Paper Citation Record · LEDGER

FM-G-CAM: A Holistic Approach for Explainable AI in Computer Vision

As of 21 August 2026, this Paper Citation Record lists 56 of 56 outbound references and 2 inbound Pith citation observations for arXiv:2312.05975.

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

pith.paper-citation-record.v1
2312.05975 v3

Coverage vector

measured 56 of 56 reference resolution

Typed states for the displayed outbound observations.

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measured 58 of 58 standing notices

One-hop event checks from named stored sources.

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measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T04:20:41.736834Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T09:51:00.945032Z

Reference resolution

56 of 56 outbound references displayed

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External citation measurements

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Outbound references

Observation 024ae010-365c-4209-8c02-fc4585ba3445 · outbound

This paper cites Explainable artiﬁcial intelligence (xai): Concepts, taxonomies, opportunities and challenges toward responsible ai.

FM-G-CAM: A Holistic Approach for Explainable AI in Computer Vision Explainable artiﬁcial intelligence (xai): Concepts, taxonomies, opportunities and challenges toward responsible ai

Reference 1

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Observation 22b95779-a6f0-46c7-adbd-f268e2f16651 · outbound

This paper cites Explain able artiﬁcial intelligence: a comprehensive review.

FM-G-CAM: A Holistic Approach for Explainable AI in Computer Vision Explain able artiﬁcial intelligence: a comprehensive review

Reference 2

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Observation 46d91c83-49c2-4d9f-91a2-ea89e5501329 · outbound

This paper cites Application of explainable artiﬁcial intelligence for hea lthcare: A systematic review of the last decade (2011– 2022).

FM-G-CAM: A Holistic Approach for Explainable AI in Computer Vision Application of explainable artiﬁcial intelligence for hea lthcare: A systematic review of the last decade (2011– 2022)

Reference 3

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Observation 606718fb-cbd4-4bb5-b322-220ef287ad76 · outbound

This paper cites Tuberculosis detecti on in chest radiograph using convolutional neural network architecture and explainable artiﬁcial intellige nce.

FM-G-CAM: A Holistic Approach for Explainable AI in Computer Vision Tuberculosis detecti on in chest radiograph using convolutional neural network architecture and explainable artiﬁcial intellige nce

Reference 4

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Observation f13c7a88-71fe-44e5-b1a8-e9a2a4100e7d · outbound

This paper cites Alzheimer’s disease analysis using explainable artiﬁcial intelligenc e (xai).

FM-G-CAM: A Holistic Approach for Explainable AI in Computer Vision Alzheimer’s disease analysis using explainable artiﬁcial intelligenc e (xai)

Reference 5

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Observation 5f5ffe0e-6943-4a5d-8c88-2d714dc04354 · outbound

This paper cites Review of deep learning: Concepts, cnn architectures, challenges, applications, future directi ons.

FM-G-CAM: A Holistic Approach for Explainable AI in Computer Vision Review of deep learning: Concepts, cnn architectures, challenges, applications, future directi ons

Reference 6

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Observation 1b6bead3-6a51-442f-8d4f-029fa9cc7501 · outbound

This paper cites A survey on vision transformer.

FM-G-CAM: A Holistic Approach for Explainable AI in Computer Vision A survey on vision transformer

Reference 7

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Observation ee6ad196-96b4-413e-8676-00d2475a623e · outbound

This paper cites Transformers in vision: A survey.

FM-G-CAM: A Holistic Approach for Explainable AI in Computer Vision Transformers in vision: A survey

Reference 8

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Observation 90e0057c-7dc2-43ee-90a2-2632c247cd65 · outbound

This paper cites A survey of methods for explaining black box models.

FM-G-CAM: A Holistic Approach for Explainable AI in Computer Vision A survey of methods for explaining black box models

Reference 9

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Observation 1a47a3bd-e278-4c82-8f25-b4b794793f24 · outbound

This paper cites Grad-cam: Visual explanations from deep networks vi a gradient-based localization.

FM-G-CAM: A Holistic Approach for Explainable AI in Computer Vision Grad-cam: Visual explanations from deep networks vi a gradient-based localization

Reference 10

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Observation 33ac0a16-8c5f-4f4d-80b8-444ef12a4240 · outbound

This paper cites Grad-cam++: Gener- alized gradient-based visual explanations for deep convol utional networks.

FM-G-CAM: A Holistic Approach for Explainable AI in Computer Vision Grad-cam++: Gener- alized gradient-based visual explanations for deep convol utional networks

Reference 11

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Observation 2cfd8f59-4f03-4697-bae4-4ab22203e67a · outbound

This paper cites Deep learn ing (cnn) and transfer learning: a review.

FM-G-CAM: A Holistic Approach for Explainable AI in Computer Vision Deep learn ing (cnn) and transfer learning: a review

Reference 12

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Observation 59262870-f9a3-459c-b785-e000af3437c1 · outbound

This paper cites Imagenet: A large-scale hierarchical image database.

FM-G-CAM: A Holistic Approach for Explainable AI in Computer Vision Imagenet: A large-scale hierarchical image database

Reference 13

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Observation 2dcc5e04-c3af-4b3a-be31-75d425ea2b9d · outbound

This paper cites A survey of methods for explaining black box models.

FM-G-CAM: A Holistic Approach for Explainable AI in Computer Vision A survey of methods for explaining black box models

Reference 14

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Observation 2cd02345-e17f-412a-86da-f2821da9acb4 · outbound

This paper cites Rajendra Acharya.

FM-G-CAM: A Holistic Approach for Explainable AI in Computer Vision Rajendra Acharya

Reference 15

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Observation 1a24aa1b-7361-4c39-9602-a7b870ea1379 · outbound

This paper cites Transformer inter pretability beyond attention visualization.

FM-G-CAM: A Holistic Approach for Explainable AI in Computer Vision Transformer inter pretability beyond attention visualization

Reference 16

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Observation 10a249ab-e2b0-4e4f-bec1-1f37368be9d4 · outbound

This paper cites Vision transformer in stenosis detection of coronary arter ies.

FM-G-CAM: A Holistic Approach for Explainable AI in Computer Vision Vision transformer in stenosis detection of coronary arter ies

Reference 17

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Observation ae89443d-721c-4b9e-a76b-0af63db8de89 · outbound

This paper cites Imagenet classiﬁcation with deep convolutional neural networks.

FM-G-CAM: A Holistic Approach for Explainable AI in Computer Vision Imagenet classiﬁcation with deep convolutional neural networks

Reference 18

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Observation 70045f84-3d19-4a95-8350-1c4b774678a2 · outbound

This paper cites Automated 3d fe rrograph image analysis for similar particle identiﬁcation with the knowledge-embedded double-cnn mod el.

FM-G-CAM: A Holistic Approach for Explainable AI in Computer Vision Automated 3d fe rrograph image analysis for similar particle identiﬁcation with the knowledge-embedded double-cnn mod el

Reference 19

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Observation b92716ef-d8f7-4020-9fa3-66ce9ae2a12f · outbound

This paper cites Rauber, Samuel G.

FM-G-CAM: A Holistic Approach for Explainable AI in Computer Vision Rauber, Samuel G

Reference 20

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Observation 98956066-b87a-4861-bfc4-8de81d2e39a1 · outbound

This paper cites Visualizing de ep convolutional neural networks using natural pre- images.

FM-G-CAM: A Holistic Approach for Explainable AI in Computer Vision Visualizing de ep convolutional neural networks using natural pre- images

Reference 21

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Observation 3e35e956-788d-4e31-865d-831676aff94a · outbound

This paper cites Network dissection: Quantifying interpretability of deep visual representations.

FM-G-CAM: A Holistic Approach for Explainable AI in Computer Vision Network dissection: Quantifying interpretability of deep visual representations

Reference 22

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Observation c6fc5b82-fe46-40c4-9736-23a0ba1ee0a6 · outbound

This paper cites Zeiler and Rob Fergus.

FM-G-CAM: A Holistic Approach for Explainable AI in Computer Vision Zeiler and Rob Fergus

Reference 23

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Observation 8f918b63-ec23-4d5e-8b06-6b49ade387f4 · outbound

This paper cites Visualizing deep neural network decisions: Prediction difference analysis.

FM-G-CAM: A Holistic Approach for Explainable AI in Computer Vision Visualizing deep neural network decisions: Prediction difference analysis

Reference 24

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This paper cites Deep inside conv olutional networks: visualising image classiﬁcation models and saliency maps.

FM-G-CAM: A Holistic Approach for Explainable AI in Computer Vision Deep inside conv olutional networks: visualising image classiﬁcation models and saliency maps

Reference 25

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Observation 6c3b4b8f-0a45-48bf-8385-91e2d72fb69a · outbound

This paper cites Striving for simplicity: The all convolu- tional net.

FM-G-CAM: A Holistic Approach for Explainable AI in Computer Vision Striving for simplicity: The all convolu- tional net

Reference 26

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Observation 004b7594-a836-46c7-b80f-1a7aed96ab43 · outbound

This paper cites On pixel-wise explanations for non-linear cla ssiﬁer decisions by layer-wise relevance propagation.

FM-G-CAM: A Holistic Approach for Explainable AI in Computer Vision On pixel-wise explanations for non-linear cla ssiﬁer decisions by layer-wise relevance propagation

Reference 27

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Observation 73dfb137-85ad-4990-8f74-cd2a8bf36010 · outbound

This paper cites Axiomat ic attribution for deep networks.

FM-G-CAM: A Holistic Approach for Explainable AI in Computer Vision Axiomat ic attribution for deep networks

Reference 28

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Observation 8561bfca-e7d4-490e-ac55-b02ba57594c6 · outbound

This paper cites Ex- plaining nonlinear classiﬁcation decisions with deep tayl or decomposition.

FM-G-CAM: A Holistic Approach for Explainable AI in Computer Vision Ex- plaining nonlinear classiﬁcation decisions with deep tayl or decomposition

Reference 29

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Observation c4a37676-31f1-4102-98c7-a4f282f1eced · outbound

This paper cites Learning deep features for discriminative localization.

FM-G-CAM: A Holistic Approach for Explainable AI in Computer Vision Learning deep features for discriminative localization

Reference 30

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Observation 2f31a8dd-0548-444c-8033-1b718df8da29 · outbound

This paper cites Axiom-based Grad-CAM: Towards Accurate Visualization and Explanation of CNNs.

FM-G-CAM: A Holistic Approach for Explainable AI in Computer Vision Axiom-based Grad-CAM: Towards Accurate Visualization and Explanation of CNNs

Reference 31

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Observation 0c2686b0-6fe1-4032-94b6-0b99ef058dd5 · outbound

This paper cites Use HiResCAM instead of Grad-CAM for faithful explanations of convolutional neural networks.

FM-G-CAM: A Holistic Approach for Explainable AI in Computer Vision Use HiResCAM instead of Grad-CAM for faithful explanations of convolutional neural networks

Reference 32

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Observation aa5fcd62-9958-4dcd-805a-c5c4fc3193a9 · outbound

This paper cites Seg-xres-cam: Explaining spatially local regions in image segmentation.

FM-G-CAM: A Holistic Approach for Explainable AI in Computer Vision Seg-xres-cam: Explaining spatially local regions in image segmentation

Reference 33

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Observation c0235c9d-901d-4e30-a050-6365f660df1e · outbound

This paper cites Ramaswamy.

FM-G-CAM: A Holistic Approach for Explainable AI in Computer Vision Ramaswamy

Reference 34

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Observation b8250e97-0f5e-43d5-8dfb-bebe9c838b1c · outbound

This paper cites Score- cam: Score-weighted visual explanations for convolutiona l neural networks.

FM-G-CAM: A Holistic Approach for Explainable AI in Computer Vision Score- cam: Score-weighted visual explanations for convolutiona l neural networks

Reference 35

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Observation 4fecda44-9875-49cc-9699-49553b5bea74 · outbound

This paper cites Eigen-cam : Class activation map using principal com- ponents.

FM-G-CAM: A Holistic Approach for Explainable AI in Computer Vision Eigen-cam : Class activation map using principal com- ponents

Reference 36

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Observation 001bc5c3-9a71-4c80-8ae8-77311476dd7b · outbound

This paper cites Layercam: Exploring hierarchical class activation maps for localization.

FM-G-CAM: A Holistic Approach for Explainable AI in Computer Vision Layercam: Exploring hierarchical class activation maps for localization

Reference 37

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This paper cites Deep feature factorization for concept discovery.

FM-G-CAM: A Holistic Approach for Explainable AI in Computer Vision Deep feature factorization for concept discovery

Reference 38

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Observation 057ed16a-a660-48d5-94b2-8fc39dd148b1 · outbound

This paper cites Somewhere over the rainbow: A n empirical assessment of quantitative colormaps.

FM-G-CAM: A Holistic Approach for Explainable AI in Computer Vision Somewhere over the rainbow: A n empirical assessment of quantitative colormaps

Reference 39

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Observation df261eb0-8cae-4858-b5b9-6b3c6ecb2ab6 · outbound

This paper cites Augmented grad- cam: Heat-maps super resolution through augmentation.

FM-G-CAM: A Holistic Approach for Explainable AI in Computer Vision Augmented grad- cam: Heat-maps super resolution through augmentation

Reference 40

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Observation 3757b4e6-21db-4c72-ab84-7dfb4f44aa5c · outbound

This paper cites RISE: Randomized Input Sampling for Explanation of Black-box Models.

FM-G-CAM: A Holistic Approach for Explainable AI in Computer Vision RISE: Randomized Input Sampling for Explanation of Black-box Models

Reference 41

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Observation 26146166-ee35-4d91-aaa0-3155bccd02e6 · outbound

This paper cites Black-box explanation of object detectors via sali ency maps.

FM-G-CAM: A Holistic Approach for Explainable AI in Computer Vision Black-box explanation of object detectors via sali ency maps

Reference 42

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Observation f5387bc4-36b2-4618-9775-ac2b6b95f1b6 · outbound

This paper cites Met rics for saliency map evaluation of deep learning explanation methods.

FM-G-CAM: A Holistic Approach for Explainable AI in Computer Vision Met rics for saliency map evaluation of deep learning explanation methods

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:46:01.300209Z

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Observation 6c42d9da-b99d-4543-9f13-3ad0754a1d0d · outbound

This paper cites I dentity mappings in deep residual networks.

FM-G-CAM: A Holistic Approach for Explainable AI in Computer Vision I dentity mappings in deep residual networks

Reference 44

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Observation b9c73e78-b984-43e3-83c0-a661b8071797 · outbound

This paper cites Microsoft coco: Common objects in conte xt.

FM-G-CAM: A Holistic Approach for Explainable AI in Computer Vision Microsoft coco: Common objects in conte xt

Reference 45

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Observation f24ee45a-d026-4424-9f93-0964a90fa06e · outbound

This paper cites Convoluti onal neural networks in medical image understanding: a survey.

FM-G-CAM: A Holistic Approach for Explainable AI in Computer Vision Convoluti onal neural networks in medical image understanding: a survey

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:46:01.171207Z

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Observation 872c214b-25f9-44a5-aea2-c275c448755c · outbound

This paper cites Chexpe rt: A large chest radiograph dataset with uncer- tainty labels and expert comparison.

FM-G-CAM: A Holistic Approach for Explainable AI in Computer Vision Chexpe rt: A large chest radiograph dataset with uncer- tainty labels and expert comparison

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:46:01.184961Z

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Observation ae767a21-680a-4d8f-b438-da4d14564181 · outbound

This paper cites A cnn model: earlier diagnosis and classiﬁcation of alzheimer disease u sing mri.

FM-G-CAM: A Holistic Approach for Explainable AI in Computer Vision A cnn model: earlier diagnosis and classiﬁcation of alzheimer disease u sing mri

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:46:01.196705Z

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Observation 7188ccbb-96d8-4ec5-9631-b17cbe032711 · outbound

This paper cites Computer-aided diagnosis of breast ultrasound images usin g ensemble learning from convolutional neural net- works.

FM-G-CAM: A Holistic Approach for Explainable AI in Computer Vision Computer-aided diagnosis of breast ultrasound images usin g ensemble learning from convolutional neural net- works

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:46:01.191647Z

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Observation 17e98815-eddc-461b-9d00-e64d228e9a5a · outbound

This paper cites Torchxrayvision: A library of chest x- ray datasets and models.

FM-G-CAM: A Holistic Approach for Explainable AI in Computer Vision Torchxrayvision: A library of chest x- ray datasets and models

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:46:01.164133Z

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Observation d472f97c-39c9-4227-bc3e-aa159ecaaef0 · outbound

This paper cites Densely connected convolutional networks.

FM-G-CAM: A Holistic Approach for Explainable AI in Computer Vision Densely connected convolutional networks

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:46:01.202675Z

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Observation 93b09133-657e-4677-9deb-8249fa5dbed7 · outbound

This paper cites Object detectors emerge in deep scene cnns.

FM-G-CAM: A Holistic Approach for Explainable AI in Computer Vision Object detectors emerge in deep scene cnns

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:46:01.197926Z

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Observation 6e7dc0fd-a833-4d53-a46d-1b8e22d7ab8c · outbound

This paper cites Dot-net: Document layout classiﬁcati on using texture-based cnn.

FM-G-CAM: A Holistic Approach for Explainable AI in Computer Vision Dot-net: Document layout classiﬁcati on using texture-based cnn

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:46:01.247525Z

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Observation 450f730b-f49c-433d-ab99-ad9ce81d0a3c · outbound

This paper cites Predicting clustered weather patterns: A test case for applications of convolutional neural networks to spati o-temporal climate data.

FM-G-CAM: A Holistic Approach for Explainable AI in Computer Vision Predicting clustered weather patterns: A test case for applications of convolutional neural networks to spati o-temporal climate data

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:46:01.063279Z

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Observation 280ea098-02fd-4683-b394-596ad0b2bf73 · outbound

This paper cites Efﬁcient multi-sc ale 3d cnn with fully connected crf for accurate brain lesion segmentation.

FM-G-CAM: A Holistic Approach for Explainable AI in Computer Vision Efﬁcient multi-sc ale 3d cnn with fully connected crf for accurate brain lesion segmentation

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:46:01.095479Z

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Observation 23b4d02b-c537-42bd-8c30-961882705e00 · outbound

This paper cites Hyperdense-net: a hyper-densely connected cnn for multi-m odal image segmentation.

FM-G-CAM: A Holistic Approach for Explainable AI in Computer Vision Hyperdense-net: a hyper-densely connected cnn for multi-m odal image segmentation

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:46:01.212752Z

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Pith citing papers

Observation a1bc115a-1f23-48d8-b16e-3ce8a5f9e91d · inbound

ArtBrain: An Explainable end-to-end Toolkit for Classification and Attribution of AI-Generated Art and Style cites this paper.

ArtBrain: An Explainable end-to-end Toolkit for Classification and Attribution of AI-Generated Art and Style FM-G-CAM: A Holistic Approach for Explainable AI in Computer Vision

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-12T04:20:41.736834Z

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Observation c7914ec9-d475-4628-91fc-7c8be13dd1ee · inbound

Efficient KernelSHAP Explanations for Patch-based 3D Medical Image Segmentation cites this paper.

Efficient KernelSHAP Explanations for Patch-based 3D Medical Image Segmentation FM-G-CAM: A Holistic Approach for Explainable AI in Computer Vision

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-20T00:00:14.616347Z

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