SPA: Efficient User-Preference Alignment against Uncertainty in Medical Image Segmentation | Paper Citation Record

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This paper cites Armato, Geoffrey McLennan, Luc Bidaut, Michael F.

SPA: Efficient User-Preference Alignment against Uncertainty in Medical Image Segmentation Armato, Geoffrey McLennan, Luc Bidaut, Michael F

Reference 1

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Observation 1083ee66-f7ae-41d4-9c60-ab7140066668 · outbound

This paper cites Fisher, Thomas R.

SPA: Efficient User-Preference Alignment against Uncertainty in Medical Image Segmentation Fisher, Thomas R

Reference 2

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Observation 24fccd79-d36d-4ad3-8121-67143d20d63a · outbound

This paper cites Baumgartner, Kerem C.

SPA: Efficient User-Preference Alignment against Uncertainty in Medical Image Segmentation Baumgartner, Kerem C

Reference 3

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This paper cites Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation,.

SPA: Efficient User-Preference Alignment against Uncertainty in Medical Image Segmentation Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation,

Reference 4

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

SPA: Efficient User-Preference Alignment against Uncertainty in Medical Image Segmentation TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

Reference 5

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This paper cites FocalClick: Towards Practical Interactive Image Segmentation.

SPA: Efficient User-Preference Alignment against Uncertainty in Medical Image Segmentation FocalClick: Towards Practical Interactive Image Segmentation

Reference 6

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This paper cites The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository.

SPA: Efficient User-Preference Alignment against Uncertainty in Medical Image Segmentation The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository

Reference 7

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This paper cites A comparison between three-and four-option multiple choice questions.

SPA: Efficient User-Preference Alignment against Uncertainty in Medical Image Segmentation A comparison between three-and four-option multiple choice questions

Reference 8

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Observation 05da17d5-3843-46b9-bc69-3025419a17e1 · outbound

This paper cites SAM-U: Multi-box prompts triggered uncertainty estimation for reliable SAM in medical image.

SPA: Efficient User-Preference Alignment against Uncertainty in Medical Image Segmentation SAM-U: Multi-box prompts triggered uncertainty estimation for reliable SAM in medical image

Reference 9

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This paper cites REFUGE2 Challenge: A Treasure Trove for Multi-Dimension Analysis and Evaluation in Glaucoma Screening.

SPA: Efficient User-Preference Alignment against Uncertainty in Medical Image Segmentation REFUGE2 Challenge: A Treasure Trove for Multi-Dimension Analysis and Evaluation in Glaucoma Screening

Reference 10

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This paper cites Who Said What: Modeling Individual Labelers Improves Classification.

SPA: Efficient User-Preference Alignment against Uncertainty in Medical Image Segmentation Who Said What: Modeling Individual Labelers Improves Classification

Reference 11

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This paper cites Masked Autoencoders Are Scalable Vision Learners.

SPA: Efficient User-Preference Alignment against Uncertainty in Medical Image Segmentation Masked Autoencoders Are Scalable Vision Learners

Reference 12

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This paper cites Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges.

SPA: Efficient User-Preference Alignment against Uncertainty in Medical Image Segmentation Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges

Reference 13

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This paper cites A review of uncertainty quantification in medical image analysis: Probabilistic and non-probabilistic methods.

SPA: Efficient User-Preference Alignment against Uncertainty in Medical Image Segmentation A review of uncertainty quantification in medical image analysis: Probabilistic and non-probabilistic methods

Reference 14

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This paper cites Improving Uncertainty Estimation in Convolutional Neural Networks Using Inter-rater Agreement.

SPA: Efficient User-Preference Alignment against Uncertainty in Medical Image Segmentation Improving Uncertainty Estimation in Convolutional Neural Networks Using Inter-rater Agreement

Reference 15

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This paper cites Learning Calibrated Medical Image Segmentation via Multi- rater Agreement Modeling.

SPA: Efficient User-Preference Alignment against Uncertainty in Medical Image Segmentation Learning Calibrated Medical Image Segmentation via Multi- rater Agreement Modeling

Reference 16

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This paper cites What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?.

SPA: Efficient User-Preference Alignment against Uncertainty in Medical Image Segmentation What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?

Reference 17

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This paper cites Segment Anything.

SPA: Efficient User-Preference Alignment against Uncertainty in Medical Image Segmentation Segment Anything

Reference 18

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This paper cites Aleatory or epis- temic? Does it matter? Structural Safety, 31(2):105–112,.

SPA: Efficient User-Preference Alignment against Uncertainty in Medical Image Segmentation Aleatory or epis- temic? Does it matter? Structural Safety, 31(2):105–112,

Reference 19

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This paper cites an unresolved cited work.

SPA: Efficient User-Preference Alignment against Uncertainty in Medical Image Segmentation Unresolved cited work

Reference 20

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This paper cites A Hierarchical Probabilistic U-Net for Modeling Multi-Scale Ambiguities.

SPA: Efficient User-Preference Alignment against Uncertainty in Medical Image Segmentation A Hierarchical Probabilistic U-Net for Modeling Multi-Scale Ambiguities

Reference 21

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This paper cites A Probabilistic U-Net for Segmentation of Ambiguous Images.

SPA: Efficient User-Preference Alignment against Uncertainty in Medical Image Segmentation A Probabilistic U-Net for Segmentation of Ambiguous Images

Reference 22

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This paper cites QUBIQ: Uncertainty Quantification for Biomedical Image Segmentation Challenge.

SPA: Efficient User-Preference Alignment against Uncertainty in Medical Image Segmentation QUBIQ: Uncertainty Quantification for Biomedical Image Segmentation Challenge

Reference 23

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This paper cites SimpleClick: Interactive Image Segmentation with Simple Vision Transformers.

SPA: Efficient User-Preference Alignment against Uncertainty in Medical Image Segmentation SimpleClick: Interactive Image Segmentation with Simple Vision Transformers

Reference 24

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This paper cites Segment Anything in Medical Images.

SPA: Efficient User-Preference Alignment against Uncertainty in Medical Image Segmentation Segment Anything in Medical Images

Reference 25

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This paper cites Prasanna, Helen B.

SPA: Efficient User-Preference Alignment against Uncertainty in Medical Image Segmentation Prasanna, Helen B

Reference 26

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This paper cites U-Net: Convolutional Networks for Biomedical Image Segmentation,.

SPA: Efficient User-Preference Alignment against Uncertainty in Medical Image Segmentation U-Net: Convolutional Networks for Biomedical Image Segmentation,

Reference 27

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Observation bbc743ce-cf15-425b-9a5e-5731aaaf7d55 · outbound

This paper cites Learning in an Uncertain World: Representing Ambiguity Through Multiple Hypotheses.

SPA: Efficient User-Preference Alignment against Uncertainty in Medical Image Segmentation Learning in an Uncertain World: Representing Ambiguity Through Multiple Hypotheses

Reference 28

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This paper cites Interactive segmentation of medical images through fully convolutional neural networks.

SPA: Efficient User-Preference Alignment against Uncertainty in Medical Image Segmentation Interactive segmentation of medical images through fully convolutional neural networks

Reference 29

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This paper cites Reviving Iterative Training with Mask Guidance for Interactive Segmentation.

SPA: Efficient User-Preference Alignment against Uncertainty in Medical Image Segmentation Reviving Iterative Training with Mask Guidance for Interactive Segmentation

Reference 30

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This paper cites Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?.

SPA: Efficient User-Preference Alignment against Uncertainty in Medical Image Segmentation Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?

Reference 31

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This paper cites Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning.

SPA: Efficient User-Preference Alignment against Uncertainty in Medical Image Segmentation Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning

Reference 32

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This paper cites Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation,.

SPA: Efficient User-Preference Alignment against Uncertainty in Medical Image Segmentation Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation,

Reference 33

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Observation 557f5d80-a82b-4391-9e81-950c75edc1e7 · outbound

This paper cites Disentangling Human Error from the Ground Truth in Segmentation of Medical Images.

SPA: Efficient User-Preference Alignment against Uncertainty in Medical Image Segmentation Disentangling Human Error from the Ground Truth in Segmentation of Medical Images

Reference 34

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This paper cites Medical SAM 2: Segment medical images as video via Segment Anything Model 2.

SPA: Efficient User-Preference Alignment against Uncertainty in Medical Image Segmentation Medical SAM 2: Segment medical images as video via Segment Anything Model 2

Reference 35

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Observation 8dbfdedf-0a4b-4a18-9549-13feb5989ab6 · outbound

This paper cites A Review of Uncertainty Estimation and its Application in Medical Imaging.

SPA: Efficient User-Preference Alignment against Uncertainty in Medical Image Segmentation A Review of Uncertainty Estimation and its Application in Medical Imaging

Reference 36

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This paper cites A preliminary experiment testing the impact of individual clinicians, conducted for the optic cup segmentation on REFUGE2 test set under U-Net’s structure with Dice Score (%).

SPA: Efficient User-Preference Alignment against Uncertainty in Medical Image Segmentation A preliminary experiment testing the impact of individual clinicians, conducted for the optic cup segmentation on REFUGE2 test set under U-Net’s structure with Dice Score (%)

Reference 41

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This paper cites from a specific component N (µu, σ2 u).

SPA: Efficient User-Preference Alignment against Uncertainty in Medical Image Segmentation from a specific component N (µu, σ2 u)

Reference 42

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This paper cites 48 predictions is experimentally the best to balance model performance and computational cost.

SPA: Efficient User-Preference Alignment against Uncertainty in Medical Image Segmentation 48 predictions is experimentally the best to balance model performance and computational cost

Reference 43

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This paper cites We provide the failure rate statistics for REFUGE2 dataset reaching Dice 70% and 80%, for LIDC reaching Dice 60% and 70% in Table 9.

SPA: Efficient User-Preference Alignment against Uncertainty in Medical Image Segmentation We provide the failure rate statistics for REFUGE2 dataset reaching Dice 70% and 80%, for LIDC reaching Dice 60% and 70% in Table 9

Reference 44

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This paper cites Overall Diff.

SPA: Efficient User-Preference Alignment against Uncertainty in Medical Image Segmentation Overall Diff

Reference 45

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This paper cites Diff 1” refers to the improvement from Iteration 1 to Iteration 2, “Diff 2.

SPA: Efficient User-Preference Alignment against Uncertainty in Medical Image Segmentation Diff 1” refers to the improvement from Iteration 1 to Iteration 2, “Diff 2

Reference 46

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This paper cites Five medical professionals, each with over five years of graduate-level expertise, participated in the study.

SPA: Efficient User-Preference Alignment against Uncertainty in Medical Image Segmentation Five medical professionals, each with over five years of graduate-level expertise, participated in the study

Reference 47

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Observation c4a54bc4-168a-4b23-9c6f-1a3712d138b8 · outbound

This paper cites 7 illustrates a visual comparison of the differences between SPA’s segmentation predictions and individual clin- icians’ annotations over six iterations.

SPA: Efficient User-Preference Alignment against Uncertainty in Medical Image Segmentation 7 illustrates a visual comparison of the differences between SPA’s segmentation predictions and individual clin- icians’ annotations over six iterations

Reference 48

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Observation 8ed3d5b1-932f-4024-9a1a-47d010c8b80b · outbound

This paper cites U-Net: Convolutional Networks for Biomedical Image Segmentation.

SPA: Efficient User-Preference Alignment against Uncertainty in Medical Image Segmentation U-Net: Convolutional Networks for Biomedical Image Segmentation

Reference 2015

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SPA: Efficient User-Preference Alignment against Uncertainty in Medical Image Segmentation Unresolved cited work

Reference 2019

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Observation 9b91565e-a975-4433-a2f0-a9184ded2a69 · outbound

This paper cites Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation.

SPA: Efficient User-Preference Alignment against Uncertainty in Medical Image Segmentation Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation

Reference 2021

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Observation 5d0e1151-2615-4092-ace7-8afba6cde903 · outbound

This paper cites Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation.

SPA: Efficient User-Preference Alignment against Uncertainty in Medical Image Segmentation Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 2023

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