Advancing Generalizable Tumor Segmentation with Anomaly-Aware Open-Vocabulary Attention Maps and Frozen Foundation Diffusion Models | Paper Citation Record

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This paper cites The brain tumor segmentation (brats) chal- lenge 2023: glioma segmentation in sub-saharan africa pa- tient population (brats-africa).

Advancing Generalizable Tumor Segmentation with Anomaly-Aware Open-Vocabulary Attention Maps and Frozen Foundation Diffusion Models The brain tumor segmentation (brats) chal- lenge 2023: glioma segmentation in sub-saharan africa pa- tient population (brats-africa)

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This paper cites Test-time adaptation with salip: A cascade of sam and clip for zero-shot medical image seg- mentation.

Advancing Generalizable Tumor Segmentation with Anomaly-Aware Open-Vocabulary Attention Maps and Frozen Foundation Diffusion Models Test-time adaptation with salip: A cascade of sam and clip for zero-shot medical image seg- mentation

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This paper cites The medical segmentation decathlon.Nature communications, 13(1):4128, 2022.

Advancing Generalizable Tumor Segmentation with Anomaly-Aware Open-Vocabulary Attention Maps and Frozen Foundation Diffusion Models The medical segmentation decathlon.Nature communications, 13(1):4128, 2022

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This paper cites Guided Reconstruction with Conditioned Diffusion Models for Unsupervised Anomaly Detection in Brain MRIs.

Advancing Generalizable Tumor Segmentation with Anomaly-Aware Open-Vocabulary Attention Maps and Frozen Foundation Diffusion Models Guided Reconstruction with Conditioned Diffusion Models for Unsupervised Anomaly Detection in Brain MRIs

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Advancing Generalizable Tumor Segmentation with Anomaly-Aware Open-Vocabulary Attention Maps and Frozen Foundation Diffusion Models Diffusion models with implicit guidance for medical anomaly detection

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This paper cites Cancerunit: Towards a single unified model for ef- fective detection, segmentation, and diagnosis of eight major cancers using a large collection of ct scans.

Advancing Generalizable Tumor Segmentation with Anomaly-Aware Open-Vocabulary Attention Maps and Frozen Foundation Diffusion Models Cancerunit: Towards a single unified model for ef- fective detection, segmentation, and diagnosis of eight major cancers using a large collection of ct scans

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This paper cites Towards generaliz- able tumor synthesis.

Advancing Generalizable Tumor Segmentation with Anomaly-Aware Open-Vocabulary Attention Maps and Frozen Foundation Diffusion Models Towards generaliz- able tumor synthesis

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This paper cites Berdiff: Conditional bernoulli diffusion model for medical image segmentation.

Advancing Generalizable Tumor Segmentation with Anomaly-Aware Open-Vocabulary Attention Maps and Frozen Foundation Diffusion Models Berdiff: Conditional bernoulli diffusion model for medical image segmentation

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Advancing Generalizable Tumor Segmentation with Anomaly-Aware Open-Vocabulary Attention Maps and Frozen Foundation Diffusion Models Masked-attention mask transformer for universal image segmentation

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This paper cites Unleashing the potential of sam for medical adaptation via hierarchi- cal decoding.

Advancing Generalizable Tumor Segmentation with Anomaly-Aware Open-Vocabulary Attention Maps and Frozen Foundation Diffusion Models Unleashing the potential of sam for medical adaptation via hierarchi- cal decoding

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Advancing Generalizable Tumor Segmentation with Anomaly-Aware Open-Vocabulary Attention Maps and Frozen Foundation Diffusion Models Diffusion trans- former u-net for medical image segmentation

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Advancing Generalizable Tumor Segmentation with Anomaly-Aware Open-Vocabulary Attention Maps and Frozen Foundation Diffusion Models Latentpaint: Image inpainting in latent space with diffusion models

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This paper cites De- coupling zero-shot semantic segmentation.

Advancing Generalizable Tumor Segmentation with Anomaly-Aware Open-Vocabulary Attention Maps and Frozen Foundation Diffusion Models De- coupling zero-shot semantic segmentation

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This paper cites Training like a medical resident: Context-prior learning toward universal medical image segmentation.

Advancing Generalizable Tumor Segmentation with Anomaly-Aware Open-Vocabulary Attention Maps and Frozen Foundation Diffusion Models Training like a medical resident: Context-prior learning toward universal medical image segmentation

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This paper cites Scal- ing open-vocabulary image segmentation with image-level labels.

Advancing Generalizable Tumor Segmentation with Anomaly-Aware Open-Vocabulary Attention Maps and Frozen Foundation Diffusion Models Scal- ing open-vocabulary image segmentation with image-level labels

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This paper cites Maisi: Medical ai for synthetic imaging.

Advancing Generalizable Tumor Segmentation with Anomaly-Aware Open-Vocabulary Attention Maps and Frozen Foundation Diffusion Models Maisi: Medical ai for synthetic imaging

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This paper cites Accelerating dif- fusion models via pre-segmentation diffusion sampling for medical image segmentation.

Advancing Generalizable Tumor Segmentation with Anomaly-Aware Open-Vocabulary Attention Maps and Frozen Foundation Diffusion Models Accelerating dif- fusion models via pre-segmentation diffusion sampling for medical image segmentation

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This paper cites The KiTS21 Challenge: Automatic segmentation of kidneys, renal tumors, and renal cysts in corticomedullary-phase CT.

Advancing Generalizable Tumor Segmentation with Anomaly-Aware Open-Vocabulary Attention Maps and Frozen Foundation Diffusion Models The KiTS21 Challenge: Automatic segmentation of kidneys, renal tumors, and renal cysts in corticomedullary-phase CT

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Advancing Generalizable Tumor Segmentation with Anomaly-Aware Open-Vocabulary Attention Maps and Frozen Foundation Diffusion Models Label-free liver tumor segmentation

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This paper cites Adapting visual-language models for generalizable anomaly detection in medical im- ages.

Advancing Generalizable Tumor Segmentation with Anomaly-Aware Open-Vocabulary Attention Maps and Frozen Foundation Diffusion Models Adapting visual-language models for generalizable anomaly detection in medical im- ages

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Advancing Generalizable Tumor Segmentation with Anomaly-Aware Open-Vocabulary Attention Maps and Frozen Foundation Diffusion Models nnu-net: a self-configuring 9 method for deep learning-based biomedical image segmen- tation

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Advancing Generalizable Tumor Segmentation with Anomaly-Aware Open-Vocabulary Attention Maps and Frozen Foundation Diffusion Models Zept: Zero-shot pan-tumor segmentation via query-disentangling and self-prompting

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Advancing Generalizable Tumor Segmentation with Anomaly-Aware Open-Vocabulary Attention Maps and Frozen Foundation Diffusion Models Unleashing the Potential of Vision-Language Pre-Training for 3D Zero-Shot Lesion Segmentation via Mask-Attribute Alignment

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Advancing Generalizable Tumor Segmentation with Anomaly-Aware Open-Vocabulary Attention Maps and Frozen Foundation Diffusion Models Diffusion Adversarial Representation Learning for Self-supervised Vessel Segmentation

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This paper cites Segment any- thing.

Advancing Generalizable Tumor Segmentation with Anomaly-Aware Open-Vocabulary Attention Maps and Frozen Foundation Diffusion Models Segment any- thing

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Advancing Generalizable Tumor Segmentation with Anomaly-Aware Open-Vocabulary Attention Maps and Frozen Foundation Diffusion Models Self-supervised diffusion model for anomaly segmentation in medical imaging

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Advancing Generalizable Tumor Segmentation with Anomaly-Aware Open-Vocabulary Attention Maps and Frozen Foundation Diffusion Models Open-vocabulary semantic segmentation with mask-adapted clip

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Advancing Generalizable Tumor Segmentation with Anomaly-Aware Open-Vocabulary Attention Maps and Frozen Foundation Diffusion Models Clip-driven universal model for organ segmentation and tumor detection

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Advancing Generalizable Tumor Segmentation with Anomaly-Aware Open-Vocabulary Attention Maps and Frozen Foundation Diffusion Models Decoupled Weight Decay Regularization

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Advancing Generalizable Tumor Segmentation with Anomaly-Aware Open-Vocabulary Attention Maps and Frozen Foundation Diffusion Models Repaint: Inpainting using denoising diffusion probabilistic models

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Advancing Generalizable Tumor Segmentation with Anomaly-Aware Open-Vocabulary Attention Maps and Frozen Foundation Diffusion Models V-net: Fully convolutional neural networks for volumetric medical image segmentation

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Advancing Generalizable Tumor Segmentation with Anomaly-Aware Open-Vocabulary Attention Maps and Frozen Foundation Diffusion Models A threshold selection method from gray-level histograms

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Advancing Generalizable Tumor Segmentation with Anomaly-Aware Open-Vocabulary Attention Maps and Frozen Foundation Diffusion Models Freeseg: Unified, universal and open-vocabulary image segmentation

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Advancing Generalizable Tumor Segmentation with Anomaly-Aware Open-Vocabulary Attention Maps and Frozen Foundation Diffusion Models Learning transferable visual models from natural language supervi- sion

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Advancing Generalizable Tumor Segmentation with Anomaly-Aware Open-Vocabulary Attention Maps and Frozen Foundation Diffusion Models Ambiguous medical image segmentation using diffusion models

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Advancing Generalizable Tumor Segmentation with Anomaly-Aware Open-Vocabulary Attention Maps and Frozen Foundation Diffusion Models SAM 2: Segment Anything in Images and Videos

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Advancing Generalizable Tumor Segmentation with Anomaly-Aware Open-Vocabulary Attention Maps and Frozen Foundation Diffusion Models Zero-shot medical image segmentation based on sparse prompt using finetuned sam

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Advancing Generalizable Tumor Segmentation with Anomaly-Aware Open-Vocabulary Attention Maps and Frozen Foundation Diffusion Models Learning fixed points in gener- ative adversarial networks: From image-to-image transla- tion to disease detection and localization

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Advancing Generalizable Tumor Segmentation with Anomaly-Aware Open-Vocabulary Attention Maps and Frozen Foundation Diffusion Models Unresolved cited work

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Advancing Generalizable Tumor Segmentation with Anomaly-Aware Open-Vocabulary Attention Maps and Frozen Foundation Diffusion Models To- talsegmentator: robust segmentation of 104 anatomic struc- tures in ct images

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Advancing Generalizable Tumor Segmentation with Anomaly-Aware Open-Vocabulary Attention Maps and Frozen Foundation Diffusion Models Diffusion models for medical anomaly detection

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Advancing Generalizable Tumor Segmentation with Anomaly-Aware Open-Vocabulary Attention Maps and Frozen Foundation Diffusion Models Diffusion models for implicit image segmentation ensembles

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Advancing Generalizable Tumor Segmentation with Anomaly-Aware Open-Vocabulary Attention Maps and Frozen Foundation Diffusion Models Medsegdiff: Medical image segmentation with diffusion probabilistic model

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Advancing Generalizable Tumor Segmentation with Anomaly-Aware Open-Vocabulary Attention Maps and Frozen Foundation Diffusion Models Medsegdiff-v2: Diffusion-based medical im- age segmentation with transformer

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This paper cites FreeTumor: Advance Tumor Segmentation via Large-Scale Tumor Synthesis.

Advancing Generalizable Tumor Segmentation with Anomaly-Aware Open-Vocabulary Attention Maps and Frozen Foundation Diffusion Models FreeTumor: Advance Tumor Segmentation via Large-Scale Tumor Synthesis

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Advancing Generalizable Tumor Segmentation with Anomaly-Aware Open-Vocabulary Attention Maps and Frozen Foundation Diffusion Models Anoddpm: Anomaly detection with de- noising diffusion probabilistic models using simplex noise

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Advancing Generalizable Tumor Segmentation with Anomaly-Aware Open-Vocabulary Attention Maps and Frozen Foundation Diffusion Models Zero-shot 3D Segmentation of Abdominal Organs in CT Scans Using Segment Anything Model 2: Adapting Video Tracking Capabilities for 3D Medical Imaging

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Advancing Generalizable Tumor Segmentation with Anomaly-Aware Open-Vocabulary Attention Maps and Frozen Foundation Diffusion Models Clinical-bert: Vision-language pre-training for radiograph diagnosis and reports generation

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Observation 26117795-b687-4c8e-965e-633f47af6e26 · outbound

This paper cites Uniseg: A prompt-driven universal segmenta- tion model as well as a strong representation learner.

Advancing Generalizable Tumor Segmentation with Anomaly-Aware Open-Vocabulary Attention Maps and Frozen Foundation Diffusion Models Uniseg: A prompt-driven universal segmenta- tion model as well as a strong representation learner

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-16T00:48:19.570005Z

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Observation da084fac-5324-46b5-a3c8-f86abf8e3deb · outbound

This paper cites Adding conditional control to text-to-image diffusion models.

Advancing Generalizable Tumor Segmentation with Anomaly-Aware Open-Vocabulary Attention Maps and Frozen Foundation Diffusion Models Adding conditional control to text-to-image diffusion models

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-16T00:48:19.614671Z

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Observation 4825b19a-5a20-4060-8038-7fce355164f0 · outbound

This paper cites Large-Vocabulary Segmentation for Medical Images with Text Prompts.

Advancing Generalizable Tumor Segmentation with Anomaly-Aware Open-Vocabulary Attention Maps and Frozen Foundation Diffusion Models Large-Vocabulary Segmentation for Medical Images with Text Prompts

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-16T00:48:19.660354Z

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Observation ba6d4746-b56c-4494-a21b-9805706217f1 · outbound

This paper cites #~𝑞Masklnput 𝑥! 𝑥!.

Advancing Generalizable Tumor Segmentation with Anomaly-Aware Open-Vocabulary Attention Maps and Frozen Foundation Diffusion Models #~𝑞Masklnput 𝑥! 𝑥!

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:48:19.959987Z

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