Pith. sign in

REVIEW 3 major objections 4 minor 39 references

Enhancing Fluorescence Lifetime Parameter Estimation Accuracy with Differential Transformer Based Deep Learning Model Incorporating Pixelwise Instrument Response Function

T0 review · 3 major / 4 minor · reviewed 2026-08-12 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Feeding each pixel's instrument-response function alongside the photon histogram into a differential-transformer encoder-decoder yields fluorescence lifetime estimates that stay accurate when the sample surface moves through the imaging…

desk verdict A real engineering advance for real-time FLI, but the headline claim about robustness to surface-height IRF shifts is not actually demonstrated by the training data or the validation. read the letter →

arxiv 2411.16896 v2 pith:2F2VKDPN submitted 2024-11-25 eess.IV cs.AIcs.LGphysics.optics

Ismail Erbas , Vikas Pandey , Navid Ibtehaj Nizam , Nanxue Yuan , Amit Verma , Margarida Barosso , Xavier Intes This is my paper · ORCID
classification eess.IVcs.AIcs.LGphysics.optics
keywords fluorescencelifetimeimagingmacroscopicFLIinstrumentresponsefunctiondifferentialtransformerdeeplearningtime-resolvedIRFoffsetin-vivo
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper introduces MFliNet, a deep-learning model for macroscopic fluorescence lifetime imaging that takes the pixelwise instrument response function (IRF) as an explicit second input beside the measured photon time-of-arrival histogram. The claim is that this extra input, combined with a differential-attention transformer, lets the network absorb the temporal offsets caused by surface topography, so lifetime estimates do not drift when the sample is not flat. The paper supports this with a five-step ladder phantom whose heights impose IRF shifts up to 160 ps, where MFliNet stays within about 0.04 ns of nonlinear least-squares fitting while a standard DL baseline and offset-free fitting drift with height. In two HER2+ tumor xenografts, MFliNet reproduces the fitted short and long lifetimes to within a few hundredths of a nanosecond while processing 90,480 pixels in 63 seconds versus roughly 6 hours for the fitter. If correct, this removes a known bias that has limited deep-learning FLI to planar samples and opens the method to in-vivo and surgical imaging.

What carries the argument

The load-bearing mechanism is the differential attention layer, defined as DiffAttn(X) = softmax(Q1K1^T/$\sqrt$(dk))V1 − λ·softmax(Q2K2^T/$\sqrt$(dk))V2, where λ is a learnable scalar. Subtracting two attention maps cancels the noise floor and sharpens attention onto the informative features of the input pair, which in this application are the early-arrival variations that encode the IRF offset. The encoder-decoder processes the TPSF and pixelwise IRF as an input pair, and three parallel output heads regress the short lifetime, long lifetime, and fractional amplitude.

What would settle it

Check the training dataset for IRFs with the 40–160 ps offsets observed in the phantom; then measure MFliNet's lifetime error on a phantom placed at heights whose IRF shift lies outside that range or whose IRF shape changes (not just shifts). If the error grows with unseen offsets or with IRF shape changes, the central robustness claim is limited to interpolation over the training IRF distribution.

Watch

Extended reading notes

Core claim

MFliNet's central discovery is that making the IRF a per-pixel model input, rather than a fixed global correction, de-biases deep-learning lifetime estimation under realistic depth-of-field variations. The model uses a differential attention mechanism that computes the difference between two softmax attention maps, scaled by a learned parameter, to concentrate on the parts of the TPSF–IRF pair that carry lifetime information and suppress noise. On the step-ladder phantom, MFliNet matches NLSF across all five heights, whereas FLI-Net errors grow with height and NLSF without offset correction systematically underestimates. In vivo, its short-lifetime estimates (0.52 ± 0.05 ns and 0.58 ± 0.04 ns for two HCC1954 xenografts) and long-lifetime estimates (1.19 ± 0.02 ns and 1.18 ± 0.03 ns) align with NLSF values to within measurement spread.

Load-bearing premise

The training IRFs were captured from a flat white diffuser on the imaging table, and the paper does not state whether height-shifted IRFs (40–160 ps) were included in training, so the model's robustness to those shifts may rest on an untested generalization rather than on having learned to handle them.

Editorial extensions

If this is right

  • Macroscopic FLI can be run in real time: 90,480 pixels analyzed in 63 seconds on a single dataset, versus roughly 6 hours for NLSF.
  • The model removes the need for manual offset correction in NLSF-style analysis, since the IRF input accounts for time-of-arrival shifts per pixel.
  • FLI becomes usable on non-planar samples such as whole animals and surgical fields, where surface height varies by centimeters.
  • The differential attention mechanism is the component that gives the gain: a same-architecture standard transformer trained on the same data performs worse across heights.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The paper does not state whether the training set included IRFs shifted by 40–160 ps like those seen in the phantom; if it did not, the demonstrated height invariance is an untested generalization rather than a learned behavior, and the robustness claim would need a dedicated generalization test.
  • The same architecture could be applied to other deconvolution problems where a per-pixel or per-channel instrument response is known, such as time-correlated single-photon counting arrays or pulse oximetry, since the differential attention is not specific to fluorescence.
  • A direct test of the mechanism would be to ablate the IRF input while keeping the differential transformer: the paper compares against a standard transformer with IRF input, but an ablation without IRF input would separate the benefit of the extra input from the benefit of differential attention.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 4 minor

Summary. The paper proposes MFliNet, a deep learning architecture that estimates fluorescence lifetime parameters (short lifetime, long lifetime, fractional amplitude) from a temporal point spread function (TPSF) and a pixelwise instrument response function (IRF) input. The model uses a differential transformer encoder–decoder with three output branches, trained on synthetic data generated from the bi-exponential convolution model of Eq. (1), where experimental IRFs were captured from a flat white diffuser paper placed on the imaging table. The authors validate MFliNet on a step-ladder phantom that introduces height-dependent IRF offsets of 40–160 ps and on HER2+ tumor xenografts in mice, comparing against NLSF (AlliGator), FLI-Net, and a standard transformer of the same architecture. The paper claims that MFliNet matches NLSF accuracy while being much faster, and that it avoids the height-dependent bias seen in FLI-Net and offset-free NLSF.

Significance. If the results hold, MFliNet would be a timely contribution: it is, to my knowledge, the first macroscopic FLI network that explicitly ingests the pixelwise IRF, and the differential-transformer design is a plausible way to focus on informative temporal features. The inclusion of a stepped-phantom experiment and an in-vivo xenograft comparison is commendable, and the reported speed advantage over NLSF is substantial. However, the central accuracy claim is not yet established: the reference method (NLSF) shares the same bi-exponential convolution model used to generate the training data, so agreement between MFliNet and NLSF demonstrates consistency rather than absolute accuracy; and phantom MFliNet values are not reported numerically. The paper would be significantly stronger with an independent lifetime standard, quantitative phantom values with statistical comparisons, and a demonstration that the training IRF distribution actually contains the height- and anatomy-dependent offsets that the model is claimed to handle.

major comments (3)
  1. [Sec. 2.2 (training data generation)] The training IRFs are described as captured from a flat white diffuser paper placed on the imaging table and convolved with simulated decays; the paper does not state that height-induced time-of-flight shifts (the 40–160 ps offsets reported in Sec. 3) were included in the training set. If these offsets are absent from the training distribution, then the phantom and in-vivo results in Sec. 3 demonstrate an untested generalization rather than a learned offset-invariance. Because the pixelwise IRF input is the paper's main novelty, this gap is load-bearing. Please clarify whether the training data included IRFs spanning the observed offset range, or, if not, provide a controlled experiment (e.g., test-time IRFs with known shifts) showing that the model generalizes to out-of-distribution offsets.
  2. [Sec. 3 (phantom results)] The text states that MFliNet results were 'within the same range as NLSF' but reports no numerical mean±SD values for MFliNet at any of the five heights. Without these numbers, the claim that MFliNet avoids the height-dependent bias visible in FLI-Net and offset-free NLSF cannot be verified quantitatively from the manuscript. Please report the per-height means and standard deviations for all methods and include a statistical test (e.g., ANOVA or paired comparisons) for height-dependence.
  3. [Sec. 3 and Eq. (1)] The evaluation reference (NLSF with AlliGator) and the training data are both based on the same bi-exponential convolution model of Eq. (1). Consequently, agreement between MFliNet and NLSF establishes that the network reproduces the NLSF fit under the same model assumptions, but not that the estimated lifetimes are accurate in an absolute sense. No independent lifetime standard (e.g., a fluorophore with a known lifetime or a physical calibration target) is used. Please add such a validation, or explicitly discuss the limitation that the reported accuracy is relative to the NLSF model.
minor comments (4)
  1. [Sec. 2.2] The sentence 'the data was generated using the MNIST dataset' is vague; please clarify how MNIST images determine the spatial distribution of the three lifetime parameters (τ1, τ2, AR) across the 28×28 pixel frame.
  2. [Eq. (2)] The differential attention formula, as written, computes a difference of two softmax-weighted value sums: softmax(Q1K1^T/√dk)V1 − λ·softmax(Q2K2^T/√dk)V2. Please verify that this matches the definition in Ref. [33], or state explicitly any adaptation made for the FLI setting.
  3. [Fig. 3 caption] The caption says 'Violin plots of NLSF analysis, FLI-Net, transformer model and MFliNet for all outputs,' but the figure panels appear to show lifetime maps and violin plots for the amplitude-weighted mean lifetime. Please make the caption more specific about which output (τM, τ1, τ2, or AR) is displayed.
  4. [Sec. 3] The sentence about processing speed reports that NLSF took 6 hours for 598 pixels 'covering only the tumor area' while MFliNet processed 90,480 pixels; since this appears in the phantom section, please clarify whether the NLSF analysis was restricted to the phantom fluorescence embeddings or to the in-vivo tumor region.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: MFliNet is a learned inverse mapping from (TPSF, IRF) inputs to lifetime parameters, not a derivation that reduces to its own inputs.

full rationale

MFliNet is an empirical deep-learning model; its outputs are a learned inverse mapping from (TPSF, IRF) pairs to lifetime parameters, not a quantity reconstructed from the same parameters by definition. Training labels are generated from the forward convolution model in Eq. 1, but this is standard supervised learning with synthetic ground truth; the model is not given the labels as inputs, so its predictions are not equivalent to its inputs by construction. The main evaluation reference, NLSF/AlliGator, also uses Eq. 1, so agreement between MFliNet and NLSF is a consistency check within the same decay-model family rather than validation against an independent physical ground truth; this is a validation limitation, not a circular derivation. The IRF-offset robustness claim depends on whether the training IRF library (flat diffuser on the imaging table, Sec. 2.2) covers the height/anatomy offsets shown in Fig. 2, but that is an untested generalization risk, not circularity. Self-citations such as refs. [22] and [34] supply background and instrumentation details and are not load-bearing uniqueness claims. No fitted parameter is renamed as a prediction, no result is forced by a self-citation chain, and no known result is merely renamed. Therefore no significant circularity is present.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The central contribution is a learned inverse mapping, so the network weights are the main fitted quantities. The predictive claim also depends on the bi-exponential convolution model, the representativeness of the flat-diffuser IRF set, the imported noise model, and the use of NLSF as an accuracy proxy. No new physical entities are postulated.

free parameters (3)
  • All learned weights of MFliNet = not reported (architecture sizes omitted)
    The model's outputs are direct functions of these fitted weights; training used 2000 samples of 28x28 images with MSE loss on the three output branches.
  • Lifetime and amplitude simulation ranges = tau1 in 0.2-0.8 ns, tau2 in 0.8-1.5 ns, AR in 0-100 percent
    These hand-chosen ranges bound the training distribution and therefore define the validity domain of the estimator.
  • Noise model constants from reference [34] = not restated
    Synthetic training data inherit read-out and dark noise statistics from the cited prior system characterization; no re-estimation is reported.
assumptions (4)
  • domain assumption The measured TPSF is the convolution of a bi-exponential fluorescence decay with the IRF (Eq. 1).
    This forward model generates all training data and is also the model fitted by the NLSF reference. Any non-exponential decay or unmodeled IRF distortion biases both the network and the benchmark.
  • domain assumption IRFs captured from a flat white diffuser paper at one imaging plane are representative of pixelwise IRF variability across phantom heights and in-vivo anatomy.
    Training TPSFs are produced by randomly selecting IRFs from this dataset (Sec. 2.2), while the phantom and mouse IRF shifts in Fig. 2 may include offsets not present in that training set.
  • domain assumption The synthetic noise model from reference [34] accurately reproduces the noise of the current imaging system.
    This noise is added to training TPSFs; if the real noise statistics differ, the learned mapping may not transfer to experimental data.
  • domain assumption NLSF/AlliGator estimates provide a valid ground truth for experimental lifetime parameters.
    NLSF is the reference for phantom and in-vivo comparisons, but it is itself subject to model mismatch, initialization, and offset-correction choices.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Enhancing Fluorescence Lifetime Parameter Estimation Accuracy with Differential Transformer Based Deep Learning Model Incorporating Pixelwise Instrument Response Function." pith.science (2026). https://pith.science/paper/2F2VKDPN

@misc{pith2026241116896,
  author       = {Pith},
  title        = {Pith review of: Enhancing Fluorescence Lifetime Parameter Estimation Accuracy with Differential Transformer Based Deep Learning Model Incorporating Pixelwise Instrument Response Function},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2F2VKDPN}},
  note         = {Machine review of arXiv:2411.16896}
}
read the original abstract

Fluorescence Lifetime Imaging (FLI) is a critical molecular imaging modality that provides unique information about the tissue microenvironment, which is invaluable for biomedical applications. FLI operates by acquiring and analyzing photon time-of-arrival histograms to extract quantitative parameters associated with temporal fluorescence decay. These histograms are influenced by the intrinsic properties of the fluorophore, instrument parameters, time-of-flight distributions associated with pixel-wise variations in the topographic and optical characteristics of the sample. Recent advancements in Deep Learning (DL) have enabled improved fluorescence lifetime parameter estimation. However, existing models are primarily designed for planar surface samples, limiting their applicability in translational scenarios involving complex surface profiles, such as \textit{in-vivo} whole-animal or imaged guided surgical applications. To address this limitation, we present MFliNet (Macroscopic FLI Network), a novel DL architecture that integrates the Instrument Response Function (IRF) as an additional input alongside experimental photon time-of-arrival histograms. Leveraging the capabilities of a Differential Transformer encoder-decoder architecture, MFliNet effectively focuses on critical input features, such as variations in photon time-of-arrival distributions. We evaluate MFliNet using rigorously designed tissue-mimicking phantoms and preclinical in-vivo cancer xenograft models. Our results demonstrate the model's robustness and suitability for complex macroscopic FLI applications, offering new opportunities for advanced biomedical imaging in diverse and challenging settings.

Figures

Figures reproduced from arXiv: 2411.16896 by the authors.

Figure 1
Figure 1. Proposed transformer-based deep learning network architecture [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Illustration of designed 3D step ladder phantom and the IRF shifts as a result of [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Phantom experiment results. a) Image overlay of the lifetime estimation results, [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Comparison of in-vivo results for both NLSF and MFliNet a) Image overlays of the short and long-lifetime results for both NLSF and MFliNet b) plot of means and standard deviations of the predicted lifetime values of both methods [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

39 extracted references · 38 canonical work pages

  1. [1]

    Fluorescence lifetime imaging–techniques and applications,

    W. Becker, “Fluorescence lifetime imaging–techniques and applications,” J. microscopy247, 119–136 (2012)

    work page 2012

  2. [2]

    Fluorescence lifetime imaging for quantification of targeted drug delivery in varying tumor microenvironments,

    A. Verma, V. Pandey, C. Sherry,et al., “Fluorescence lifetime imaging for quantification of targeted drug delivery in varying tumor microenvironments,” Adv. Sci. p. 2403253 (2024)

    work page 2024

  3. [3]

    Computational macroscopic lifetime imaging and concentration unmixing of autofluorescence,

    M. Ochoa, J. T. Smith, S. Gao, and X. Intes, “Computational macroscopic lifetime imaging and concentration unmixing of autofluorescence,” J. biophotonics15, e202200133 (2022)

    work page 2022

  4. [4]

    Multiplexed non-invasive tumor imaging of glucose metabolism and receptor-ligand engagement using dark quencher fret acceptor,

    A. Rudkouskaya, N. Sinsuebphon, M. Ochoa,et al., “Multiplexed non-invasive tumor imaging of glucose metabolism and receptor-ligand engagement using dark quencher fret acceptor,” Theranostics10, 10309 (2020)

    work page 2020

  5. [5]

    Tomographic fluorescence lifetime multiplexing in the spatial frequency domain,

    A. T. Kumar, S. S. Hou, and W. L. Rice, “Tomographic fluorescence lifetime multiplexing in the spatial frequency domain,” Optica5, 624–627 (2018)

    work page 2018

  6. [6]

    Rapid diagnosis and intraoperative margin assessment of human lung cancer with fluorescence lifetime imaging microscopy,

    M. Wang, F. Tang, X. Pan,et al., “Rapid diagnosis and intraoperative margin assessment of human lung cancer with fluorescence lifetime imaging microscopy,” BBA clinical8, 7–13 (2017)

    work page 2017

  7. [7]

    Fluorescence lifetime imaging (flim): Basic concepts and recent applications,

    K. Suhling, L. M. Hirvonen, J. A. Levitt,et al., “Fluorescence lifetime imaging (flim): Basic concepts and recent applications,” Adv. Time-Correlated Single Photon Count. Appl. pp. 119–188 (2015)

    work page 2015

  8. [8]

    Antibody-target binding quantification in living tumors using macroscopy fluorescence lifetime forster resonance energy transfer imaging (mfli fret),

    N. Yuan, V. Pandey, A. Verma,et al., “Antibody-target binding quantification in living tumors using macroscopy fluorescence lifetime forster resonance energy transfer imaging (mfli fret),” inVisualizing and Quantifying Drug Distribution in Tissue VIII,vol. 12821 (SPIE, 2024), pp. 17–20

    work page 2024

Show all 39 references
  1. [9]

    Using meditope-based antibody labeling to improve fluorescence lifetime fret imaging,

    A. Verma, C. Sherry, N. Yuan,et al., “Using meditope-based antibody labeling to improve fluorescence lifetime fret imaging,” inMultiphoton Microscopy in the Biomedical Sciences XXIV,(SPIE, 2024), p. PC128470S

    2024

  2. [10]

    Fluorescence lifetime imaging microscopy: fundamentals and advances in instrumentation, analysis, and applications,

    R. Datta, T. M. Heaster, J. T. Sharick,et al., “Fluorescence lifetime imaging microscopy: fundamentals and advances in instrumentation, analysis, and applications,” J. biomedical optics25, 071203–071203 (2020)

    2020

  3. [11]

    Luminescencelifetimeimagingofthree-dimensionalbiologicalobjects,

    R.I.Dmitriev,X.Intes,andM.M.Barroso,“Luminescencelifetimeimagingofthree-dimensionalbiologicalobjects,” J. Cell Sci.134, 1–17 (2021)

    2021

  4. [12]

    Near infrared fluorescence lifetime fret microscopy to evaluate antibody drug binding in various her2 positive cancer cell lines,

    C. Sherry, A. Verma, J. Smith,et al., “Near infrared fluorescence lifetime fret microscopy to evaluate antibody drug binding in various her2 positive cancer cell lines,” inMultiphoton Microscopy in the Biomedical Sciences XXIII,vol. 12384 (SPIE, 2023), pp. 162–167

    2023

  5. [13]

    Design and characterization of a time-domain optical tomography platform for mesoscopic lifetime imaging,

    S. Gao, M. Li, J. T. Smith, and X. Intes, “Design and characterization of a time-domain optical tomography platform for mesoscopic lifetime imaging,” Biomed. Opt. Express13, 4637–4651 (2022)

    2022

  6. [14]

    Development of an optical imaging platform for functional imaging of small animals using wide-field excitation,

    V. Venugopal, J. Chen, and X. Intes, “Development of an optical imaging platform for functional imaging of small animals using wide-field excitation,” Biomed. optics express1, 143–156 (2010)

    2010

  7. [15]

    Macroscopic fluorescence imaging,

    A. T. Kumar, “Macroscopic fluorescence imaging,” inImaging from Cells to Animals In Vivo,(CRC Press, 2020), pp. 91–106

    2020

  8. [16]

    Fluorescence lifetime measurements and biological imaging,

    M. Y. Berezin and S. Achilefu, “Fluorescence lifetime measurements and biological imaging,” Chem. reviews110, 2641–2684 (2010)

    2010

  9. [17]

    Characterization of fluorescence lifetime of organic fluorophores for molecular imaging in the shortwave infrared window,

    L. Chavez, S. Gao, and X. Intes, “Characterization of fluorescence lifetime of organic fluorophores for molecular imaging in the shortwave infrared window,” J. Biomed. Opt.28, 094806–094806 (2023)

    2023

  10. [18]

    Fluorescence lifetime imaging microscopy using near-infrared contrast agents,

    R. Nothdurft, P. Sarder, S. Bloch,et al., “Fluorescence lifetime imaging microscopy using near-infrared contrast agents,” J. microscopy247, 202–207 (2012)

    2012

  11. [19]

    Quantification of trastuzumab–her2 engagement in vitro and in vivo,

    A. Rudkouskaya, J. T. Smith, X. Intes, and M. Barroso, “Quantification of trastuzumab–her2 engagement in vitro and in vivo,” Molecules25, 5976 (2020)

    2020

  12. [20]

    Quantitative imaging of receptor-ligand engagement in intact live animals,

    A. Rudkouskaya, N. Sinsuebphon, J. Ward,et al., “Quantitative imaging of receptor-ligand engagement in intact live animals,” J. controlled release286, 451–459 (2018)

    2018

  13. [21]

    Fluorescence lifetime techniques in medical applications,

    L. Marcu, “Fluorescence lifetime techniques in medical applications,” Ann. biomedical engineering40, 304–331 (2012)

    2012

  14. [22]

    Experimental study of fluorescence lifetime uncertainty in time- gated iccd-based macroscopic fluorescence lifetime imaging,

    N. Yuan, V. Pandey, X. Michalet, and X. Intes, “Experimental study of fluorescence lifetime uncertainty in time- gated iccd-based macroscopic fluorescence lifetime imaging,” inClinical and Translational Biophotonics,(Optica Publishing Group, 2024), pp. TM5B–4

    2024

  15. [23]

    In vitro and in vivo phasor analysis of stoichiometry and pharmacokinetics using short-lifetime near-infrared dyes and time-gated imaging,

    S.-J. Chen, N. Sinsuebphon, A. Rudkouskaya,et al., “In vitro and in vivo phasor analysis of stoichiometry and pharmacokinetics using short-lifetime near-infrared dyes and time-gated imaging,” J. biophotonics12, e201800185 (2019)

    2019

  16. [24]

    Deep learning-based temporal deconvolution for photon time-of-flight distribution retrieval,

    V. Pandey, I. Erbas, X. Michalet,et al., “Deep learning-based temporal deconvolution for photon time-of-flight distribution retrieval,” Opt. Lett.49, 6457–6460 (2024)

    2024

  17. [25]

    A novel technique for fluorescence lifetime tomography,

    N. I. Nizam, V. Pandey, I. Erbas,et al., “A novel technique for fluorescence lifetime tomography,” bioRxiv (2024)

    2024

  18. [26]

    Compressingrecurrentneuralnetworksforfpga-acceleratedimplementation in fluorescence lifetime imaging,

    I.Erbas,V.Pandey,A.Amarnath, etal.,“Compressingrecurrentneuralnetworksforfpga-acceleratedimplementation in fluorescence lifetime imaging,” arXiv preprint arXiv:2410.00948 (2024)

    2024 arXiv

  19. [27]

    Unlocking real-time fluorescence lifetime imaging: multi-pixel parallelism for fpga-accelerated processing,

    I. Erbas, A. Amarnath, V. Pandey,et al., “Unlocking real-time fluorescence lifetime imaging: multi-pixel parallelism for fpga-accelerated processing,” arXiv preprint arXiv:2410.07364 (2024)

    2024 arXiv

  20. [28]

    Fast fit-free analysis of fluorescence lifetime imaging via deep learning,

    J. T. Smith, R. Yao, N. Sinsuebphon,et al., “Fast fit-free analysis of fluorescence lifetime imaging via deep learning,” Proc. National Acad. Sci.116, 24019–24030 (2019)

    2019

  21. [29]

    Transformers in time series: A survey,

    Q. Wen, T. Zhou, C. Zhang,et al., “Transformers in time series: A survey,” arXiv preprint arXiv:2202.07125 (2022)

    2022 arXiv

  22. [30]

    Transformers in vision: A survey,

    S. Khan, M. Naseer, M. Hayat,et al., “Transformers in vision: A survey,” ACM computing surveys (CSUR)54, 1–41 (2022)

    2022

  23. [31]

    Big bird: Transformers for longer sequences,

    M. Zaheer, G. Guruganesh, K. A. Dubey,et al., “Big bird: Transformers for longer sequences,” Adv. neural information processing systems33, 17283–17297 (2020)

    2020

  24. [32]

    Attention is all you need,

    A. Vaswani, N. Shazeer, N. Parmar,et al., “Attention is all you need,” Adv. neural information processing systems30 (2017)

    2017

  25. [33]

    Differential transformer,

    T. Ye, L. Dong, Y. Xia,et al., “Differential transformer,” arXiv (2024)

    2024

  26. [34]

    Venugopal,A small animal time-resolved optical tomography platform using wide-field excitation(Rensselaer Polytechnic Institute, 2011)

    V. Venugopal,A small animal time-resolved optical tomography platform using wide-field excitation(Rensselaer Polytechnic Institute, 2011)

    2011

  27. [35]

    Lakowicz,In Principles of Fluorescence Spectroscopy(Springer, US: Boston, MA, 2006)

    J. Lakowicz,In Principles of Fluorescence Spectroscopy(Springer, US: Boston, MA, 2006)

    2006

  28. [36]

    Video-rate fluorescence lifetime imaging camera with cmos single-photon avalanche diode arrays and high-speed imaging algorithm,

    D. D.-U. Li, J. Arlt, D. Tyndall,et al., “Video-rate fluorescence lifetime imaging camera with cmos single-photon avalanche diode arrays and high-speed imaging algorithm,” J. biomedical optics16, 096012–096012 (2011)

    2011

  29. [37]

    Alligator: A phasor computational platform for fast in vivo lifetime analysis,

    S.-J. Chen, N. Sinsuebphon, M. Barroso,et al., “Alligator: A phasor computational platform for fast in vivo lifetime analysis,” inOptical molecular probes, imaging and drug delivery,(Optica Publishing Group, 2017), pp. OmTu2D–2

    2017

  30. [38]

    Multimodal fluorescence lifetime imaging and optical coherence elastography for mesoscopic structural, biomechanical, and molecular imaging,

    L. Chavez, S. Gao, V. Pandey,et al., “Multimodal fluorescence lifetime imaging and optical coherence elastography for mesoscopic structural, biomechanical, and molecular imaging,” inClinical and Translational Biophotonics, (Optica Publishing Group, 2024), pp. TS3B–1

    2024

  31. [39]

    Assessment of open-field fluorescence guided surgery systems: implementing a standardized method for characterization and comparison,

    M. I. Ochoa, A. Ruiz, E. LaRochelle,et al., “Assessment of open-field fluorescence guided surgery systems: implementing a standardized method for characterization and comparison,” J. Biomed. Opt.28, 096007–096007 (2023)

    2023

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.