Paper Citation Record · LEDGER

No Masks Needed: Explainable AI for Deriving Segmentation from Classification

As of 23 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 0 inbound Pith citation observations for arXiv:2508.04534.

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

pith.paper-citation-record.v1
2508.04534 v1

Coverage vector

measured 30 of 30 reference resolution

Typed states for the displayed outbound observations.

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measured 30 of 30 standing notices

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Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Reference resolution

30 of 30 outbound references displayed

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External citation measurements

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Outbound references

Observation c10b9911-aec0-4710-984b-798f4a063399 · outbound

This paper cites Deep learning for medical image segmentation: State-of-the-art advancements and challenges,.

No Masks Needed: Explainable AI for Deriving Segmentation from Classification Deep learning for medical image segmentation: State-of-the-art advancements and challenges,

Reference 1

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Observation 3fc1397d-1277-4f69-af4b-f39d33d5e39b · outbound

This paper cites A brief introduction to weakly supervised learning,.

No Masks Needed: Explainable AI for Deriving Segmentation from Classification A brief introduction to weakly supervised learning,

Reference 2

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Observation 08372a33-e6a9-4dc3-a50d-48ebe451e454 · outbound

This paper cites A comprehensive survey on transfer learning,.

No Masks Needed: Explainable AI for Deriving Segmentation from Classification A comprehensive survey on transfer learning,

Reference 3

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Observation 55f53883-5fed-44bd-99fd-adc224c75f20 · outbound

This paper cites Self- supervised transformers for unsupervised object discovery using normalized cut,.

No Masks Needed: Explainable AI for Deriving Segmentation from Classification Self- supervised transformers for unsupervised object discovery using normalized cut,

Reference 4

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Observation b48be7a9-2bc9-4ae9-95d6-818ea47d8819 · outbound

This paper cites Cut and learn for unsupervised object detection and instance segmentation,.

No Masks Needed: Explainable AI for Deriving Segmentation from Classification Cut and learn for unsupervised object detection and instance segmentation,

Reference 5

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Observation cdcd738e-cc3c-4d48-a8ce-ab43e5ae76d9 · outbound

This paper cites Unsuper- vised object localization: Observing the background to discover objects,.

No Masks Needed: Explainable AI for Deriving Segmentation from Classification Unsuper- vised object localization: Observing the background to discover objects,

Reference 6

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Observation 59436881-bbba-4a6b-8668-87e8d7f552d4 · outbound

This paper cites Explainable artificial intelligence approaches: A survey,.

No Masks Needed: Explainable AI for Deriving Segmentation from Classification Explainable artificial intelligence approaches: A survey,

Reference 7

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Observation e7cc6fc3-734d-49f2-bf9a-696a7aad27f2 · outbound

This paper cites Medical image segmentation: a review,.

No Masks Needed: Explainable AI for Deriving Segmentation from Classification Medical image segmentation: a review,

Reference 8

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This paper cites A review of medical image segmentation algorithms,.

No Masks Needed: Explainable AI for Deriving Segmentation from Classification A review of medical image segmentation algorithms,

Reference 9

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Observation 524be732-b441-44c6-af78-a7f55d4af41d · outbound

This paper cites Handbook of medical imaging: V olume 2, medical image processing and analysis,.

No Masks Needed: Explainable AI for Deriving Segmentation from Classification Handbook of medical imaging: V olume 2, medical image processing and analysis,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:01:56.809449Z

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Observation 4765f435-65f1-441e-adc8-efd5a050bcb8 · outbound

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

No Masks Needed: Explainable AI for Deriving Segmentation from Classification An image is worth 16x16 words: Transformers for image recognition at scale,

Reference 11

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Observation 108af07d-b00a-4974-9f69-cb95019b6150 · outbound

This paper cites Emerging properties in self-supervised vision transformers,.

No Masks Needed: Explainable AI for Deriving Segmentation from Classification Emerging properties in self-supervised vision transformers,

Reference 12

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This paper cites Axiomatic attribution for deep networks,.

No Masks Needed: Explainable AI for Deriving Segmentation from Classification Axiomatic attribution for deep networks,

Reference 13

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Observation de57f4c1-25a7-412c-8fb9-3311d7bbb4c4 · outbound

This paper cites Grad-cam: Why did you say that?,.

No Masks Needed: Explainable AI for Deriving Segmentation from Classification Grad-cam: Why did you say that?,

Reference 14

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Observation 9dd2c7a8-c666-41c0-8d2a-3cf1d16dcf9d · outbound

This paper cites From explanations to segmentation: Using explainable ai for image segmentation,.

No Masks Needed: Explainable AI for Deriving Segmentation from Classification From explanations to segmentation: Using explainable ai for image segmentation,

Reference 15

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Observation 6967ae4d-c388-43df-b2fe-c3fb9ce3f4e6 · outbound

This paper cites From classification to segmentation with explainable ai: A study on crack detection and growth monitoring,.

No Masks Needed: Explainable AI for Deriving Segmentation from Classification From classification to segmentation with explainable ai: A study on crack detection and growth monitoring,

Reference 16

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Observation fe8581ae-8cc5-4dae-ab59-5659b0485fe0 · outbound

This paper cites Growing a brain: Fine-tuning by increasing model capacity,.

No Masks Needed: Explainable AI for Deriving Segmentation from Classification Growing a brain: Fine-tuning by increasing model capacity,

Reference 17

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This paper cites Captum: A unified and generic model interpretability library for pytorch,.

No Masks Needed: Explainable AI for Deriving Segmentation from Classification Captum: A unified and generic model interpretability library for pytorch,

Reference 18

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Observation 0fd8203c-7202-4fef-bfcb-f36ae3b3f04a · outbound

This paper cites Deep ViT Features as Dense Visual Descriptors.

No Masks Needed: Explainable AI for Deriving Segmentation from Classification Deep ViT Features as Dense Visual Descriptors

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This paper cites Survey over image thresholding techniques and quantitative perfor- mance evaluation,.

No Masks Needed: Explainable AI for Deriving Segmentation from Classification Survey over image thresholding techniques and quantitative perfor- mance evaluation,

Reference 20

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Observation e3d8a315-9f1a-459b-bd5c-67cdf849dbdb · outbound

This paper cites Adaptive histogram equalization and its variations,.

No Masks Needed: Explainable AI for Deriving Segmentation from Classification Adaptive histogram equalization and its variations,

Reference 21

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This paper cites Normalized cuts and image segmentation,.

No Masks Needed: Explainable AI for Deriving Segmentation from Classification Normalized cuts and image segmentation,

Reference 22

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No Masks Needed: Explainable AI for Deriving Segmentation from Classification Efficient inference in fully connected crfs with gaussian edge potentials,

Reference 23

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No Masks Needed: Explainable AI for Deriving Segmentation from Classification A curated mammography data set for use in computer-aided detection and diagnosis research,

Reference 24

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This paper cites Nuinsseg: A fully annotated dataset for nuclei instance segmentation in h&e-stained histological images,.

No Masks Needed: Explainable AI for Deriving Segmentation from Classification Nuinsseg: A fully annotated dataset for nuclei instance segmentation in h&e-stained histological images,

Reference 25

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No Masks Needed: Explainable AI for Deriving Segmentation from Classification Kvasir-seg: A segmented polyp dataset,

Reference 26

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No Masks Needed: Explainable AI for Deriving Segmentation from Classification Automatic differentiation in pytorch,

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No Masks Needed: Explainable AI for Deriving Segmentation from Classification A stochastic approximation method,

Reference 28

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No Masks Needed: Explainable AI for Deriving Segmentation from Classification Microscopy analysis neural network to solve detection, enumeration and segmentation from image-level annotations,

Reference 29

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No Masks Needed: Explainable AI for Deriving Segmentation from Classification Neuronal activity remodels the f-actin based submembrane lattice in dendrites but not axons of hippocampal neurons,

Reference 30

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Pith citing papers

No inbound Pith citation observations are available.