Rethinking Decoder Design: Improving Biomarker Segmentation Using Depth-to-Space Restoration and Residual Linear Attention | Paper Citation Record

Observation 77264b33-cc0f-44f2-96e0-e655a5f02a7d · outbound

This paper cites Intuitive explanation of skip connections in deep learning.https://theaisummer.com/, 2020.

Rethinking Decoder Design: Improving Biomarker Segmentation Using Depth-to-Space Restoration and Residual Linear Attention Intuitive explanation of skip connections in deep learning.https://theaisummer.com/, 2020

Reference 1

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This paper cites Dan-nucnet: A dual attention based framework for nuclei segmentation in cancer histology images under wild clinical conditions.Expert Systems with Applications, 213:118945,.

Rethinking Decoder Design: Improving Biomarker Segmentation Using Depth-to-Space Restoration and Residual Linear Attention Dan-nucnet: A dual attention based framework for nuclei segmentation in cancer histology images under wild clinical conditions.Expert Systems with Applications, 213:118945,

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This paper cites Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation.

Rethinking Decoder Design: Improving Biomarker Segmentation Using Depth-to-Space Restoration and Residual Linear Attention Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation

Reference 3

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This paper cites Ma-unet: An improved ver- sion of unet based on multi-scale and attention mechanism for medical image segmentation.

Rethinking Decoder Design: Improving Biomarker Segmentation Using Depth-to-Space Restoration and Residual Linear Attention Ma-unet: An improved ver- sion of unet based on multi-scale and attention mechanism for medical image segmentation

Reference 4

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This paper cites Nu- cleus segmentation across imaging experiments: the 2018 data science bowl.Nature methods, 16(12):1247–1253,.

Rethinking Decoder Design: Improving Biomarker Segmentation Using Depth-to-Space Restoration and Residual Linear Attention Nu- cleus segmentation across imaging experiments: the 2018 data science bowl.Nature methods, 16(12):1247–1253,

Reference 5

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

Rethinking Decoder Design: Improving Biomarker Segmentation Using Depth-to-Space Restoration and Residual Linear Attention Swin-unet: Unet-like pure transformer for medical image segmentation

Reference 6

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Observation d0345ce0-3f8b-4cd0-9c76-79b29920c981 · outbound

This paper cites Dseu-net: A novel deep supervision seu-net for medical ultrasound image seg- mentation.Expert Systems with Applications, 223:119939,.

Rethinking Decoder Design: Improving Biomarker Segmentation Using Depth-to-Space Restoration and Residual Linear Attention Dseu-net: A novel deep supervision seu-net for medical ultrasound image seg- mentation.Expert Systems with Applications, 223:119939,

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Observation f90fa76f-f072-43b9-93d8-3018ac92930a · outbound

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

Rethinking Decoder Design: Improving Biomarker Segmentation Using Depth-to-Space Restoration and Residual Linear Attention TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

Reference 8

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Rethinking Decoder Design: Improving Biomarker Segmentation Using Depth-to-Space Restoration and Residual Linear Attention Unresolved cited work

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Observation f21e6825-c589-4b10-8883-f4eb16a92d3e · outbound

This paper cites UN-SAM: Universal Prompt-Free Segmentation for Generalized Nuclei Images.

Rethinking Decoder Design: Improving Biomarker Segmentation Using Depth-to-Space Restoration and Residual Linear Attention UN-SAM: Universal Prompt-Free Segmentation for Generalized Nuclei Images

Reference 10

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This paper cites Conditional Positional Encodings for Vision Transformers.

Rethinking Decoder Design: Improving Biomarker Segmentation Using Depth-to-Space Restoration and Residual Linear Attention Conditional Positional Encodings for Vision Transformers

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Observation 2c10aef2-7f20-4e88-8f72-6de5ca24cdba · outbound

This paper cites All-in-sam: from weak annota- tion to pixel-wise nuclei segmentation with prompt-based finetuning.

Rethinking Decoder Design: Improving Biomarker Segmentation Using Depth-to-Space Restoration and Residual Linear Attention All-in-sam: from weak annota- tion to pixel-wise nuclei segmentation with prompt-based finetuning

Reference 12

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This paper cites Polyp-PVT: Polyp Segmentation with Pyramid Vision Transformers.

Rethinking Decoder Design: Improving Biomarker Segmentation Using Depth-to-Space Restoration and Residual Linear Attention Polyp-PVT: Polyp Segmentation with Pyramid Vision Transformers

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This paper cites Pamsnet: A medical image segmentation network based on spatial pyramid and attention mecha- nism.Biomedical Signal Processing and Control, 94: 106285, 2024.

Rethinking Decoder Design: Improving Biomarker Segmentation Using Depth-to-Space Restoration and Residual Linear Attention Pamsnet: A medical image segmentation network based on spatial pyramid and attention mecha- nism.Biomedical Signal Processing and Control, 94: 106285, 2024

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Observation 423c039b-cfbe-44f8-a7f1-4fe8cebb0d65 · outbound

This paper cites An enhanced u-network by combining ppm and cbam for med- ical image segmentation.IEEE Access, 12:107098–107112,.

Rethinking Decoder Design: Improving Biomarker Segmentation Using Depth-to-Space Restoration and Residual Linear Attention An enhanced u-network by combining ppm and cbam for med- ical image segmentation.IEEE Access, 12:107098–107112,

Reference 15

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This paper cites Double encoder-decoder networks for gastroin- testinal polyp segmentation.

Rethinking Decoder Design: Improving Biomarker Segmentation Using Depth-to-Space Restoration and Residual Linear Attention Double encoder-decoder networks for gastroin- testinal polyp segmentation

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This paper cites Hover-net: Simultaneous segmentation and classi- fication of nuclei in multi-tissue histology images.Medical image analysis, 58:101563, 2019.

Rethinking Decoder Design: Improving Biomarker Segmentation Using Depth-to-Space Restoration and Residual Linear Attention Hover-net: Simultaneous segmentation and classi- fication of nuclei in multi-tissue histology images.Medical image analysis, 58:101563, 2019

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This paper cites Squeeze-and-excitation net- works.

Rethinking Decoder Design: Improving Biomarker Segmentation Using Depth-to-Space Restoration and Residual Linear Attention Squeeze-and-excitation net- works

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

Rethinking Decoder Design: Improving Biomarker Segmentation Using Depth-to-Space Restoration and Residual Linear Attention Unet 3+: A full-scale connected unet for medical image segmentation

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Observation 34cbb186-0519-45aa-bb15-3207a6c74bc9 · outbound

This paper cites nnu-net: a self-configuring method for deep learning-based biomedical image segmen- tation.Nature methods, 18(2):203–211, 2021.

Rethinking Decoder Design: Improving Biomarker Segmentation Using Depth-to-Space Restoration and Residual Linear Attention nnu-net: a self-configuring method for deep learning-based biomedical image segmen- tation.Nature methods, 18(2):203–211, 2021

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Observation 5c76614c-93d0-4825-8ed8-a1d41c693993 · outbound

This paper cites How Much Position Information Do Convolutional Neural Networks Encode?.

Rethinking Decoder Design: Improving Biomarker Segmentation Using Depth-to-Space Restoration and Residual Linear Attention How Much Position Information Do Convolutional Neural Networks Encode?

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Observation 7a9ddcb3-8162-4e2f-9914-a271580b6e46 · outbound

This paper cites A survey of loss functions for semantic seg- mentation.

Rethinking Decoder Design: Improving Biomarker Segmentation Using Depth-to-Space Restoration and Residual Linear Attention A survey of loss functions for semantic seg- mentation

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Observation 24b27f06-b7c7-4b98-b6bd-a4d5626483f8 · outbound

This paper cites AttResDU-Net: Medical Image Segmentation Using Attention-based Residual Double U-Net.

Rethinking Decoder Design: Improving Biomarker Segmentation Using Depth-to-Space Restoration and Residual Linear Attention AttResDU-Net: Medical Image Segmentation Using Attention-based Residual Double U-Net

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Observation 2fd14a96-e21f-45dc-80c3-617e11e3f9a9 · outbound

This paper cites Segment any- thing.

Rethinking Decoder Design: Improving Biomarker Segmentation Using Depth-to-Space Restoration and Residual Linear Attention Segment any- thing

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Observation 763ae0d8-cbfe-4a73-8477-faca84e9645f · outbound

This paper cites A dataset and a technique for generalized nuclear segmentation for computational pathology.IEEE transactions on medical imaging, 36(7):1550–1560, 2017.

Rethinking Decoder Design: Improving Biomarker Segmentation Using Depth-to-Space Restoration and Residual Linear Attention A dataset and a technique for generalized nuclear segmentation for computational pathology.IEEE transactions on medical imaging, 36(7):1550–1560, 2017

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This paper cites FusionU-Net: U- Net with enhanced skip connection for pathology image seg- mentation.

Rethinking Decoder Design: Improving Biomarker Segmentation Using Depth-to-Space Restoration and Residual Linear Attention FusionU-Net: U- Net with enhanced skip connection for pathology image seg- mentation

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Observation b8e72c94-5ef1-4a15-9c3f-474ca6cd9203 · outbound

This paper cites Rethinking Skip Connection with Layer Normalization in Transformers and ResNets.

Rethinking Decoder Design: Improving Biomarker Segmentation Using Depth-to-Space Restoration and Residual Linear Attention Rethinking Skip Connection with Layer Normalization in Transformers and ResNets

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This paper cites Fully convolutional networks for semantic segmentation.

Rethinking Decoder Design: Improving Biomarker Segmentation Using Depth-to-Space Restoration and Residual Linear Attention Fully convolutional networks for semantic segmentation

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This paper cites Learning for structured prediction using approximate subgradient descent with working sets.

Rethinking Decoder Design: Improving Biomarker Segmentation Using Depth-to-Space Restoration and Residual Linear Attention Learning for structured prediction using approximate subgradient descent with working sets

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Observation 1ed8a4c2-2a37-4ed7-a6e4-282531f930fd · outbound

This paper cites Segment anything in medical images.Nature Communications, 15(1):654, 2024.

Rethinking Decoder Design: Improving Biomarker Segmentation Using Depth-to-Space Restoration and Residual Linear Attention Segment anything in medical images.Nature Communications, 15(1):654, 2024

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This paper cites ALReLU: A different approach on Leaky ReLU activation function to improve Neural Networks Performance.

Rethinking Decoder Design: Improving Biomarker Segmentation Using Depth-to-Space Restoration and Residual Linear Attention ALReLU: A different approach on Leaky ReLU activation function to improve Neural Networks Performance

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Rethinking Decoder Design: Improving Biomarker Segmentation Using Depth-to-Space Restoration and Residual Linear Attention 3d mri brain tumor segmentation using autoencoder regularization

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This paper cites Segment Any Cell: A SAM-based Auto-prompting Fine-tuning Framework for Nuclei Segmentation.

Rethinking Decoder Design: Improving Biomarker Segmentation Using Depth-to-Space Restoration and Residual Linear Attention Segment Any Cell: A SAM-based Auto-prompting Fine-tuning Framework for Nuclei Segmentation

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This paper cites In- stasam: Instance-aware segment any nuclei model with point annotations.

Rethinking Decoder Design: Improving Biomarker Segmentation Using Depth-to-Space Restoration and Residual Linear Attention In- stasam: Instance-aware segment any nuclei model with point annotations

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This paper cites Segmentation of nuclei in histopathology images by deep re- gression of the distance map.IEEE transactions on medical imaging, 38(2):448–459, 2018.

Rethinking Decoder Design: Improving Biomarker Segmentation Using Depth-to-Space Restoration and Residual Linear Attention Segmentation of nuclei in histopathology images by deep re- gression of the distance map.IEEE transactions on medical imaging, 38(2):448–459, 2018

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Rethinking Decoder Design: Improving Biomarker Segmentation Using Depth-to-Space Restoration and Residual Linear Attention Raunet: Residual attention u-net for semantic segmentation of cataract surgical instruments

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

Rethinking Decoder Design: Improving Biomarker Segmentation Using Depth-to-Space Restoration and Residual Linear Attention Attention U-Net: Learning Where to Look for the Pancreas

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Rethinking Decoder Design: Improving Biomarker Segmentation Using Depth-to-Space Restoration and Residual Linear Attention U2-net: Go- ing deeper with nested u-structure for salient object detec- tion.Pattern recognition, 106:107404, 2020

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This paper cites Rahman, S.

Rethinking Decoder Design: Improving Biomarker Segmentation Using Depth-to-Space Restoration and Residual Linear Attention Rahman, S

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This paper cites Medical im- age segmentation via cascaded attention decoding.

Rethinking Decoder Design: Improving Biomarker Segmentation Using Depth-to-Space Restoration and Residual Linear Attention Medical im- age segmentation via cascaded attention decoding

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This paper cites Emcad: Efficient multi-scale convolutional atten- tion decoding for medical image segmentation.

Rethinking Decoder Design: Improving Biomarker Segmentation Using Depth-to-Space Restoration and Residual Linear Attention Emcad: Efficient multi-scale convolutional atten- tion decoding for medical image segmentation

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This paper cites Mist: Medical image segmentation trans- former with convolutional attention mixing (cam) decoder.

Rethinking Decoder Design: Improving Biomarker Segmentation Using Depth-to-Space Restoration and Residual Linear Attention Mist: Medical image segmentation trans- former with convolutional attention mixing (cam) decoder

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This paper cites Searching for Activation Functions.

Rethinking Decoder Design: Improving Biomarker Segmentation Using Depth-to-Space Restoration and Residual Linear Attention Searching for Activation Functions

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Rethinking Decoder Design: Improving Biomarker Segmentation Using Depth-to-Space Restoration and Residual Linear Attention U- net: Convolutional networks for biomedical image segmen- tation

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This paper cites Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network.

Rethinking Decoder Design: Improving Biomarker Segmentation Using Depth-to-Space Restoration and Residual Linear Attention Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network

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Observation b3a9e042-1abd-4e22-bb71-dc5a0b708cc0 · outbound

This paper cites Unleashing the power of prompt-driven nu- cleus instance segmentation.

Rethinking Decoder Design: Improving Biomarker Segmentation Using Depth-to-Space Restoration and Residual Linear Attention Unleashing the power of prompt-driven nu- cleus instance segmentation

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:54:18.535016Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 8240b9ae-12ef-47ca-8972-4c866433ffdc · outbound

This paper cites Ddanet: Dual decoder attention network for auto- matic polyp segmentation.

Rethinking Decoder Design: Improving Biomarker Segmentation Using Depth-to-Space Restoration and Residual Linear Attention Ddanet: Dual decoder attention network for auto- matic polyp segmentation

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:54:18.522695Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 26f631a9-9abf-4904-81f8-86424a2ec35f · outbound

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

Rethinking Decoder Design: Improving Biomarker Segmentation Using Depth-to-Space Restoration and Residual Linear Attention Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:54:18.510470Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 5bb5589f-fcc5-45f0-9f87-37952ea659ff · outbound

This paper cites U-Net Using Stacked Dilated Convolutions for Medical Image Segmentation.

Rethinking Decoder Design: Improving Biomarker Segmentation Using Depth-to-Space Restoration and Residual Linear Attention U-Net Using Stacked Dilated Convolutions for Medical Image Segmentation

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-15T18:54:18.117218Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation db8b4826-9c66-4fb2-bc2b-0b4f90eaef34 · outbound

This paper cites Uformer: A general u-shaped transformer for image restoration.

Rethinking Decoder Design: Improving Biomarker Segmentation Using Depth-to-Space Restoration and Residual Linear Attention Uformer: A general u-shaped transformer for image restoration

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-15T18:54:18.121577Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f753295e-8bb1-4fa6-ab17-4ffbc0409746 · outbound

This paper cites Histoseg: Quick attention with multi-loss function for multi-structure seg- mentation in digital histology images.

Rethinking Decoder Design: Improving Biomarker Segmentation Using Depth-to-Space Restoration and Residual Linear Attention Histoseg: Quick attention with multi-loss function for multi-structure seg- mentation in digital histology images

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:54:18.491493Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation c183a13e-c078-4ebc-8fd9-4fef780c4a5f · outbound

This paper cites Cbam: Convolutional block attention module.

Rethinking Decoder Design: Improving Biomarker Segmentation Using Depth-to-Space Restoration and Residual Linear Attention Cbam: Convolutional block attention module

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-15T18:54:18.129283Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4900a551-4de4-49d9-b3fc-965f0e71883d · outbound

This paper cites Segformer: Simple and efficient design for semantic segmentation with transform- ers.Advances in neural information processing systems, 34: 12077–12090, 2021.

Rethinking Decoder Design: Improving Biomarker Segmentation Using Depth-to-Space Restoration and Residual Linear Attention Segformer: Simple and efficient design for semantic segmentation with transform- ers.Advances in neural information processing systems, 34: 12077–12090, 2021

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:54:18.471924Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b75199fa-c479-4246-8788-d8d9d542aa4d · outbound

This paper cites Sea-net: medical image segmentation network based on spi- ral squeeze-and-excitation and attention modules.BMC Medical Imaging, 24(1):17, 2024.

Rethinking Decoder Design: Improving Biomarker Segmentation Using Depth-to-Space Restoration and Residual Linear Attention Sea-net: medical image segmentation network based on spi- ral squeeze-and-excitation and attention modules.BMC Medical Imaging, 24(1):17, 2024

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:54:18.459890Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 28d5b2d0-d578-4a56-ac02-810c354b4c58 · outbound

This paper cites A medical image segmentation method based on improved unet 3+ network.Diagnostics, 13(3):576, 2023.

Rethinking Decoder Design: Improving Biomarker Segmentation Using Depth-to-Space Restoration and Residual Linear Attention A medical image segmentation method based on improved unet 3+ network.Diagnostics, 13(3):576, 2023

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:54:18.448116Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation a4b6e8f4-0bd9-40b4-8b7c-fd3852e5035a · outbound

This paper cites Deep layer aggregation.

Rethinking Decoder Design: Improving Biomarker Segmentation Using Depth-to-Space Restoration and Residual Linear Attention Deep layer aggregation

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:54:18.436132Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation a46460ae-19cf-4bc8-bb83-484cd47fbc08 · outbound

This paper cites mu-net: Medical image segmentation using efficient and effective deep supervision.

Rethinking Decoder Design: Improving Biomarker Segmentation Using Depth-to-Space Restoration and Residual Linear Attention mu-net: Medical image segmentation using efficient and effective deep supervision

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:54:18.423100Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation c469eb69-886a-4e1e-993b-f057e05e6c16 · outbound

This paper cites Fsa-net: Re- thinking the attention mechanisms in medical image segmen- tation from releasing global suppressed information.Com- puters in Biology and Medicine, 161:106932, 2023.

Rethinking Decoder Design: Improving Biomarker Segmentation Using Depth-to-Space Restoration and Residual Linear Attention Fsa-net: Re- thinking the attention mechanisms in medical image segmen- tation from releasing global suppressed information.Com- puters in Biology and Medicine, 161:106932, 2023

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:54:18.411268Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 9faf4b3e-f9ce-4360-92a2-6acefc400a30 · outbound

This paper cites Amulet: Aggregating multi-level convolu- tional features for salient object detection.

Rethinking Decoder Design: Improving Biomarker Segmentation Using Depth-to-Space Restoration and Residual Linear Attention Amulet: Aggregating multi-level convolu- tional features for salient object detection

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:54:18.398689Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation fad35b5f-2a0d-400d-8800-1ffeeb45646d · outbound

This paper cites Scau-net: spatial-channel attention u-net for gland segmen- tation.Frontiers in Bioengineering and Biotechnology, 8: 670, 2020.

Rethinking Decoder Design: Improving Biomarker Segmentation Using Depth-to-Space Restoration and Residual Linear Attention Scau-net: spatial-channel attention u-net for gland segmen- tation.Frontiers in Bioengineering and Biotechnology, 8: 670, 2020

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:54:18.386349Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation f34798cb-7685-47c1-8d17-70631b11e8a6 · outbound

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

Rethinking Decoder Design: Improving Biomarker Segmentation Using Depth-to-Space Restoration and Residual Linear Attention Unet++: A nested u-net ar- chitecture for medical image segmentation

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:54:18.374770Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 111d6f5a-17b1-42a6-a43d-32d7a28eabb0 · outbound

This paper cites an unresolved cited work.

Rethinking Decoder Design: Improving Biomarker Segmentation Using Depth-to-Space Restoration and Residual Linear Attention Unresolved cited work

Reference 242

Resolution: parse uncertain; no resolver link, observed 2026-08-15T18:54:18.056082Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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