RamanMAE: Masked Autoencoders Enable Efficient Molecular Imaging by Learning Biologically Meaningful Spectral Representations
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Abstract
Traditional histopathological analysis of cells and tissue relies on morphological features from stained biopsy samples, which fail to leverage the wealth of chemical information about the underlying pathological states. Raman spectroscopy, a form of vibrational spectroscopy, uses light scattering to capture chemical information about the biological specimen. However, advancements in Raman spectroscopy are hindered by the method’s intrinsic low throughput and the difficulty in deriving meaningful insights from the high-dimensional noisy datasets. In this paper, we propose RamanMAE, a spectral language model using masked autoencoders on large Raman spectral datasets from biological applications that can be used for spectral processing in applications with limited data. We achieved excellent reconstruction of masked patches of the spectra. We learned meaningful latent representations of the spectra that capture biological compositional information and serve as a low-dimensional feature space for building downstream machine learning methods. We showed that the decoder serves as an effective smoothing technique to reduce noise in the spectra and allow better localization and visualization of biological features in the spectral maps. We also demonstrated the transferability of the representations learned on one dataset to a different biological application.
Article activity feed
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Overall, these re-sults show that the RamanMAE embeddings capture biolog-ically relevant information and have utility for downstreamclassification tasks in challenging biological datasets
I agree that this analysis sounds promising, but I think it would be more convincing with a comparison to more traditional methods (e.g. random forest after PCA or NMF)
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e shaped400-dimensional Raman spectra in the fingerprint wavenum-ber region
Since several different datasets were used here, what preprocessing was necessary to obtain spectra with this consistent range and resolution? Was it necessary to upsample or downsample the spectra from some datasets?
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As expected, the UMAP projection showsthat the P231, CTC, and LM cells are progressively clustered
I would be very cautious about drawing this (or any) conclusions on the basis of a UMAP, as UMAP embeddings can be strongly dependent on hyperparameter choices.
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Since the circulating tumor cells will have a population ofcells that will become lung metastatic cells in the future, asignificant degree of overlap between CTC and LM cells isreasonably expected
I'm not sure I follow this: just because some CTCs will eventually become metastatic doesn't necessarily mean that they will look similar or "overlap" in the present.
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We split the entire high SNR dataset into training,validation, and test dataset
I think more details abour how the data were split into train and test sets would be important to include here. From reading the supmat of [20], it sounds like the 172k spectra were obtained from 11 hyperspectral images, implying that many of the individual spectra are likely to be highly correlated, so I think care would be needed when splitting into train/test sets to avoid data leakage (e.g. perhaps by splitting at the level of the original hyperspectral images)
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We leveraged a large publicly available Raman spec-tral datase
This is missing a citation - presumably this dataset was from [20]?
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we useda masking ratio of 0.5 to randomly mask half the region ofthe spectra in each step
Could you comment on this was value was chosen? It seems like it could be a really important hyperparameter...
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For each spectrum,we used a patch size of 16 in 1D spectra, which translatedto a patch size of 4 in 2D image, yielding 16 patches foreach spectrum
If the spectra are 400-dimensional and the patch size is 16, there would be 20 patches per spectrum, not 16.
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