D-Cube: Exploiting Hyper-Features of Diffusion Model for Robust Medical Classification | Paper Citation Record

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

D-Cube: Exploiting Hyper-Features of Diffusion Model for Robust Medical Classification U-net: Convolutional networks for biomedical image segmentation,

Reference 1

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Observation b3b3187b-e5c2-4385-9f93-fcb70d2562a4 · outbound

This paper cites Synthetic CT generation from CBCT images via deep learning,.

D-Cube: Exploiting Hyper-Features of Diffusion Model for Robust Medical Classification Synthetic CT generation from CBCT images via deep learning,

Reference 2

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This paper cites Denoising diffusion probabilistic models,.

D-Cube: Exploiting Hyper-Features of Diffusion Model for Robust Medical Classification Denoising diffusion probabilistic models,

Reference 3

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This paper cites Applications of artificial intelligence in pancreatic and biliary diseases,.

D-Cube: Exploiting Hyper-Features of Diffusion Model for Robust Medical Classification Applications of artificial intelligence in pancreatic and biliary diseases,

Reference 4

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This paper cites Pancreatic cancer detection on CT scans with deep learning: a nationwide population-based study,.

D-Cube: Exploiting Hyper-Features of Diffusion Model for Robust Medical Classification Pancreatic cancer detection on CT scans with deep learning: a nationwide population-based study,

Reference 5

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This paper cites Unsupervised Visual Representation Learning Based on Segmentation of Geometric Pseudo- Shapes for Transformer-Based Medical Tasks,.

D-Cube: Exploiting Hyper-Features of Diffusion Model for Robust Medical Classification Unsupervised Visual Representation Learning Based on Segmentation of Geometric Pseudo- Shapes for Transformer-Based Medical Tasks,

Reference 6

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This paper cites "A robust and efficient AI assistant for breast tumor segmentation from DCE-MRI via a spatial.

D-Cube: Exploiting Hyper-Features of Diffusion Model for Robust Medical Classification "A robust and efficient AI assistant for breast tumor segmentation from DCE-MRI via a spatial

Reference 7

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This paper cites Synthetic Data from Diffusion Models Improves ImageNet Classification.

D-Cube: Exploiting Hyper-Features of Diffusion Model for Robust Medical Classification Synthetic Data from Diffusion Models Improves ImageNet Classification

Reference 8

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This paper cites All are worth words: A vit backbone for diffusion models,.

D-Cube: Exploiting Hyper-Features of Diffusion Model for Robust Medical Classification All are worth words: A vit backbone for diffusion models,

Reference 9

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This paper cites Zero-shot text-to-image generation,.

D-Cube: Exploiting Hyper-Features of Diffusion Model for Robust Medical Classification Zero-shot text-to-image generation,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:00:17.209663Z

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This paper cites Stablevideo: Text-driven consistency-aware diffusion video editing,.

D-Cube: Exploiting Hyper-Features of Diffusion Model for Robust Medical Classification Stablevideo: Text-driven consistency-aware diffusion video editing,

Reference 11

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This paper cites Deep residual learning for image recognition,.

D-Cube: Exploiting Hyper-Features of Diffusion Model for Robust Medical Classification Deep residual learning for image recognition,

Reference 12

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This paper cites Aggregated residual transformations for deep neural networks,.

D-Cube: Exploiting Hyper-Features of Diffusion Model for Robust Medical Classification Aggregated residual transformations for deep neural networks,

Reference 13

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This paper cites ResNeSt: Split-Attention Networks.

D-Cube: Exploiting Hyper-Features of Diffusion Model for Robust Medical Classification ResNeSt: Split-Attention Networks

Reference 14

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This paper cites Shufflenet v2: Practical guidelines for efficient cnn architecture design,.

D-Cube: Exploiting Hyper-Features of Diffusion Model for Robust Medical Classification Shufflenet v2: Practical guidelines for efficient cnn architecture design,

Reference 15

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This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale,.

D-Cube: Exploiting Hyper-Features of Diffusion Model for Robust Medical Classification An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale,

Reference 16

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This paper cites Tokens-to-token vit: Training vision transformers from scratch on imagenet,.

D-Cube: Exploiting Hyper-Features of Diffusion Model for Robust Medical Classification Tokens-to-token vit: Training vision transformers from scratch on imagenet,

Reference 17

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This paper cites Cvt: Introducing convolutions to vision transformers,.

D-Cube: Exploiting Hyper-Features of Diffusion Model for Robust Medical Classification Cvt: Introducing convolutions to vision transformers,

Reference 18

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This paper cites Rethinking spatial dimensions of vision transformers,.

D-Cube: Exploiting Hyper-Features of Diffusion Model for Robust Medical Classification Rethinking spatial dimensions of vision transformers,

Reference 19

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This paper cites Pvt v2: Improved baselines with pyramid vision transformer,.

D-Cube: Exploiting Hyper-Features of Diffusion Model for Robust Medical Classification Pvt v2: Improved baselines with pyramid vision transformer,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:00:17.024436Z

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This paper cites Pyramid vision transformer: A versatile backbone for dense prediction without convolutions,.

D-Cube: Exploiting Hyper-Features of Diffusion Model for Robust Medical Classification Pyramid vision transformer: A versatile backbone for dense prediction without convolutions,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:00:17.008726Z

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This paper cites On Aliased Resizing and Surprising Subtleties in GAN Evaluation,.

D-Cube: Exploiting Hyper-Features of Diffusion Model for Robust Medical Classification On Aliased Resizing and Surprising Subtleties in GAN Evaluation,

Reference 22

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This paper cites SegFormer: Simple and efficient design for semantic segmentation with transformers,.

D-Cube: Exploiting Hyper-Features of Diffusion Model for Robust Medical Classification SegFormer: Simple and efficient design for semantic segmentation with transformers,

Reference 23

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This paper cites An overview of deep learning in medical imaging focusing on MRI,.

D-Cube: Exploiting Hyper-Features of Diffusion Model for Robust Medical Classification An overview of deep learning in medical imaging focusing on MRI,

Reference 24

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This paper cites U-GAT-IT: Unsupervised Gen- erative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation,.

D-Cube: Exploiting Hyper-Features of Diffusion Model for Robust Medical Classification U-GAT-IT: Unsupervised Gen- erative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation,

Reference 25

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This paper cites Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI,.

D-Cube: Exploiting Hyper-Features of Diffusion Model for Robust Medical Classification Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI,

Reference 26

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This paper cites Convolutional neural network of multiparametric MRI accurately detects axillary lymph node metastasis in breast cancer patients with pre neoadjuvant chemotherapy,.

D-Cube: Exploiting Hyper-Features of Diffusion Model for Robust Medical Classification Convolutional neural network of multiparametric MRI accurately detects axillary lymph node metastasis in breast cancer patients with pre neoadjuvant chemotherapy,

Reference 27

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This paper cites Deep learning for identifying radiogenomic associations in breast cancer,.

D-Cube: Exploiting Hyper-Features of Diffusion Model for Robust Medical Classification Deep learning for identifying radiogenomic associations in breast cancer,

Reference 28

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This paper cites I., Schönlieb, C.

D-Cube: Exploiting Hyper-Features of Diffusion Model for Robust Medical Classification I., Schönlieb, C

Reference 29

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This paper cites Can AI help in screening Viral and COVID-19 pneumonia?.

D-Cube: Exploiting Hyper-Features of Diffusion Model for Robust Medical Classification Can AI help in screening Viral and COVID-19 pneumonia?

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:00:16.864253Z

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This paper cites Exploring the Effect of Image Enhancement Techniques on COVID-19 Detection using Chest X-rays Images.

D-Cube: Exploiting Hyper-Features of Diffusion Model for Robust Medical Classification Exploring the Effect of Image Enhancement Techniques on COVID-19 Detection using Chest X-rays Images

Reference 31

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This paper cites and Mazurowski, M.A., 2018.

D-Cube: Exploiting Hyper-Features of Diffusion Model for Robust Medical Classification and Mazurowski, M.A., 2018

Reference 32

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This paper cites C., Pareek, A., Jensen, M., Lungren, M.

D-Cube: Exploiting Hyper-Features of Diffusion Model for Robust Medical Classification C., Pareek, A., Jensen, M., Lungren, M

Reference 33

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This paper cites Diffusion-based Radiotherapy Dose Prediction Guided by Inter-slice Aware Structure Encoding.

D-Cube: Exploiting Hyper-Features of Diffusion Model for Robust Medical Classification Diffusion-based Radiotherapy Dose Prediction Guided by Inter-slice Aware Structure Encoding

Reference 34

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D-Cube: Exploiting Hyper-Features of Diffusion Model for Robust Medical Classification Diffusion deep learning for brain age prediction and longitudinal tracking in children through adulthood

Reference 35

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This paper cites SP-DiffDose: A Conditional Diffusion Model for Radiation Dose Prediction Based on Multi-Scale Fusion of Anatomical Structures, Guided by SwinTransformer and Projector.

D-Cube: Exploiting Hyper-Features of Diffusion Model for Robust Medical Classification SP-DiffDose: A Conditional Diffusion Model for Radiation Dose Prediction Based on Multi-Scale Fusion of Anatomical Structures, Guided by SwinTransformer and Projector

Reference 36

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This paper cites (2023, October).

D-Cube: Exploiting Hyper-Features of Diffusion Model for Robust Medical Classification (2023, October)

Reference 37

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D-Cube: Exploiting Hyper-Features of Diffusion Model for Robust Medical Classification Unresolved cited work

Reference 38

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Reference 39

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This paper cites A., Tselykh, A., Muthanna, M.

D-Cube: Exploiting Hyper-Features of Diffusion Model for Robust Medical Classification A., Tselykh, A., Muthanna, M

Reference 40

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This paper cites Data variation-aware medical image segmen- tation.

D-Cube: Exploiting Hyper-Features of Diffusion Model for Robust Medical Classification Data variation-aware medical image segmen- tation

Reference 41

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This paper cites Medsegdiff: Medical image segmentation with diffusion probabilistic model.

D-Cube: Exploiting Hyper-Features of Diffusion Model for Robust Medical Classification Medsegdiff: Medical image segmentation with diffusion probabilistic model

Reference 42

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This paper cites Diffusiondet: Diffusion model for object detection.

D-Cube: Exploiting Hyper-Features of Diffusion Model for Robust Medical Classification Diffusiondet: Diffusion model for object detection

Reference 43

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This paper cites DFormer: Diffusion-guided Transformer for Universal Image Segmentation.

D-Cube: Exploiting Hyper-Features of Diffusion Model for Robust Medical Classification DFormer: Diffusion-guided Transformer for Universal Image Segmentation

Reference 44

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This paper cites Your diffusion model is secretly a zero-shot classifier.

D-Cube: Exploiting Hyper-Features of Diffusion Model for Robust Medical Classification Your diffusion model is secretly a zero-shot classifier

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:00:16.681192Z

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Observation bee208be-d7b1-499e-ba3d-c149290fd090 · outbound

This paper cites Diffusion hyperfeatures: Searching through time and space for semantic correspondence.

D-Cube: Exploiting Hyper-Features of Diffusion Model for Robust Medical Classification Diffusion hyperfeatures: Searching through time and space for semantic correspondence

Reference 46

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This paper cites Label-Efficient Semantic Segmentation with Diffusion Models.

D-Cube: Exploiting Hyper-Features of Diffusion Model for Robust Medical Classification Label-Efficient Semantic Segmentation with Diffusion Models

Reference 47

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This paper cites Freedom: Training-free energy-guided conditional diffusion model.

D-Cube: Exploiting Hyper-Features of Diffusion Model for Robust Medical Classification Freedom: Training-free energy-guided conditional diffusion model

Reference 48

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This paper cites Learning transferable visual models from natural language supervision.

D-Cube: Exploiting Hyper-Features of Diffusion Model for Robust Medical Classification Learning transferable visual models from natural language supervision

Reference 49

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D-Cube: Exploiting Hyper-Features of Diffusion Model for Robust Medical Classification Unresolved cited work

Reference 50

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This paper cites J., Li, K., & Fei-Fei, L.

D-Cube: Exploiting Hyper-Features of Diffusion Model for Robust Medical Classification J., Li, K., & Fei-Fei, L

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:00:16.587093Z

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Observation 1e2d7390-831f-40a1-a33d-2091a0f01ffd · outbound

This paper cites How Do Vision Transformers Work?.

D-Cube: Exploiting Hyper-Features of Diffusion Model for Robust Medical Classification How Do Vision Transformers Work?

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-12T19:00:16.123817Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 050fc6cf-9b3c-4d16-a1e9-384227962939 · outbound

This paper cites AM- RADIO: Agglomerative Visual Foundation Model – Reduce All Domains Into One.

D-Cube: Exploiting Hyper-Features of Diffusion Model for Robust Medical Classification AM- RADIO: Agglomerative Visual Foundation Model – Reduce All Domains Into One

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:00:16.570577Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 1977cbec-aab5-44a4-acdb-f287e6091b59 · outbound

This paper cites an unresolved cited work.

D-Cube: Exploiting Hyper-Features of Diffusion Model for Robust Medical Classification Unresolved cited work

Reference 54

Resolution: unresolved; raw_fallback, observed 2026-08-12T19:00:16.554650Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 5cc679a4-a321-4edc-af08-82e3ebff71aa · outbound

This paper cites Does robustness on imagenet transfer to downstream tasks?.

D-Cube: Exploiting Hyper-Features of Diffusion Model for Robust Medical Classification Does robustness on imagenet transfer to downstream tasks?

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:00:16.538629Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 9b1ab93f-6a8b-4480-8c27-f2163fed1697 · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

D-Cube: Exploiting Hyper-Features of Diffusion Model for Robust Medical Classification LoRA: Low-Rank Adaptation of Large Language Models

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:00:16.521345Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 27285d44-3138-4d2c-8f04-3c940fb7a351 · outbound

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

D-Cube: Exploiting Hyper-Features of Diffusion Model for Robust Medical Classification MedCLIP: Contrastive Learning from Unpaired Medical Images and Text

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-12T19:00:16.148200Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b0a529e1-b84d-4529-93c5-cec4277ead59 · outbound

This paper cites K., Heidari, M., Azad, R., Fayyaz, M., Hacihaliloglu, I., & Merhof, D.

D-Cube: Exploiting Hyper-Features of Diffusion Model for Robust Medical Classification K., Heidari, M., Azad, R., Fayyaz, M., Hacihaliloglu, I., & Merhof, D

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:00:16.503151Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 025b6a47-f838-42ba-92bd-f7deba479d9d · outbound

This paper cites T., Parekh, Z., Pham, H.,.

D-Cube: Exploiting Hyper-Features of Diffusion Model for Robust Medical Classification T., Parekh, Z., Pham, H.,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:00:16.485291Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 1e79de50-a2fb-4225-a6d8-55fbd7138d1a · outbound

This paper cites Sora: Creating video from text.

D-Cube: Exploiting Hyper-Features of Diffusion Model for Robust Medical Classification Sora: Creating video from text

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:00:16.466732Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 79b0845e-d13d-4a53-b0ac-d4545ce97ba1 · outbound

This paper cites E., Setio, A.

D-Cube: Exploiting Hyper-Features of Diffusion Model for Robust Medical Classification E., Setio, A

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:00:16.448844Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f0b3f2d5-43b8-4a56-bd35-df95493b1fc8 · outbound

This paper cites C., Roth, H.

D-Cube: Exploiting Hyper-Features of Diffusion Model for Robust Medical Classification C., Roth, H

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:00:16.430782Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 78863386-521f-496f-8c20-d29430a4fbca · outbound

This paper cites an unresolved cited work.

D-Cube: Exploiting Hyper-Features of Diffusion Model for Robust Medical Classification Unresolved cited work

Reference 63

Resolution: unresolved; raw_fallback, observed 2026-08-12T19:00:16.413738Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c6c5ee52-02d6-44d6-b5fa-9c6a3ee4e65c · outbound

This paper cites an unresolved cited work.

D-Cube: Exploiting Hyper-Features of Diffusion Model for Robust Medical Classification Unresolved cited work

Reference 64

Resolution: unresolved; raw_fallback, observed 2026-08-12T19:00:16.396996Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers