Paper Citation Record · LEDGER

MedGrad E-CLIP: Enhancing Trust and Transparency in AI-Driven Skin Lesion Diagnosis

As of 15 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2501.06887.

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

pith.paper-citation-record.v1
2501.06887 v1

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T20:52:33.932308Z

measured 36 of 36 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

36 of 36 outbound references displayed

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External citation measurements

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Outbound references

Observation 7fd28d63-4a0b-4961-bd4b-3b8726e628ba · outbound

This paper cites Deep learning tech- niques for skin lesion analysis and melanoma cancer detec- tion: a survey of state-of-the-art.

MedGrad E-CLIP: Enhancing Trust and Transparency in AI-Driven Skin Lesion Diagnosis Deep learning tech- niques for skin lesion analysis and melanoma cancer detec- tion: a survey of state-of-the-art

Reference 1

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Observation 08cd248b-5e11-4ae9-9811-7a115d975f68 · outbound

This paper cites An enhanced tech- nique of skin cancer classification using deep convolutional neural network with transfer learning models.

MedGrad E-CLIP: Enhancing Trust and Transparency in AI-Driven Skin Lesion Diagnosis An enhanced tech- nique of skin cancer classification using deep convolutional neural network with transfer learning models

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:52:34.484043Z

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Observation 36e96d8e-00ce-40e3-a12a-633894fd1b67 · outbound

This paper cites On pixel-wise explanations for non-linear classi- fier decisions by layer-wise relevance propagation.PloS one, 10(7):e0130140, 2015.

MedGrad E-CLIP: Enhancing Trust and Transparency in AI-Driven Skin Lesion Diagnosis On pixel-wise explanations for non-linear classi- fier decisions by layer-wise relevance propagation.PloS one, 10(7):e0130140, 2015

Reference 3

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Observation ba24b969-bb32-4336-bdbb-675bbee4daf8 · outbound

This paper cites Validity and reliability of dermoscopic criteria used to differentiate nevi from melanoma: a web-based in- ternational dermoscopy society study.

MedGrad E-CLIP: Enhancing Trust and Transparency in AI-Driven Skin Lesion Diagnosis Validity and reliability of dermoscopic criteria used to differentiate nevi from melanoma: a web-based in- ternational dermoscopy society study

Reference 4

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Observation b6110911-727a-4f31-961f-4e1c8ee820f7 · outbound

This paper cites Grad-cam++: General- ized gradient-based visual explanations for deep convolu- tional networks.

MedGrad E-CLIP: Enhancing Trust and Transparency in AI-Driven Skin Lesion Diagnosis Grad-cam++: General- ized gradient-based visual explanations for deep convolu- tional networks

Reference 5

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Observation 4f2c80c5-50ea-49f8-96f6-2fd86d3db774 · outbound

This paper cites Mammo-CLIP: Leveraging Contrastive Language-Image Pre-training (CLIP) for Enhanced Breast Cancer Diagnosis with Multi-view Mammography.

MedGrad E-CLIP: Enhancing Trust and Transparency in AI-Driven Skin Lesion Diagnosis Mammo-CLIP: Leveraging Contrastive Language-Image Pre-training (CLIP) for Enhanced Breast Cancer Diagnosis with Multi-view Mammography

Reference 6

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Observation d3dc2618-a99a-4163-9576-9f3a4b271cd9 · outbound

This paper cites an unresolved cited work.

MedGrad E-CLIP: Enhancing Trust and Transparency in AI-Driven Skin Lesion Diagnosis Unresolved cited work

Reference 7

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Source-reported events for the cited work

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Observation 05e54ca2-f733-41aa-bcf9-9110f4405e2d · outbound

This paper cites Dermatologist-level classification of skin cancer with deep neural networks.

MedGrad E-CLIP: Enhancing Trust and Transparency in AI-Driven Skin Lesion Diagnosis Dermatologist-level classification of skin cancer with deep neural networks

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-10T20:52:33.798408Z

Source-reported events for the cited work

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Observation a5ba672c-adc3-4c74-8009-75b30839a35f · outbound

This paper cites Skin disease recognition us- ing deep saliency features and multimodal learning of der- moscopy and clinical images.

MedGrad E-CLIP: Enhancing Trust and Transparency in AI-Driven Skin Lesion Diagnosis Skin disease recognition us- ing deep saliency features and multimodal learning of der- moscopy and clinical images

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:52:34.400117Z

Source-reported events for the cited work

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Observation fd414558-36fd-4818-9ec3-fba074cd09dc · outbound

This paper cites Skin lesion classification using ensembles of multi-resolution efficientnets with meta data.

MedGrad E-CLIP: Enhancing Trust and Transparency in AI-Driven Skin Lesion Diagnosis Skin lesion classification using ensembles of multi-resolution efficientnets with meta data

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:52:34.384889Z

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Observation 9f707453-09e5-4f71-b468-1eda9d92bf59 · outbound

This paper cites pathclip: Detection of genes and gene relations from biological pathway figures through image-text contrastive learning.

MedGrad E-CLIP: Enhancing Trust and Transparency in AI-Driven Skin Lesion Diagnosis pathclip: Detection of genes and gene relations from biological pathway figures through image-text contrastive learning

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:52:34.368873Z

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Observation c32000fc-be5b-4185-9d91-5f940d60ab9a · outbound

This paper cites Skin lesion classification using multitask multimodal neural nets.

MedGrad E-CLIP: Enhancing Trust and Transparency in AI-Driven Skin Lesion Diagnosis Skin lesion classification using multitask multimodal neural nets

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:52:34.352518Z

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Observation 082fe0a0-a632-45cf-9e3d-2a13ef0c71e2 · outbound

This paper cites Improving Medical Multi-modal Contrastive Learning with Expert Annotations.

MedGrad E-CLIP: Enhancing Trust and Transparency in AI-Driven Skin Lesion Diagnosis Improving Medical Multi-modal Contrastive Learning with Expert Annotations

Reference 13

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Observation 3cb2c6fb-6353-4674-bd4a-fcb8f1a3d1be · outbound

This paper cites Cli- path: Fine-tune clip with visual feature fusion for pathology image analysis towards minimizing data collection efforts.

MedGrad E-CLIP: Enhancing Trust and Transparency in AI-Driven Skin Lesion Diagnosis Cli- path: Fine-tune clip with visual feature fusion for pathology image analysis towards minimizing data collection efforts

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:52:34.336570Z

Source-reported events for the cited work

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Observation 6b498033-0e67-4b25-b0ca-e972cff8a8f6 · outbound

This paper cites Deep-lift: Deep label- specific feature learning for image annotation.

MedGrad E-CLIP: Enhancing Trust and Transparency in AI-Driven Skin Lesion Diagnosis Deep-lift: Deep label- specific feature learning for image annotation

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:52:34.321879Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 5786a417-dd91-4936-a5f5-3c38090aedec · outbound

This paper cites Skin lesion classification from dermo- scopic images using deep learning techniques.

MedGrad E-CLIP: Enhancing Trust and Transparency in AI-Driven Skin Lesion Diagnosis Skin lesion classification from dermo- scopic images using deep learning techniques

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:52:34.306674Z

Source-reported events for the cited work

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Observation 888f5ce2-a8ba-41e5-8bc0-907377f45a29 · outbound

This paper cites A unified approach to interpreting model predictions.

MedGrad E-CLIP: Enhancing Trust and Transparency in AI-Driven Skin Lesion Diagnosis A unified approach to interpreting model predictions

Reference 17

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Observation 29267384-32f9-4f91-afb6-85792fc0f25a · outbound

This paper cites Computer aided diagnostic support system for skin cancer: a review of tech- niques and algorithms.

MedGrad E-CLIP: Enhancing Trust and Transparency in AI-Driven Skin Lesion Diagnosis Computer aided diagnostic support system for skin cancer: a review of tech- niques and algorithms

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:52:34.273938Z

Source-reported events for the cited work

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Observation 52588e9b-16c7-442d-9f99-ffe217e95625 · outbound

This paper cites Ph 2-a dermoscopic image database for research and benchmarking.

MedGrad E-CLIP: Enhancing Trust and Transparency in AI-Driven Skin Lesion Diagnosis Ph 2-a dermoscopic image database for research and benchmarking

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:52:34.258578Z

Source-reported events for the cited work

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Observation fc4166ea-5666-47c4-ab00-ba54dcf41156 · outbound

This paper cites Explainable AI for prac- titioners.

MedGrad E-CLIP: Enhancing Trust and Transparency in AI-Driven Skin Lesion Diagnosis Explainable AI for prac- titioners

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:52:34.242987Z

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Observation 47336f7a-000c-45a6-82d2-5c58c23cf8ea · outbound

This paper cites The abcd rule of dermatoscopy: high prospective value in the diagnosis of doubtful melanocytic skin lesions.

MedGrad E-CLIP: Enhancing Trust and Transparency in AI-Driven Skin Lesion Diagnosis The abcd rule of dermatoscopy: high prospective value in the diagnosis of doubtful melanocytic skin lesions

Reference 21

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Observation 18a0b3a9-f7fa-49fe-96a9-1ccfeed3793c · outbound

This paper cites A deep learn- ing approach based on explainable artificial intelligence for skin lesion classification.

MedGrad E-CLIP: Enhancing Trust and Transparency in AI-Driven Skin Lesion Diagnosis A deep learn- ing approach based on explainable artificial intelligence for skin lesion classification

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:52:34.212181Z

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Observation 6d4a2983-6d2d-4879-b498-47f63b52f4d7 · outbound

This paper cites Learning transferable visual models from natural language supervi- sion.

MedGrad E-CLIP: Enhancing Trust and Transparency in AI-Driven Skin Lesion Diagnosis Learning transferable visual models from natural language supervi- sion

Reference 23

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Observation f1330c6c-b5e1-4be8-b154-351f1f66fa1a · outbound

This paper cites ” why should i trust you?” explaining the predictions of any classifier.

MedGrad E-CLIP: Enhancing Trust and Transparency in AI-Driven Skin Lesion Diagnosis ” why should i trust you?” explaining the predictions of any classifier

Reference 24

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Observation 053ec8cd-8da5-461e-98c5-fa8d16a52b95 · outbound

This paper cites Differentiation of atypical moles (dysplastic nevi) from early melanomas by dermoscopy.

MedGrad E-CLIP: Enhancing Trust and Transparency in AI-Driven Skin Lesion Diagnosis Differentiation of atypical moles (dysplastic nevi) from early melanomas by dermoscopy

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:52:34.170658Z

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Observation 8abd8730-e180-41c9-b1ce-47570b9a8497 · outbound

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

MedGrad E-CLIP: Enhancing Trust and Transparency in AI-Driven Skin Lesion Diagnosis Grad-cam: Visual explanations from deep networks via gradient-based localization

Reference 26

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

MedGrad E-CLIP: Enhancing Trust and Transparency in AI-Driven Skin Lesion Diagnosis Grad-CAM: Why did you say that?

Reference 27

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Observation bfce7ef9-1196-422d-979a-1ae936aceaf5 · outbound

This paper cites Miter: Medical image– text joint adaptive pretraining with multi-level contrastive learning.

MedGrad E-CLIP: Enhancing Trust and Transparency in AI-Driven Skin Lesion Diagnosis Miter: Medical image– text joint adaptive pretraining with multi-level contrastive learning

Reference 28

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Observation 7ee17c0d-a120-4c2e-a0ef-ea8ea96895fe · outbound

This paper cites Detection techniques for melanoma diagnosis: A perfor- mance evaluation.

MedGrad E-CLIP: Enhancing Trust and Transparency in AI-Driven Skin Lesion Diagnosis Detection techniques for melanoma diagnosis: A perfor- mance evaluation

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:52:34.130463Z

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Observation f5e173e7-4e5b-47b4-954d-15d4d7c07553 · outbound

This paper cites SmoothGrad: removing noise by adding noise.

MedGrad E-CLIP: Enhancing Trust and Transparency in AI-Driven Skin Lesion Diagnosis SmoothGrad: removing noise by adding noise

Reference 30

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Observation ab1a10c3-c26e-4c14-9416-f4216abf976f · outbound

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MedGrad E-CLIP: Enhancing Trust and Transparency in AI-Driven Skin Lesion Diagnosis Unresolved cited work

Reference 31

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Observation 7b4f821d-991a-4063-88eb-4657062ec1fc · outbound

This paper cites MedCLIP: Contrastive Learning from Unpaired Medical Images and Text.

MedGrad E-CLIP: Enhancing Trust and Transparency in AI-Driven Skin Lesion Diagnosis MedCLIP: Contrastive Learning from Unpaired Medical Images and Text

Reference 32

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Observation 237f82f2-9814-4406-b60b-ad6d2b9ad992 · outbound

This paper cites EventCLIP: Adapting CLIP for Event-based Object Recognition.

MedGrad E-CLIP: Enhancing Trust and Transparency in AI-Driven Skin Lesion Diagnosis EventCLIP: Adapting CLIP for Event-based Object Recognition

Reference 33

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Source-reported events for the cited work

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Observation be9e065a-850c-4091-a974-805beccb67ec · outbound

This paper cites Attention residual learning for skin lesion classification.

MedGrad E-CLIP: Enhancing Trust and Transparency in AI-Driven Skin Lesion Diagnosis Attention residual learning for skin lesion classification

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:52:34.098397Z

Source-reported events for the cited work

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Observation ddf85834-8fd6-42af-a79e-dee1c6772b6d · outbound

This paper cites Contrastive learning of medical visual representations from paired images and text.

MedGrad E-CLIP: Enhancing Trust and Transparency in AI-Driven Skin Lesion Diagnosis Contrastive learning of medical visual representations from paired images and text

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-10T20:52:33.926786Z

Source-reported events for the cited work

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Observation b769037d-2371-4331-a37a-03c505d7363c · outbound

This paper cites Gradient-based visual explanation for transformer-based clip.

MedGrad E-CLIP: Enhancing Trust and Transparency in AI-Driven Skin Lesion Diagnosis Gradient-based visual explanation for transformer-based clip

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:52:34.071799Z

Source-reported events for the cited work

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Pith citing papers

No inbound Pith citation observations are available.