MIS-HCC: Hierarchical Channel Clustering for Efficient Medical Image Segmentation | Paper Citation Record

Observation 605842cf-1ae2-46dc-9227-272b05f01542 · outbound

This paper cites Knowledge regularized negative feature tuning of vision-language models for out- of-distribution detection,.

MIS-HCC: Hierarchical Channel Clustering for Efficient Medical Image Segmentation Knowledge regularized negative feature tuning of vision-language models for out- of-distribution detection,

Reference 1

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Observation 548c4914-bbe5-4feb-842a-dbb6feb6fe24 · outbound

This paper cites Lifelong scene graph generation,.

MIS-HCC: Hierarchical Channel Clustering for Efficient Medical Image Segmentation Lifelong scene graph generation,

Reference 2

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Observation 89a7d9bc-04f7-4527-8cbd-c92ecc9e102f · outbound

This paper cites Boosting learning efficiency in few-shot tasks with layer-adaptive pid control,.

MIS-HCC: Hierarchical Channel Clustering for Efficient Medical Image Segmentation Boosting learning efficiency in few-shot tasks with layer-adaptive pid control,

Reference 3

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Observation 3dd6cfa7-1cfe-41ed-b473-05ee5281685d · outbound

This paper cites Adaptive event stream slicing for open-vocabulary event-based object detection via vision-language knowledge distillation,.

MIS-HCC: Hierarchical Channel Clustering for Efficient Medical Image Segmentation Adaptive event stream slicing for open-vocabulary event-based object detection via vision-language knowledge distillation,

Reference 4

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Observation 7a578cea-de49-4b78-b5d1-8640f45073fd · outbound

This paper cites Em- bodiment: Self-supervised depth estimation based on camera models,.

MIS-HCC: Hierarchical Channel Clustering for Efficient Medical Image Segmentation Em- bodiment: Self-supervised depth estimation based on camera models,

Reference 5

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Observation 0566ca82-b653-4ac3-8372-55c59193e3b5 · outbound

This paper cites Vision-language embodiment for monocular depth estimation,.

MIS-HCC: Hierarchical Channel Clustering for Efficient Medical Image Segmentation Vision-language embodiment for monocular depth estimation,

Reference 6

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Observation 58cf7046-a042-4dc0-83f8-f5415bd9f07f · outbound

This paper cites Depth estimation based on 3d gaussian splatting siamese defocus,.

MIS-HCC: Hierarchical Channel Clustering for Efficient Medical Image Segmentation Depth estimation based on 3d gaussian splatting siamese defocus,

Reference 7

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Observation a0940a31-0ca9-4015-86c6-b46fd708a53a · outbound

This paper cites Decoding with structured awareness: integrating directional, frequency-spatial, and structural attention for medical image segmentation,.

MIS-HCC: Hierarchical Channel Clustering for Efficient Medical Image Segmentation Decoding with structured awareness: integrating directional, frequency-spatial, and structural attention for medical image segmentation,

Reference 8

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Observation 82fdb0ff-9bee-465a-9b20-210019d0b835 · outbound

This paper cites Computing nodes for plane data points by constructing cubic polynomial with constraints,.

MIS-HCC: Hierarchical Channel Clustering for Efficient Medical Image Segmentation Computing nodes for plane data points by constructing cubic polynomial with constraints,

Reference 9

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Observation 75073a35-aac0-43c6-ba70-bbf9ce3602c8 · outbound

This paper cites Reversible primitive–composition alignment for continual vision–language learn- ing,.

MIS-HCC: Hierarchical Channel Clustering for Efficient Medical Image Segmentation Reversible primitive–composition alignment for continual vision–language learn- ing,

Reference 10

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Observation bf477fd9-4d36-474c-9b4b-5585ee1224ff · outbound

This paper cites Boosting deep detector efficiency and robustness through detection discriminant reorganization and compression,.

MIS-HCC: Hierarchical Channel Clustering for Efficient Medical Image Segmentation Boosting deep detector efficiency and robustness through detection discriminant reorganization and compression,

Reference 11

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Observation efaaee52-b7ac-49fd-9286-d8d1d240da04 · outbound

This paper cites Visual detector compression via location-aware discriminant analysis,.

MIS-HCC: Hierarchical Channel Clustering for Efficient Medical Image Segmentation Visual detector compression via location-aware discriminant analysis,

Reference 12

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Observation dbd54625-b54d-4415-ae66-1a3c417d0845 · outbound

This paper cites Map: Parameter-efficient tuning for referring expression comprehen- sion via multi-modal adaptive positional encoding,.

MIS-HCC: Hierarchical Channel Clustering for Efficient Medical Image Segmentation Map: Parameter-efficient tuning for referring expression comprehen- sion via multi-modal adaptive positional encoding,

Reference 13

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Observation 2a9086d2-0a88-43be-b897-d3a484592672 · outbound

This paper cites Frequency-aligned knowledge distillation for lightweight spatiotem- poral forecasting,.

MIS-HCC: Hierarchical Channel Clustering for Efficient Medical Image Segmentation Frequency-aligned knowledge distillation for lightweight spatiotem- poral forecasting,

Reference 14

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Observation fc8dc75f-8f56-4c56-ab6f-113b2c4e63c6 · outbound

This paper cites A comprehensive survey of interaction techniques in 3d scene generation,.

MIS-HCC: Hierarchical Channel Clustering for Efficient Medical Image Segmentation A comprehensive survey of interaction techniques in 3d scene generation,

Reference 15

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Observation ff62e06e-2bea-446b-a911-41df72dfcb5d · outbound

This paper cites Sepprune: Structured pruning for efficient deep speech separation,.

MIS-HCC: Hierarchical Channel Clustering for Efficient Medical Image Segmentation Sepprune: Structured pruning for efficient deep speech separation,

Reference 16

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Observation 27e691df-367d-4ffa-be69-b7312c5a92a2 · outbound

This paper cites Detail Consistent Stage-Wise Distillation for Efficient 3D MRI Segmentation.

MIS-HCC: Hierarchical Channel Clustering for Efficient Medical Image Segmentation Detail Consistent Stage-Wise Distillation for Efficient 3D MRI Segmentation

Reference 17

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Observation 95ddd025-f8c2-4251-8ff2-9df8d720ca3f · outbound

This paper cites Displacement Preserving Relational Distillation for Robust Medical Segmentation.

MIS-HCC: Hierarchical Channel Clustering for Efficient Medical Image Segmentation Displacement Preserving Relational Distillation for Robust Medical Segmentation

Reference 18

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Observation 3e321438-d4c9-4015-b7bd-e75a806b93aa · outbound

This paper cites Acam-kd: adaptive and cooperative attention masking for knowledge distillation,.

MIS-HCC: Hierarchical Channel Clustering for Efficient Medical Image Segmentation Acam-kd: adaptive and cooperative attention masking for knowledge distillation,

Reference 19

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Observation 02de2d42-a78f-40a6-bf87-f1c27601653e · outbound

This paper cites Clockdistill: Consistent location and con- text aware knowledge distillation for detrs,.

MIS-HCC: Hierarchical Channel Clustering for Efficient Medical Image Segmentation Clockdistill: Consistent location and con- text aware knowledge distillation for detrs,

Reference 20

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Observation 437f4fba-5c7d-4d0f-b036-3df4d3fc026d · outbound

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

MIS-HCC: Hierarchical Channel Clustering for Efficient Medical Image Segmentation U-net: Convolutional networks for biomedical image segmentation,

Reference 21

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Observation 165473fe-62b5-4129-a1f9-7d6d52049547 · outbound

This paper cites TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation.

MIS-HCC: Hierarchical Channel Clustering for Efficient Medical Image Segmentation TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

Reference 22

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Observation f8275fc0-9728-4e16-b10a-1cb72bf6ab4d · outbound

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

MIS-HCC: Hierarchical Channel Clustering for Efficient Medical Image Segmentation An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 23

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Observation 72055d4e-d00d-4cf4-9637-1d321bf6e3cb · outbound

This paper cites Fusing structure and appearance features in facial expression recognition transformer,.

MIS-HCC: Hierarchical Channel Clustering for Efficient Medical Image Segmentation Fusing structure and appearance features in facial expression recognition transformer,

Reference 24

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Observation e23bdd32-aac5-43bb-b73b-987e1742a1b6 · outbound

This paper cites Fakeshield: Explainable image forgery detection and localization via multi-modal large language models,.

MIS-HCC: Hierarchical Channel Clustering for Efficient Medical Image Segmentation Fakeshield: Explainable image forgery detection and localization via multi-modal large language models,

Reference 25

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Observation 3ce843a6-ed74-47a8-8125-676877ffc017 · outbound

This paper cites Avatarshield: Visual reinforcement learning for human-centric synthetic video de- tection,.

MIS-HCC: Hierarchical Channel Clustering for Efficient Medical Image Segmentation Avatarshield: Visual reinforcement learning for human-centric synthetic video de- tection,

Reference 26

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Observation 69b76a21-e2fe-47ea-b3b5-eb9ea7cf097a · outbound

This paper cites Symmetry-aware causal inference for robust neural pde solvers,.

MIS-HCC: Hierarchical Channel Clustering for Efficient Medical Image Segmentation Symmetry-aware causal inference for robust neural pde solvers,

Reference 27

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Observation 4f821757-2b56-4ac7-bedb-046bf3e4556d · outbound

This paper cites Mpq-dmv2: Flexible residual mixed precision quantization for low-bit diffusion models with temporal distillation,.

MIS-HCC: Hierarchical Channel Clustering for Efficient Medical Image Segmentation Mpq-dmv2: Flexible residual mixed precision quantization for low-bit diffusion models with temporal distillation,

Reference 28

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Observation fa51d136-edce-4373-90d4-fe273253d6b0 · outbound

This paper cites RoboAlign-R1: Distilled Multimodal Reward Alignment for Robot Video World Models.

MIS-HCC: Hierarchical Channel Clustering for Efficient Medical Image Segmentation RoboAlign-R1: Distilled Multimodal Reward Alignment for Robot Video World Models

Reference 29

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Observation 52dde1a3-c628-4f0a-8d3d-7616551e7cbc · outbound

This paper cites Prediction of weaning from mechanical ventilation using convolutional neural networks,.

MIS-HCC: Hierarchical Channel Clustering for Efficient Medical Image Segmentation Prediction of weaning from mechanical ventilation using convolutional neural networks,

Reference 30

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Observation 30f44beb-eb9e-4ae2-9ed6-e7b844e5ea30 · outbound

This paper cites Filter pruning via ge- ometric median for deep convolutional neural networks acceleration,.

MIS-HCC: Hierarchical Channel Clustering for Efficient Medical Image Segmentation Filter pruning via ge- ometric median for deep convolutional neural networks acceleration,

Reference 31

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Observation 850e4f55-25fc-4edc-90c9-4d9729dc2ed6 · outbound

This paper cites Learning efficient convolutional networks through network slimming,.

MIS-HCC: Hierarchical Channel Clustering for Efficient Medical Image Segmentation Learning efficient convolutional networks through network slimming,

Reference 32

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Observation 528f9caa-2544-4e25-b987-f0ce685ee460 · outbound

This paper cites Hrank: Filter pruning using high-rank feature map,.

MIS-HCC: Hierarchical Channel Clustering for Efficient Medical Image Segmentation Hrank: Filter pruning using high-rank feature map,

Reference 33

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Observation 57bfa55a-8c36-424e-8660-eac61cb8fddd · outbound

This paper cites Chex: Channel exploration for cnn model compression,.

MIS-HCC: Hierarchical Channel Clustering for Efficient Medical Image Segmentation Chex: Channel exploration for cnn model compression,

Reference 34

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Observation 74ba5e94-684c-4fba-8be0-c3b69a378369 · outbound

This paper cites Sauron u-net: Simple automated redundancy elimination in medical image segmentation via filter pruning,.

MIS-HCC: Hierarchical Channel Clustering for Efficient Medical Image Segmentation Sauron u-net: Simple automated redundancy elimination in medical image segmentation via filter pruning,

Reference 35

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Observation f11377f2-74e1-4a35-ab4f-5b3c2fd21ba1 · outbound

This paper cites Automatic channel pruning of u-net applied to retinal blood vessels segmentation,.

MIS-HCC: Hierarchical Channel Clustering for Efficient Medical Image Segmentation Automatic channel pruning of u-net applied to retinal blood vessels segmentation,

Reference 36

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Observation fc60fcb0-1611-40f3-baa4-37e6a59d83eb · outbound

This paper cites Similarity of neural network representations revisited,.

MIS-HCC: Hierarchical Channel Clustering for Efficient Medical Image Segmentation Similarity of neural network representations revisited,

Reference 37

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Observation 1f388512-1b45-477f-8948-1edccc999b71 · outbound

This paper cites Training-free cross-modal alignment via anchor profiles with statistical significance testing,.

MIS-HCC: Hierarchical Channel Clustering for Efficient Medical Image Segmentation Training-free cross-modal alignment via anchor profiles with statistical significance testing,

Reference 38

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Observation a70c3d3c-4faf-4faa-8c36-7a5fd4eb8347 · outbound

This paper cites Curvature-adaptive consistency flow matching: Autonomous trajectory optimization via reinforcement learning,.

MIS-HCC: Hierarchical Channel Clustering for Efficient Medical Image Segmentation Curvature-adaptive consistency flow matching: Autonomous trajectory optimization via reinforcement learning,

Reference 39

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Observation 918a6cf7-2c63-4942-987d-a34446ede7c1 · outbound

This paper cites Divergence of empirical neural tangent kernel in classification problems,.

MIS-HCC: Hierarchical Channel Clustering for Efficient Medical Image Segmentation Divergence of empirical neural tangent kernel in classification problems,

Reference 40

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Observation 1713a61b-d70b-42a8-97ab-5e9e244f6d0e · outbound

This paper cites Un- supervised hyperspectral image super-resolution via self-supervised modality decoupling,.

MIS-HCC: Hierarchical Channel Clustering for Efficient Medical Image Segmentation Un- supervised hyperspectral image super-resolution via self-supervised modality decoupling,

Reference 41

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Observation cdb40d8f-c027-495a-a212-c773026a3d8b · outbound

This paper cites Pansharpening for thin-cloud contaminated remote sensing images: a unified framework and benchmark dataset,.

MIS-HCC: Hierarchical Channel Clustering for Efficient Medical Image Segmentation Pansharpening for thin-cloud contaminated remote sensing images: a unified framework and benchmark dataset,

Reference 42

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Observation be05dc64-d9a8-4c5d-9573-f82b6527fbd3 · outbound

This paper cites Frequency-decoupled learning for joint thin-cloud removal and pansharpening,.

MIS-HCC: Hierarchical Channel Clustering for Efficient Medical Image Segmentation Frequency-decoupled learning for joint thin-cloud removal and pansharpening,

Reference 43

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Observation 5abb3ab9-6637-4c5d-8cd1-5065961c30b2 · outbound

This paper cites Ips: In-prompt process supervision for short video content moderation,.

MIS-HCC: Hierarchical Channel Clustering for Efficient Medical Image Segmentation Ips: In-prompt process supervision for short video content moderation,

Reference 44

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Observation f58b996b-efee-46ae-a0b4-7d88f11f4f73 · outbound

This paper cites Reasoning-enhanced domain-adaptive pre- training of multimodal large language models for short video content governance,.

MIS-HCC: Hierarchical Channel Clustering for Efficient Medical Image Segmentation Reasoning-enhanced domain-adaptive pre- training of multimodal large language models for short video content governance,

Reference 45

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Observation 0995c606-6d85-4960-be54-61e464017898 · outbound

This paper cites Audio-enhanced vision-language modeling with latent space broadening for high quality data expansion,.

MIS-HCC: Hierarchical Channel Clustering for Efficient Medical Image Segmentation Audio-enhanced vision-language modeling with latent space broadening for high quality data expansion,

Reference 46

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Observation 61235cb2-deac-41f7-aa6d-02b9750b583b · outbound

This paper cites Render-in-the-Loop: Vector Graphics Generation via Visual Self-Feedback.

MIS-HCC: Hierarchical Channel Clustering for Efficient Medical Image Segmentation Render-in-the-Loop: Vector Graphics Generation via Visual Self-Feedback

Reference 47

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Observation b96d4f84-0d29-4953-86b4-68472b37c2a5 · outbound

This paper cites VAnim: Rendering-Aware Sparse State Modeling for Structure-Preserving Vector Animation.

MIS-HCC: Hierarchical Channel Clustering for Efficient Medical Image Segmentation VAnim: Rendering-Aware Sparse State Modeling for Structure-Preserving Vector Animation

Reference 48

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Observation 0b6dd9a3-ba9e-4c01-9879-e946bf44fded · outbound

This paper cites Multi-object sketch animation with grouping and motion trajectory priors,.

MIS-HCC: Hierarchical Channel Clustering for Efficient Medical Image Segmentation Multi-object sketch animation with grouping and motion trajectory priors,

Reference 49

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Observation fb43247f-e3b6-4171-ba33-245de5f3696e · outbound

This paper cites Spatiotemporal multi-view continual dictionary learning with graph diffusion,.

MIS-HCC: Hierarchical Channel Clustering for Efficient Medical Image Segmentation Spatiotemporal multi-view continual dictionary learning with graph diffusion,

Reference 50

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Observation 5d3f3b79-4449-4b1d-bfc4-021baf115392 · outbound

This paper cites Multi-resolution context augmentation and dual channel attention for 3d lane detection,.

MIS-HCC: Hierarchical Channel Clustering for Efficient Medical Image Segmentation Multi-resolution context augmentation and dual channel attention for 3d lane detection,

Reference 51

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Observation b00c8a41-9517-4da3-b306-66147e5dce6a · outbound

This paper cites Multi-scale convolution and dynamic task interaction detection head for efficient lightweight plum detec- tion,.

MIS-HCC: Hierarchical Channel Clustering for Efficient Medical Image Segmentation Multi-scale convolution and dynamic task interaction detection head for efficient lightweight plum detec- tion,

Reference 52

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Observation d221507f-3dcf-453b-b01e-52f397466fed · outbound

This paper cites MaPPO: Maximum a Posteriori Preference Optimization with Prior Knowledge.

MIS-HCC: Hierarchical Channel Clustering for Efficient Medical Image Segmentation MaPPO: Maximum a Posteriori Preference Optimization with Prior Knowledge

Reference 53

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Observation 8411e6ba-711d-4beb-899e-b3537f25b906 · outbound

This paper cites Ants: Adaptive negative textual space shaping for ood detection via test-time mllm understanding and reasoning,.

MIS-HCC: Hierarchical Channel Clustering for Efficient Medical Image Segmentation Ants: Adaptive negative textual space shaping for ood detection via test-time mllm understanding and reasoning,

Reference 54

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Observation 4b4a8268-b66f-456e-90d1-a2f309bf2f96 · outbound

This paper cites Unet++: A nested u-net architecture for medical image segmentation,.

MIS-HCC: Hierarchical Channel Clustering for Efficient Medical Image Segmentation Unet++: A nested u-net architecture for medical image segmentation,

Reference 55

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Observation e31f6990-ed8b-45bf-9c1b-325f61f2ab3d · outbound

This paper cites Learning how to use tools, not just when: Pattern-aware tool-integrated reasoning,.

MIS-HCC: Hierarchical Channel Clustering for Efficient Medical Image Segmentation Learning how to use tools, not just when: Pattern-aware tool-integrated reasoning,

Reference 56

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Observation 00633776-1744-46f4-a99e-a913c946f63e · outbound

This paper cites Find your optimal teacher: Personalized data synthesis via router-guided multi-teacher distillation,.

MIS-HCC: Hierarchical Channel Clustering for Efficient Medical Image Segmentation Find your optimal teacher: Personalized data synthesis via router-guided multi-teacher distillation,

Reference 57

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Observation 3ef12b09-db81-4130-9d15-a279ab094e3e · outbound

This paper cites Drp: Distilled reasoning pruning with skill-aware step decomposition for efficient large reasoning models,.

MIS-HCC: Hierarchical Channel Clustering for Efficient Medical Image Segmentation Drp: Distilled reasoning pruning with skill-aware step decomposition for efficient large reasoning models,

Reference 58

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This paper cites Neural clustering based visual representation learning,.

MIS-HCC: Hierarchical Channel Clustering for Efficient Medical Image Segmentation Neural clustering based visual representation learning,

Reference 59

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Observation fc8cd6c6-e718-4ce8-84e2-cf4d344fd95c · outbound

This paper cites Dataset of breast ultrasound images,.

MIS-HCC: Hierarchical Channel Clustering for Efficient Medical Image Segmentation Dataset of breast ultrasound images,

Reference 60

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This paper cites Nucleus segmentation across imaging experiments: the 2018 data science bowl,.

MIS-HCC: Hierarchical Channel Clustering for Efficient Medical Image Segmentation Nucleus segmentation across imaging experiments: the 2018 data science bowl,

Reference 61

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Observation f85ab066-dd52-45ac-8b26-088ccfc8e31f · outbound

This paper cites Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC).

MIS-HCC: Hierarchical Channel Clustering for Efficient Medical Image Segmentation Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)

Reference 62

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Observation 7ae8d60c-4ddb-4d0e-b0eb-e963a8b25613 · outbound

This paper cites Fast and low-gpu-memory abdomen ct organ segmentation: the flare challenge,.

MIS-HCC: Hierarchical Channel Clustering for Efficient Medical Image Segmentation Fast and low-gpu-memory abdomen ct organ segmentation: the flare challenge,

Reference 63

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Observation a0eb3acd-a51d-4379-bf15-d81ade15a3ee · outbound

This paper cites MedSAM2: Segment Anything in 3D Medical Images and Videos.

MIS-HCC: Hierarchical Channel Clustering for Efficient Medical Image Segmentation MedSAM2: Segment Anything in 3D Medical Images and Videos

Reference 64

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Observation 28198cd8-f48f-4fcd-9cb9-6ee079c88360 · outbound

This paper cites Pruning Filters for Efficient ConvNets.

MIS-HCC: Hierarchical Channel Clustering for Efficient Medical Image Segmentation Pruning Filters for Efficient ConvNets

Reference 65

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Observation 7d0b529d-62fe-49f6-91e6-fcb6579a52e9 · outbound

This paper cites Trainability preserving neural pruning,.

MIS-HCC: Hierarchical Channel Clustering for Efficient Medical Image Segmentation Trainability preserving neural pruning,

Reference 66

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