LM-Net: A Light-weight and Multi-scale Network for Medical Image Segmentation | Paper Citation Record

Observation ee8c5d96-d465-47b7-8854-6970adcecdbe · outbound

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

LM-Net: A Light-weight and Multi-scale Network for Medical Image Segmentation U-net: Convolutional net- works for biomedical image segmentation

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:49:00.410943Z

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 fe76cfae-cf2c-41b1-9bc4-88ad6d959138 · outbound

This paper cites A review of deep learning segmentation methods for carotid artery ultrasound images.

LM-Net: A Light-weight and Multi-scale Network for Medical Image Segmentation A review of deep learning segmentation methods for carotid artery ultrasound images

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:49:00.392616Z

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 74309db0-43c7-4d3a-a017-5308bb2ccbd7 · outbound

This paper cites Resunet++: An advanced architecture for medical image segmentation.

LM-Net: A Light-weight and Multi-scale Network for Medical Image Segmentation Resunet++: An advanced architecture for medical image segmentation

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:49:00.374205Z

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 fbec25e0-34ce-4899-899a-f62e04d4bbd2 · outbound

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

LM-Net: A Light-weight and Multi-scale Network for Medical Image Segmentation Unet++: A nested u-net architecture for medical image segmentation

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:49:00.354526Z

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 ccd76356-d001-4e0d-9000-9774f8e78c63 · outbound

This paper cites Unet 3+: A full-scale connected 20 unet for medical image segmentation.

LM-Net: A Light-weight and Multi-scale Network for Medical Image Segmentation Unet 3+: A full-scale connected 20 unet for medical image segmentation

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:49:00.337312Z

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 b43ddaa3-f652-4a5f-98a8-efeb7213ee9e · outbound

This paper cites Road extraction by deep residual u-net.

LM-Net: A Light-weight and Multi-scale Network for Medical Image Segmentation Road extraction by deep residual u-net

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-10T21:48:59.282779Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6e178aa3-6475-4a34-9501-2bdad58c1a8f · outbound

This paper cites 3d u-net: learning dense volumetric segmentation from sparse annotation.

LM-Net: A Light-weight and Multi-scale Network for Medical Image Segmentation 3d u-net: learning dense volumetric segmentation from sparse annotation

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:49:00.312377Z

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 c9837add-ac90-43fb-9cea-70f7a0ab71c5 · outbound

This paper cites V-net: Fully convolutional neural networks for volumetric medical image segmentation.

LM-Net: A Light-weight and Multi-scale Network for Medical Image Segmentation V-net: Fully convolutional neural networks for volumetric medical image segmentation

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:49:00.296389Z

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 925a8e16-5791-4c34-b10b-d8382a5d8e88 · outbound

This paper cites Automatic 3-d imaging and measurement of human spines with a robotic ultrasound system.

LM-Net: A Light-weight and Multi-scale Network for Medical Image Segmentation Automatic 3-d imaging and measurement of human spines with a robotic ultrasound system

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:49:00.274121Z

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 78fdf6d7-4566-4d25-b735-32a0128d8108 · outbound

This paper cites Squeeze-and-excitation networks.

LM-Net: A Light-weight and Multi-scale Network for Medical Image Segmentation Squeeze-and-excitation networks

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-10T21:48:59.301097Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 242bb5e2-a823-46bf-babe-3e33ed199799 · outbound

This paper cites Pyramid feature attention network for saliency detection.

LM-Net: A Light-weight and Multi-scale Network for Medical Image Segmentation Pyramid feature attention network for saliency detection

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:49:00.247436Z

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 4ce7d557-d4d0-48a5-8b93-3bb008a6811b · outbound

This paper cites Cbam: Convolu- tional block attention module.

LM-Net: A Light-weight and Multi-scale Network for Medical Image Segmentation Cbam: Convolu- tional block attention module

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:49:00.234462Z

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 0be4d05b-c016-4966-9b02-8fb1bfe73936 · outbound

This paper cites Attention U-Net: Learning Where to Look for the Pancreas.

LM-Net: A Light-weight and Multi-scale Network for Medical Image Segmentation Attention U-Net: Learning Where to Look for the Pancreas

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-10T21:48:59.313457Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d8306f72-a706-4fd4-b8cb-16eed54976ed · outbound

This paper cites Segmentation information with atten- tion integration for classification of breast tumor in ultrasound image.

LM-Net: A Light-weight and Multi-scale Network for Medical Image Segmentation Segmentation information with atten- tion integration for classification of breast tumor in ultrasound image

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:49:00.219292Z

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 6d7f0b22-2fbc-4193-9052-cff2c3abbf46 · outbound

This paper cites Anatomical prior based vertebra modelling for reappearance of human spines.

LM-Net: A Light-weight and Multi-scale Network for Medical Image Segmentation Anatomical prior based vertebra modelling for reappearance of human spines

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:49:00.204634Z

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 6fa9c496-f0c7-4be6-b590-c10b0b7c232a · outbound

This paper cites Evaluation of pulmonary edema using ultrasound imaging in patients with covid-19 pneumonia based on a non-local channel attention resnet.

LM-Net: A Light-weight and Multi-scale Network for Medical Image Segmentation Evaluation of pulmonary edema using ultrasound imaging in patients with covid-19 pneumonia based on a non-local channel attention resnet

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:49:00.191555Z

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 ddb5a565-42c0-48c7-841a-86ccb91aff3b · outbound

This paper cites Nag-net: Nested attention-guided learning for segmentation of carotid lumen- intima interface and media-adventitia interface.

LM-Net: A Light-weight and Multi-scale Network for Medical Image Segmentation Nag-net: Nested attention-guided learning for segmentation of carotid lumen- intima interface and media-adventitia interface

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:49:00.178048Z

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 b891464a-9041-45ba-a4d7-4d9859dbe9a5 · outbound

This paper cites Bsmnet: Boundary-salience multi-branch network for intima-media identification in carotid ultrasound images.

LM-Net: A Light-weight and Multi-scale Network for Medical Image Segmentation Bsmnet: Boundary-salience multi-branch network for intima-media identification in carotid ultrasound images

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:49:00.163222Z

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 2fea7138-a967-4744-b61b-bc2940a29ec1 · outbound

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

LM-Net: A Light-weight and Multi-scale Network for Medical Image Segmentation An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-10T21:48:59.344224Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 802e5a63-76bf-4750-a909-274a50a67c1e · outbound

This paper cites Medical transformer: Gated axial-attention for medical image segmentation.

LM-Net: A Light-weight and Multi-scale Network for Medical Image Segmentation Medical transformer: Gated axial-attention for medical image segmentation

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:49:00.148419Z

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 094cff8d-2b2e-4182-b06e-303a40d21f1f · outbound

This paper cites Swin-unet: Unet-like pure transformer for medical image segmentation.

LM-Net: A Light-weight and Multi-scale Network for Medical Image Segmentation Swin-unet: Unet-like pure transformer for medical image segmentation

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:49:00.133574Z

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 6cd8f8a8-7f62-47fe-b50e-09ada2b9feea · outbound

This paper cites Multi-compound transformer for accurate biomedical image segmentation.

LM-Net: A Light-weight and Multi-scale Network for Medical Image Segmentation Multi-compound transformer for accurate biomedical image segmentation

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:49:00.119215Z

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 bc6bf3c5-fd78-4ec2-92c1-79295309e39d · outbound

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

LM-Net: A Light-weight and Multi-scale Network for Medical Image Segmentation TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-10T21:48:59.363789Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 462fa65e-a474-4f9c-ba64-d5c78a35a641 · outbound

This paper cites Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer.

LM-Net: A Light-weight and Multi-scale Network for Medical Image Segmentation Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:49:00.101707Z

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 1faa3742-8a21-49f4-9cb9-ff7e1ab5f34a · outbound

This paper cites Repvgg: Making vgg-style convnets great again.

LM-Net: A Light-weight and Multi-scale Network for Medical Image Segmentation Repvgg: Making vgg-style convnets great again

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:49:00.086635Z

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 f1abee3c-57f9-4321-b0ed-0ebf34ac7b06 · outbound

This paper cites Kvasir-seg: A segmented polyp dataset.

LM-Net: A Light-weight and Multi-scale Network for Medical Image Segmentation Kvasir-seg: A segmented polyp dataset

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:49:00.071240Z

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 31576194-ca0f-4002-9c7f-cc1225a422a6 · outbound

This paper cites ESFPNet: efficient deep learning architecture for real-time lesion segmentation in autofluorescence bronchoscopic video.

LM-Net: A Light-weight and Multi-scale Network for Medical Image Segmentation ESFPNet: efficient deep learning architecture for real-time lesion segmentation in autofluorescence bronchoscopic video

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-10T21:48:59.384393Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b907d5c6-b3fd-4290-b816-c494552784b0 · outbound

This paper cites Stepwise feature fusion: Local guides global.

LM-Net: A Light-weight and Multi-scale Network for Medical Image Segmentation Stepwise feature fusion: Local guides global

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:49:00.056740Z

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 a12c3163-729d-4d55-b718-03003db77c4f · outbound

This paper cites Fcn-transformer feature fusion for polyp segmentation.

LM-Net: A Light-weight and Multi-scale Network for Medical Image Segmentation Fcn-transformer feature fusion for polyp segmentation

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:49:00.039883Z

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 38829f98-fb9a-4882-b15c-78503523adc9 · outbound

This paper cites Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm.

LM-Net: A Light-weight and Multi-scale Network for Medical Image Segmentation Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:49:00.018508Z

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 ecc01081-7410-4550-b20f-9911a628fce6 · outbound

This paper cites an unresolved cited work.

LM-Net: A Light-weight and Multi-scale Network for Medical Image Segmentation Unresolved cited work

Reference 31

Resolution: unresolved; raw_fallback, observed 2026-08-10T21:49:00.003800Z

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 b8408286-7dac-463d-a48d-24f9c6c1a866 · outbound

This paper cites Dataset of breast ultrasound images.

LM-Net: A Light-weight and Multi-scale Network for Medical Image Segmentation Dataset of breast ultrasound images

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-10T21:48:59.414036Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ce192107-e2b4-4dd0-8db4-c02bc8737c2d · outbound

This paper cites Fully convolutional networks for semantic segmentation.

LM-Net: A Light-weight and Multi-scale Network for Medical Image Segmentation Fully convolutional networks for semantic segmentation

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-10T21:48:59.418629Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e717f469-9e68-4ced-af1c-13315b86babb · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

LM-Net: A Light-weight and Multi-scale Network for Medical Image Segmentation Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-10T21:48:59.422938Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f7676e32-97c5-4197-8010-bd9b8bcb7e60 · outbound

This paper cites Efficientnet: Rethinking model scaling for convolutional neural networks.

LM-Net: A Light-weight and Multi-scale Network for Medical Image Segmentation Efficientnet: Rethinking model scaling for convolutional neural networks

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:48:59.974112Z

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 9695fdb9-beea-405f-aa91-4f8ec94ec1d9 · outbound

This paper cites Deep residual learning for image recognition.

LM-Net: A Light-weight and Multi-scale Network for Medical Image Segmentation Deep residual learning for image recognition

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-10T21:48:59.433209Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e95f3212-b082-44bf-9835-f3d5f05b5937 · outbound

This paper cites Deeplab: Semantic image segmentation with deep convolutional nets, atrous con- volution, and fully connected crfs.

LM-Net: A Light-weight and Multi-scale Network for Medical Image Segmentation Deeplab: Semantic image segmentation with deep convolutional nets, atrous con- volution, and fully connected crfs

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-10T21:48:59.437320Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 866e1460-0dcf-453f-b170-bde18ad647fd · outbound

This paper cites Rethinking Atrous Convolution for Semantic Image Segmentation.

LM-Net: A Light-weight and Multi-scale Network for Medical Image Segmentation Rethinking Atrous Convolution for Semantic Image Segmentation

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-10T21:48:59.441474Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0bc78a41-471a-4e94-b4f6-047ec21711e3 · outbound

This paper cites Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs.

LM-Net: A Light-weight and Multi-scale Network for Medical Image Segmentation Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-10T21:48:59.445847Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6a72c506-f447-41ba-b7ff-20c4b0824b1a · outbound

This paper cites Dual attention network for scene segmentation.

LM-Net: A Light-weight and Multi-scale Network for Medical Image Segmentation Dual attention network for scene segmentation

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:48:59.942120Z

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 1ea24c94-4068-4c5f-9d56-35a197dc83e7 · outbound

This paper cites Extrac- tion of vascular wall in carotid ultrasound via a novel boundary-delineation network.

LM-Net: A Light-weight and Multi-scale Network for Medical Image Segmentation Extrac- tion of vascular wall in carotid ultrasound via a novel boundary-delineation network

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:48:59.930011Z

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 a3e253f0-25dd-460f-b4dd-bcb0e8821602 · outbound

This paper cites Dense prediction and local fusion of superpixels: A framework for breast anatomy segmentation in ultrasound image with scarce data.

LM-Net: A Light-weight and Multi-scale Network for Medical Image Segmentation Dense prediction and local fusion of superpixels: A framework for breast anatomy segmentation in ultrasound image with scarce data

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:48:59.914650Z

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 89759324-ebb0-4c42-aa83-e6312975031b · outbound

This paper cites Segnet: A deep convo- lutional encoder-decoder architecture for image segmentation.

LM-Net: A Light-weight and Multi-scale Network for Medical Image Segmentation Segnet: A deep convo- lutional encoder-decoder architecture for image segmentation

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:48:59.897519Z

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 9f0d9e7a-e1ff-41e4-9f17-55b5bfd51ac1 · outbound

This paper cites Resunet-a: A deep learning framework for semantic segmentation of remotely sensed data.

LM-Net: A Light-weight and Multi-scale Network for Medical Image Segmentation Resunet-a: A deep learning framework for semantic segmentation of remotely sensed data

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:48:59.881656Z

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 8acb61fa-4ceb-4604-a012-7927256c8d07 · outbound

This paper cites A novel image-to-knowledge inference approach for automatically diagnosing tumors.

LM-Net: A Light-weight and Multi-scale Network for Medical Image Segmentation A novel image-to-knowledge inference approach for automatically diagnosing tumors

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:48:59.868597Z

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 a34fbb94-67e3-4156-8589-a58e53fb96e7 · outbound

This paper cites Attention is all you need.

LM-Net: A Light-weight and Multi-scale Network for Medical Image Segmentation Attention is all you need

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:48:59.854085Z

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 6732acee-4c55-4ab8-b200-cf41983bd2b4 · outbound

This paper cites Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers.

LM-Net: A Light-weight and Multi-scale Network for Medical Image Segmentation Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:48:59.840495Z

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 90e66b53-eea9-4ffb-8ca1-2cdb6ed2921d · outbound

This paper cites Vision transformers for dense prediction.

LM-Net: A Light-weight and Multi-scale Network for Medical Image Segmentation Vision transformers for dense prediction

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:48:59.827069Z

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 99e9d96b-5dce-4c06-80eb-dd572fa7da81 · outbound

This paper cites Imagenet: A large-scale hierarchical image database.

LM-Net: A Light-weight and Multi-scale Network for Medical Image Segmentation Imagenet: A large-scale hierarchical image database

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-10T21:48:59.485857Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation de345baa-199a-4d6d-b7c5-3c385f5e03c7 · outbound

This paper cites Batch normalization: Accelerating deep network training by reducing internal covariate shift.

LM-Net: A Light-weight and Multi-scale Network for Medical Image Segmentation Batch normalization: Accelerating deep network training by reducing internal covariate shift

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-10T21:48:59.489767Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7a54dbdb-85b5-4958-b9d6-6e35bb01deed · outbound

This paper cites Unified perceptual parsing for scene understanding.

LM-Net: A Light-weight and Multi-scale Network for Medical Image Segmentation Unified perceptual parsing for scene understanding

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:48:59.798234Z

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 8953a912-e859-4b18-bdff-6eac1fb41140 · outbound

This paper cites Feature pyramid networks for object detection.

LM-Net: A Light-weight and Multi-scale Network for Medical Image Segmentation Feature pyramid networks for object detection

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:48:59.785640Z

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 61664943-9223-40e0-a0fe-0f524c22ccca · outbound

This paper cites Path aggregation network for instance segmentation.

LM-Net: A Light-weight and Multi-scale Network for Medical Image Segmentation Path aggregation network for instance segmentation

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:48:59.772757Z

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 97503429-c2cd-40e7-9290-4fbb2f8129a4 · outbound

This paper cites Pyramid scene parsing network.

LM-Net: A Light-weight and Multi-scale Network for Medical Image Segmentation Pyramid scene parsing network

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-10T21:48:59.506865Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 94bae54c-f59b-46f7-ba14-60f1996bb673 · outbound

This paper cites Spatial pyramid pooling in deep convolutional networks for visual recognition.

LM-Net: A Light-weight and Multi-scale Network for Medical Image Segmentation Spatial pyramid pooling in deep convolutional networks for visual recognition

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:48:59.751242Z

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 d6cd13ef-a8c0-46d9-b209-ec73faf8a1f7 · outbound

This paper cites Neighborhood Attention Transformer.

LM-Net: A Light-weight and Multi-scale Network for Medical Image Segmentation Neighborhood Attention Transformer

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-10T21:48:59.515704Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1de835b6-3c49-4f11-8fe8-e4157868e567 · outbound

This paper cites Decoupled Weight Decay Regularization.

LM-Net: A Light-weight and Multi-scale Network for Medical Image Segmentation Decoupled Weight Decay Regularization

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-10T21:48:59.520935Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3f1488b5-257e-402f-84a4-5ea6acf9aae1 · outbound

This paper cites Class-balanced loss based on effective number of samples.

LM-Net: A Light-weight and Multi-scale Network for Medical Image Segmentation Class-balanced loss based on effective number of samples

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:48:59.736638Z

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 3b357d8e-4e3a-4b29-986b-76edcc3d3b1c · outbound

This paper cites Acnet: Strengthening the kernel skeletons for powerful cnn via asymmetric convolution blocks.

LM-Net: A Light-weight and Multi-scale Network for Medical Image Segmentation Acnet: Strengthening the kernel skeletons for powerful cnn via asymmetric convolution blocks

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:48:59.723016Z

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 16b8fb36-ef87-4890-8b38-ffb71f7991d5 · outbound

This paper cites Diverse branch block: Building a convolution as an inception-like unit.

LM-Net: A Light-weight and Multi-scale Network for Medical Image Segmentation Diverse branch block: Building a convolution as an inception-like unit

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:48:59.708827Z

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 729b1397-30b3-431d-8c82-e183175f5631 · outbound

This paper cites Escaping the Big Data Paradigm with Compact Transformers.

LM-Net: A Light-weight and Multi-scale Network for Medical Image Segmentation Escaping the Big Data Paradigm with Compact Transformers

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-10T21:48:59.537992Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Coverage vector

Reference resolution

Outbound references

Pith citing papers