FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images | Paper Citation Record

Observation 071f1de4-1d61-435e-83ff-674ad8d8808c · outbound

This paper cites Single-stage semantic segmentation from image labels, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp.

FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images Single-stage semantic segmentation from image labels, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp

Reference 1

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Observation a2a0d55a-b822-4a8d-8819-babc68ed20d6 · outbound

This paper cites Weakly and semi supervised detection in medical imaging via deep dual branch net.

FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images Weakly and semi supervised detection in medical imaging via deep dual branch net

Reference 2

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This paper cites What’s the point: Semantic segmentation with point supervision, in: Euro- pean conference on computer vision, Springer.

FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images What’s the point: Semantic segmentation with point supervision, in: Euro- pean conference on computer vision, Springer

Reference 3

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This paper cites Clinical-grade computational pathology using weakly supervised deep learning on whole slide images.

FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images Clinical-grade computational pathology using weakly supervised deep learning on whole slide images

Reference 4

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This paper cites Multi-branch spectral channel attention network for breast cancer histopathology image classification.

FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images Multi-branch spectral channel attention network for breast cancer histopathology image classification

Reference 5

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FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images Unresolved cited work

Reference 6

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This paper cites Encoder-decoder with atrous separable convolution for semantic im- age segmentation, in: Proceedings of the European conference on computer vision (ECCV), pp.

FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images Encoder-decoder with atrous separable convolution for semantic im- age segmentation, in: Proceedings of the European conference on computer vision (ECCV), pp

Reference 7

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Observation 96e81d30-f8ca-4491-8a9b-e48f83398235 · outbound

This paper cites Multiple instance learning with bag dissimilarities.

FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images Multiple instance learning with bag dissimilarities

Reference 8

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Observation 743f6931-b7a0-4c33-8b0a-64efe697d660 · outbound

This paper cites Boxsup: Exploiting bounding boxes to supervise convolutional networks for semantic segmentation, in: ProceedingsoftheIEEEinternationalconferenceoncomputervision, pp.

FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images Boxsup: Exploiting bounding boxes to supervise convolutional networks for semantic segmentation, in: ProceedingsoftheIEEEinternationalconferenceoncomputervision, pp

Reference 9

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Observation 7786ad50-8def-4055-a165-e4c2a2789c0d · outbound

This paper cites Solving the multiple instance problem with axis-parallel rectangles.

FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images Solving the multiple instance problem with axis-parallel rectangles

Reference 10

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This paper cites Slf-unet: Improved unet for brain mri segmentation by combining spatial and low-frequency domain features, in: Computer Graphics International Conference, Springer.

FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images Slf-unet: Improved unet for brain mri segmentation by combining spatial and low-frequency domain features, in: Computer Graphics International Conference, Springer

Reference 11

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FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images Unresolved cited work

Reference 12

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Observation 8cea6532-5564-44a6-8b28-88409a07ef09 · outbound

This paper cites Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer.

FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer

Reference 13

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FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images Unresolved cited work

Reference 14

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This paper cites Mammil: Multiple instance learning for whole slide images with state space models, in: 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE.

FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images Mammil: Multiple instance learning for whole slide images with state space models, in: 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE

Reference 15

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Observation 4191f585-4c37-40ec-9d95-d64435c14fb5 · outbound

This paper cites Fiba: Frequency-injectionbasedbackdoorattackinmedicalimageanalysis, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.

FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images Fiba: Frequency-injectionbasedbackdoorattackinmedicalimageanalysis, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

Reference 16

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Observation fe4a3b3d-3256-43d8-8026-54d11d18daff · outbound

This paper cites Mamba: Linear-Time Sequence Modeling with Selective State Spaces.

FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 17

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This paper cites Hippo:Recurrent memory with optimal polynomial projections.

FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images Hippo:Recurrent memory with optimal polynomial projections

Reference 18

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This paper cites Asurveyonvisiontransformer.

FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images Asurveyonvisiontransformer

Reference 19

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Observation 455455a8-c825-46e8-8547-d19e32879b05 · outbound

This paper cites T-Mamba: A unified framework with Long-Range Dependency in dual-domain for 2D & 3D Tooth Segmentation.

FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images T-Mamba: A unified framework with Long-Range Dependency in dual-domain for 2D & 3D Tooth Segmentation

Reference 20

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Observation 91d5f29e-f163-4942-84e2-e03e09765cf8 · outbound

This paper cites Deep residual learning for image recognition, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp.

FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images Deep residual learning for image recognition, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp

Reference 21

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FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images Unresolved cited work

Reference 22

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Observation 311b619b-a8ec-4cc3-b34b-82ad7c63480f · outbound

This paper cites State-space models are accurate and efficient neural operators for dynamical systems.

FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images State-space models are accurate and efficient neural operators for dynamical systems

Reference 23

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Observation f7c8253b-1c9b-49e9-8160-7ad9b664f113 · outbound

This paper cites Attention-based deep multiple instance learning, in: International conference on machine learning, PMLR.

FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images Attention-based deep multiple instance learning, in: International conference on machine learning, PMLR

Reference 24

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This paper cites Online attentionaccumulationforweaklysupervisedsemanticsegmentation.

FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images Online attentionaccumulationforweaklysupervisedsemanticsegmentation

Reference 25

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FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images Unresolved cited work

Reference 26

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FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images Unresolved cited work

Reference 27

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This paper cites Weakly-supervised learning for lung carcinoma classification using deep learning.

FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images Weakly-supervised learning for lung carcinoma classification using deep learning

Reference 28

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FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images Unresolved cited work

Reference 30

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This paper cites Videomamba:Statespacemodelforefficientvideounderstanding,in: European Conference on Computer Vision, Springer.

FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images Videomamba:Statespacemodelforefficientvideounderstanding,in: European Conference on Computer Vision, Springer

Reference 31

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This paper cites Weakly supervised histopathology image segmentation with self-attention.

FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images Weakly supervised histopathology image segmentation with self-attention

Reference 32

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This paper cites Group-wise semantic mining for weakly supervised semantic segmentation, in: Proceedings of the AAAI conference on artificial intelligence, pp.

FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images Group-wise semantic mining for weakly supervised semantic segmentation, in: Proceedings of the AAAI conference on artificial intelligence, pp

Reference 33

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This paper cites Modeling annotator prefer- enceandstochasticannotationerrorformedicalimagesegmentation.

FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images Modeling annotator prefer- enceandstochasticannotationerrorformedicalimagesegmentation

Reference 34

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This paper cites Scan- net:Afastanddensescanningframeworkformetastasticbreastcancer detection from whole-slide image, in: 2018 IEEE winter conference on applications of computer vision (WACV), IEEE.

FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images Scan- net:Afastanddensescanningframeworkformetastasticbreastcancer detection from whole-slide image, in: 2018 IEEE winter conference on applications of computer vision (WACV), IEEE

Reference 35

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FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images Unresolved cited work

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Reference 37

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This paper cites A review of deep- learning-based medical image segmentation methods.

FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images A review of deep- learning-based medical image segmentation methods

Reference 38

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Reference 39

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FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images Unresolved cited work

Reference 40

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FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images Decoupled Weight Decay Regularization

Reference 41

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FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images Data-efficient and weakly supervised com- putational pathology on whole-slide images

Reference 42

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FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images A framework for multiple- instancelearning

Reference 43

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FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images Unresolved cited work

Reference 44

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This paper cites Bi-directional weakly supervisedknowledgedistillationforwholeslideimageclassification.

FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images Bi-directional weakly supervisedknowledgedistillationforwholeslideimageclassification

Reference 45

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This paper cites 11976– 11986.

FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images 11976– 11986

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:35:41.770036Z

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FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images Unresolved cited work

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This paper cites Learning multi-axis representation in frequency domain for medical image segmentation.

FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images Learning multi-axis representation in frequency domain for medical image segmentation

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:35:41.700638Z

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Observation 9a073c65-28ee-4c97-a325-a46b523380cf · outbound

This paper cites Transmil:Transformerbasedcorrelatedmultipleinstancelearningfor whole slide image classification.

FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images Transmil:Transformerbasedcorrelatedmultipleinstancelearningfor whole slide image classification

Reference 49

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This paper cites Attention is all you need.

FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images Attention is all you need

Reference 50

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This paper cites A Survey of Mamba.

FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images A Survey of Mamba

Reference 51

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FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images Unresolved cited work

Reference 52

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This paper cites IEEEreviews in biomedical engineering 10, 213–234.

FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images IEEEreviews in biomedical engineering 10, 213–234

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:35:41.717649Z

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This paper cites The American journal of pathology 189, 1686–1698.

FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images The American journal of pathology 189, 1686–1698

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:35:41.640570Z

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This paper cites Weakly supervised deep learning for whole slide lung cancer image analysis.

FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images Weakly supervised deep learning for whole slide lung cancer image analysis

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:35:41.629328Z

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This paper cites Ud-mil: uncertainty-driven deep multiple instance learning for oct image classification.

FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images Ud-mil: uncertainty-driven deep multiple instance learning for oct image classification

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:35:41.618706Z

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This paper cites State Space Model for New-Generation Network Alternative to Transformers: A Survey.

FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images State Space Model for New-Generation Network Alternative to Transformers: A Survey

Reference 57

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This paper cites An Empirical Study of Mamba-based Language Models.

FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images An Empirical Study of Mamba-based Language Models

Reference 58

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Reference 59

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FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images Unresolved cited work

Reference 60

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This paper cites Annotation-efficientdeeplearning for automatic medical image segmentation.

FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images Annotation-efficientdeeplearning for automatic medical image segmentation

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:35:41.652297Z

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This paper cites A whole-slide foundation model for digital pathology from real-world data.

FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images A whole-slide foundation model for digital pathology from real-world data

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:35:41.566049Z

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Observation 7803a00d-4c27-4aff-af1c-c893e3f829ce · outbound

This paper cites Learning in the frequency domain, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp.

FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images Learning in the frequency domain, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:35:41.555041Z

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FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images Unresolved cited work

Reference 64

Resolution: unresolved; raw_fallback, observed 2026-08-07T05:35:41.544353Z

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Observation 83a95cf9-b7f5-455e-addd-7b166a7496be · outbound

This paper cites Large scale tissue histopathology image classification, segmentation, and visualization via deep convolutional activation features.

FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images Large scale tissue histopathology image classification, segmentation, and visualization via deep convolutional activation features

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:35:41.522907Z

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This paper cites Revisiting multiple instance neural networks.

FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images Revisiting multiple instance neural networks

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:35:41.608702Z

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Reference 67

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Reference 68

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This paper cites Learningfrom massivenoisylabeleddataforimageclassification,in:Proceedingsof the IEEE conference on computer vision and pattern recognition, pp.

FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images Learningfrom massivenoisylabeleddataforimageclassification,in:Proceedingsof the IEEE conference on computer vision and pattern recognition, pp

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:35:41.577344Z

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Observation ad398a94-6ce4-45c5-9be4-f30d38ca6376 · outbound

This paper cites Attention-challenging multiple instance learning for whole slide im- age classification, in: European Conference on Computer Vision, Springer.

FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images Attention-challenging multiple instance learning for whole slide im- age classification, in: European Conference on Computer Vision, Springer

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:35:41.477297Z

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Observation 43c33d73-010a-43e3-8627-18ccb1eda86e · outbound

This paper cites Mamba2MIL: State Space Duality Based Multiple Instance Learning for Computational Pathology.

FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images Mamba2MIL: State Space Duality Based Multiple Instance Learning for Computational Pathology

Reference 71

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FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images Unresolved cited work

Reference 72

Resolution: unresolved; raw_fallback, observed 2026-08-07T05:35:41.466299Z

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Observation a33dce15-031c-4b8e-a096-69c4f519a15b · outbound

This paper cites Learning deep features for discriminative localization, in: Proceed- ings of the IEEE conference on computer vision and pattern recogni- tion, pp.

FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images Learning deep features for discriminative localization, in: Proceed- ings of the IEEE conference on computer vision and pattern recogni- tion, pp

Reference 73

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Observation b7e5ee33-1bc6-41d7-a893-250024926f86 · outbound

This paper cites Spatial-Frequency Dual Progressive Attention Network For Medical Image Segmentation.

FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images Spatial-Frequency Dual Progressive Attention Network For Medical Image Segmentation

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-07T05:35:41.198544Z

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This paper cites Weakly supervisedhistopathology cancerimage segmentationand classifica- tion.

FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images Weakly supervisedhistopathology cancerimage segmentationand classifica- tion

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:35:41.512334Z

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Observation dd4d33f9-be5a-462d-9455-3a1c5e057937 · outbound

This paper cites Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model.

FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model

Reference 76

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Observation e91fa7f0-1c6e-419e-a876-0557d24070ba · outbound

This paper cites Whole slide images based cancer survival prediction using attention guided deep multiple instance learning networks.

FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images Whole slide images based cancer survival prediction using attention guided deep multiple instance learning networks

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:35:41.488509Z

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Observation a8f0fc12-507d-45fb-af30-4a5fd4219f57 · outbound

This paper cites MedMamba: Vision Mamba for Medical Image Classification.

FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images MedMamba: Vision Mamba for Medical Image Classification

Reference 78

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Observation 89921d5b-426a-4c26-8aac-7b78e66ba1f2 · outbound

This paper cites bioRxiv , 2024–08.

FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images bioRxiv , 2024–08

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:35:41.455227Z

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Observation e3c249bc-7dfd-484e-a64a-2dca365d8427 · outbound

This paper cites 2424–2433.

FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images 2424–2433

Reference 2016

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:35:41.976844Z

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Observation 222dfd7c-10ef-4bc5-836d-703c05062b58 · outbound

This paper cites Medical image analysis 42, 60–88.

FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images Medical image analysis 42, 60–88

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-07T05:35:41.042471Z

Source-reported events for the cited work

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Observation a8309a49-833b-4867-bd78-d4a8020dd2f7 · outbound

This paper cites 2070–2079.

FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images 2070–2079

Reference 2019

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:35:41.934995Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6f14bfb5-7ce9-4218-87c3-89f74e4e121f · outbound

This paper cites 6984–6993.

FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images 6984–6993

Reference 2021

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:35:41.532958Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3a22618d-fd0f-49b3-b0c7-07c0b6fa3216 · outbound

This paper cites ACM computing surveys (CSUR) 54, 1–41.

FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images ACM computing surveys (CSUR) 54, 1–41

Reference 2022

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:35:41.894877Z

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Observation b9b3f86b-b150-4434-b698-6f3ad34f6b94 · outbound

This paper cites VMamba: Visual State Space Model.

FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images VMamba: Visual State Space Model

Reference 2024

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