Paper Citation Record · LEDGER

A Study on Context Length and Efficient Transformers for Biomedical Image Analysis

As of 15 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2501.00619.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2501.00619 v1

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T22:50:08.542917Z

measured 35 of 35 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

35 of 35 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b9304087-782a-4764-9267-9c73cc38bfb2 · outbound

This paper cites Generating Long Sequences with Sparse Transformers.

A Study on Context Length and Efficient Transformers for Biomedical Image Analysis Generating Long Sequences with Sparse Transformers

Reference 4

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Observation 1fec1c17-3cad-45f4-87ef-e55108553192 · outbound

This paper cites We required a minimum batch size of two to fit on the GPU to enable batch normalization layers.

A Study on Context Length and Efficient Transformers for Biomedical Image Analysis We required a minimum batch size of two to fit on the GPU to enable batch normalization layers

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:50:09.069513Z

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Observation d8b94dcb-23ca-470f-8d5f-0cf630fa5ef4 · outbound

This paper cites A Survey on Long Text Modeling with Transformers.

A Study on Context Length and Efficient Transformers for Biomedical Image Analysis A Survey on Long Text Modeling with Transformers

Reference 7

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Observation 97d6fd31-687e-44dc-aa02-be1ba40c6305 · outbound

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

A Study on Context Length and Efficient Transformers for Biomedical Image Analysis An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 8

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Observation 96050f84-8820-4cb1-b9d1-fdb565146b9e · outbound

This paper cites Hungry Hungry Hippos: Towards Language Modeling with State Space Models.

A Study on Context Length and Efficient Transformers for Biomedical Image Analysis Hungry Hungry Hippos: Towards Language Modeling with State Space Models

Reference 9

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Observation 48e4bbbe-0600-4594-94e3-0dbd0cbf8451 · outbound

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

A Study on Context Length and Efficient Transformers for Biomedical Image Analysis Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 10

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Observation 2cfe7152-2440-4913-b4f5-79f5ae08f6d4 · outbound

This paper cites Efficiently Modeling Long Sequences with Structured State Spaces.

A Study on Context Length and Efficient Transformers for Biomedical Image Analysis Efficiently Modeling Long Sequences with Structured State Spaces

Reference 11

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Observation c5717a34-3ce5-4ae5-956c-7098d5cbc47b · outbound

This paper cites MambaVision: A Hybrid Mamba-Transformer Vision Backbone.

A Study on Context Length and Efficient Transformers for Biomedical Image Analysis MambaVision: A Hybrid Mamba-Transformer Vision Backbone

Reference 12

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Observation 133006af-db2d-43ac-9394-575e2d16eab3 · outbound

This paper cites Exploring Long-Sequence Masked Autoencoders.

A Study on Context Length and Efficient Transformers for Biomedical Image Analysis Exploring Long-Sequence Masked Autoencoders

Reference 13

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Observation 33dc615e-6f13-419d-84ca-ab50f77ca43b · outbound

This paper cites Advancing Transformer Architecture in Long-Context Large Language Models: A Comprehensive Survey.

A Study on Context Length and Efficient Transformers for Biomedical Image Analysis Advancing Transformer Architecture in Long-Context Large Language Models: A Comprehensive Survey

Reference 14

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Observation c71813ef-d084-4f9b-a0b3-c2742c18f3ea · outbound

This paper cites Optimizing vision transformer per- formance with customizable parameters.

A Study on Context Length and Efficient Transformers for Biomedical Image Analysis Optimizing vision transformer per- formance with customizable parameters

Reference 15

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Observation 3cc94fa8-9542-48c4-b02d-c16957be701c · outbound

This paper cites U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation.

A Study on Context Length and Efficient Transformers for Biomedical Image Analysis U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation

Reference 16

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Observation 6f7a34b4-0985-4588-8253-f4b5ce189b83 · outbound

This paper cites MM1: Methods, Analysis & Insights from Multimodal LLM Pre-training.

A Study on Context Length and Efficient Transformers for Biomedical Image Analysis MM1: Methods, Analysis & Insights from Multimodal LLM Pre-training

Reference 17

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Observation 939c4584-e4f0-4317-8a4b-07b74f717717 · outbound

This paper cites DeepStack: Deeply Stacking Visual Tokens is Surprisingly Simple and Effective for LMMs.

A Study on Context Length and Efficient Transformers for Biomedical Image Analysis DeepStack: Deeply Stacking Visual Tokens is Surprisingly Simple and Effective for LMMs

Reference 18

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Observation 816a65ae-414f-4513-a9e2-53330d700eaf · outbound

This paper cites An Image is Worth More Than 16x16 Patches: Exploring Transformers on Individual Pixels.

A Study on Context Length and Efficient Transformers for Biomedical Image Analysis An Image is Worth More Than 16x16 Patches: Exploring Transformers on Individual Pixels

Reference 19

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Observation 02be7d49-63bf-489d-b418-9e0a015589b0 · outbound

This paper cites The What, Why, and How of Context Length Extension Techniques in Large Language Models -- A Detailed Survey.

A Study on Context Length and Efficient Transformers for Biomedical Image Analysis The What, Why, and How of Context Length Extension Techniques in Large Language Models -- A Detailed Survey

Reference 20

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Observation ce5a78ab-2418-4897-afd8-c61906f2784e · outbound

This paper cites EfficientVMamba: Atrous Selective Scan for Light Weight Visual Mamba.

A Study on Context Length and Efficient Transformers for Biomedical Image Analysis EfficientVMamba: Atrous Selective Scan for Light Weight Visual Mamba

Reference 21

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Observation c32aa6d6-1390-4e12-9d83-1af59b905af6 · outbound

This paper cites RWKV: Reinventing RNNs for the Transformer Era.

A Study on Context Length and Efficient Transformers for Biomedical Image Analysis RWKV: Reinventing RNNs for the Transformer Era

Reference 22

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Observation d3200488-a2d6-42e1-afd3-297cf350bfa1 · outbound

This paper cites Retentive Network: A Successor to Transformer for Large Language Models.

A Study on Context Length and Efficient Transformers for Biomedical Image Analysis Retentive Network: A Successor to Transformer for Large Language Models

Reference 24

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Observation f68abc87-e4da-4241-82bb-feb1d3ed6ba3 · outbound

This paper cites Preliminary study on patch sizes in vision transformers (vit) for covid-19 and diseased lungs classification.

A Study on Context Length and Efficient Transformers for Biomedical Image Analysis Preliminary study on patch sizes in vision transformers (vit) for covid-19 and diseased lungs classification

Reference 26

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Observation de579f3c-f5d1-46e7-abee-25f2f5508964 · outbound

This paper cites Attention Is All You Need.

A Study on Context Length and Efficient Transformers for Biomedical Image Analysis Attention Is All You Need

Reference 27

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Observation a5063579-ce1c-449a-928d-fe044fd165f8 · outbound

This paper cites Mamba-UNet: UNet-Like Pure Visual Mamba for Medical Image Segmentation.

A Study on Context Length and Efficient Transformers for Biomedical Image Analysis Mamba-UNet: UNet-Like Pure Visual Mamba for Medical Image Segmentation

Reference 28

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Observation f65213f4-174d-4d55-b463-e59628ddd2b8 · outbound

This paper cites SegMamba: Long-range Sequential Modeling Mamba For 3D Medical Image Segmentation.

A Study on Context Length and Efficient Transformers for Biomedical Image Analysis SegMamba: Long-range Sequential Modeling Mamba For 3D Medical Image Segmentation

Reference 29

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Observation 5869ef7d-c8d3-4e7d-b767-0ea904e7e55e · outbound

This paper cites W2s: microscopy data with joint denoising and super- resolution for widefield to sim mapping.

A Study on Context Length and Efficient Transformers for Biomedical Image Analysis W2s: microscopy data with joint denoising and super- resolution for widefield to sim mapping

Reference 30

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Observation de05a0e6-8518-4118-9529-dd6c1a2198fe · outbound

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

A Study on Context Length and Efficient Transformers for Biomedical Image Analysis Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model

Reference 31

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This paper cites 95% confidence intervals are reported in parentheses, com- puted by bootstrapping over the test set.

A Study on Context Length and Efficient Transformers for Biomedical Image Analysis 95% confidence intervals are reported in parentheses, com- puted by bootstrapping over the test set

Reference 35

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Observation 2adc29fb-25a1-4fe8-9687-8664f307795b · outbound

This paper cites For the microscopy denoising dataset (Zhou et al., 2020), we treated each of the three supplied channels in the public dataset as different images.

A Study on Context Length and Efficient Transformers for Biomedical Image Analysis For the microscopy denoising dataset (Zhou et al., 2020), we treated each of the three supplied channels in the public dataset as different images

Reference 400

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A Study on Context Length and Efficient Transformers for Biomedical Image Analysis Unresolved cited work

Reference 700

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This paper cites Efficient Transformers: A Survey.

A Study on Context Length and Efficient Transformers for Biomedical Image Analysis Efficient Transformers: A Survey

Reference 2017

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This paper cites Rethinking Attention with Performers.

A Study on Context Length and Efficient Transformers for Biomedical Image Analysis Rethinking Attention with Performers

Reference 2019

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This paper cites FlashAttention-3: Fast and Accurate Attention with Asynchrony and Low-precision.

A Study on Context Length and Efficient Transformers for Biomedical Image Analysis FlashAttention-3: Fast and Accurate Attention with Asynchrony and Low-precision

Reference 2020

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A Study on Context Length and Efficient Transformers for Biomedical Image Analysis FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning

Reference 2021

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A Study on Context Length and Efficient Transformers for Biomedical Image Analysis ViM-UNet: Vision Mamba for Biomedical Segmentation

Reference 2022

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A Study on Context Length and Efficient Transformers for Biomedical Image Analysis Longformer: The Long-Document Transformer

Reference 2023

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This paper cites Zoology: Measuring and Improving Recall in Efficient Language Models.

A Study on Context Length and Efficient Transformers for Biomedical Image Analysis Zoology: Measuring and Improving Recall in Efficient Language Models

Reference 2024

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Pith citing papers

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