an ensembling multi-stage deep learning approach. A contribution to the KiTS19 challenge In: SegTHOR@ ISBI

Reference 26

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This paper cites In: Scandinavian Conference on Image Analysis, Springer (2015) 201–211.

Kidney tumor segmentation using an ensembling multi-stage deep learning approach. A contribution to the KiTS19 challenge In: Scandinavian Conference on Image Analysis, Springer (2015) 201–211

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:43:29.652764Z

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Observation c55eae52-11f9-4f17-9081-7403e4627316 · outbound

This paper cites In: International MICCAI Brainlesion Workshop, Springer (2017) 450–462.

Kidney tumor segmentation using an ensembling multi-stage deep learning approach. A contribution to the KiTS19 challenge In: International MICCAI Brainlesion Workshop, Springer (2017) 450–462

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:43:29.638125Z

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Observation 9fd50304-b856-431b-b1be-d763a78c333f · outbound

This paper cites In: 12th{USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 16).

Kidney tumor segmentation using an ensembling multi-stage deep learning approach. A contribution to the KiTS19 challenge In: 12th{USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 16)

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:43:29.434030Z

Source-reported events for the cited work

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

Observation 429ca141-07e1-49d5-b569-6d8bd67c6a26 · outbound

This paper cites In: Pro- ceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops.

Kidney tumor segmentation using an ensembling multi-stage deep learning approach. A contribution to the KiTS19 challenge In: Pro- ceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:43:29.419912Z

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

Observation 9a96e7b5-928b-456e-a1f5-0abf80813f4e · outbound

This paper cites IEEE transactions on pattern analysis and machine intelligence 40(4) (2017) 834–848.

Kidney tumor segmentation using an ensembling multi-stage deep learning approach. A contribution to the KiTS19 challenge IEEE transactions on pattern analysis and machine intelligence 40(4) (2017) 834–848

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:43:29.374652Z

Source-reported events for the cited work

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

Pith citing papers

No inbound Pith citation observations are available.