Ion Image Augmentation Intensity Dependent

Ion-Image Augmentation with Intensity-Dependent Missing Values

Summary

T_ISO augmentation extends base color jitter, filtering, and Poisson noise transformations by adding intensity-dependent missing-value masking to generate contrastive image pairs for isotope ion images in mass spectrometry imaging. This skill is essential when training representation models on isotope ion data where missing-value patterns should correlate with pixel intensity rather than occur uniformly.

When to use

Apply this skill when training a contrastive encoder on mass spectrometry imaging (MSI) data in ISO mode (isotope ions from the same molecule). Use it specifically to generate two augmented image pairs from a single raw ion image when the downstream encoder requires contrastive learning with intensity-aware data augmentation.

When NOT to use

• Input is already a feature table or pre-computed representation vector (skip to downstream projection module)
• Working in COL mode (co-localized ions); use base T_COL augmentation without intensity-dependent masking instead
• Raw MSI data is unpreprocessed or contains invalid/missing calibration information; preprocess first

Inputs

• raw ion image (2D pixel array with intensity values)
• MSI matrix data [X*Y, P] where X, Y are pixel coordinates and P is ion count
• MSI peak data [P, 1] listing P ions

Outputs

• augmented image pair (two 2D pixel arrays)
• 512-dimensional representation vector (after encoder propagation)

How to apply

Load a preprocessed MSI ion image (raw pixel intensities). Apply T_COL augmentations (color jitter, filtering, Poisson noise, random missing value) to generate a first augmented image. Apply identical T_COL augmentations to create a second augmented image. On the second augmented image only, apply intensity-dependent missing-value masking, where the probability of masking each pixel is conditioned on that pixel's intensity value (e.g., higher-intensity pixels may have different masking probability than low-intensity pixels). Return both augmented images as a pair for propagation through shared-parameter ResNet18 encoders. The rationale is that isotope ions exhibit intensity-correlated spatial patterns; conditioning missing-value augmentation on intensity preserves this relationship while still creating meaningful contrastive pairs.

Related tools

• ResNet18 (Encoder backbone that receives both augmented image pairs and outputs 512-dimensional representation vectors with shared parameters between image pair)
• kornia (Image processing library used for color jitter, filtering, and geometric transformations in T_COL augmentation pipeline)
• PyTorch (Deep learning framework for implementing augmentation pipeline, encoder, and contrastive loss computation)

Examples

python run.py --input_Matrix .../DATASET/Pos_brain_data_matrix.txt --input_PeakList .../DATASET/Pos_brain_data_peak.csv --input_shape 198 422 --mode ISO --ion_mode positive --output_file Pos_ISO_result

Evaluation signals

• Verify that both augmented images are generated and have identical spatial dimensions to input
• Confirm that second augmented image has intensity-dependent masking applied (pixels with same intensity should have similar masking probability; masking probability should vary across intensity range)
• Check that first augmented image contains only T_COL augmentations (color jitter, filtering, Poisson noise, uniform random missing value) without intensity-dependent masking
• Validate that encoder produces 512-dimensional vectors with contrastive similarity maximized between the pair (via stop-gradient loss computation)
• Inspect that missing-value patterns in second image show correlation with underlying pixel intensity distribution (higher/lower intensity regions should exhibit predictable masking patterns)

Limitations

• Intensity-dependent masking rationale is tailored to isotope ions (ISO mode); application to co-localized ions (COL mode) may degrade performance
• Missing-value function design (intensity → masking probability mapping) is not explicitly specified in source material; practitioner must infer or tune empirically
• Requires preprocessed MSI data with validated intensity calibration; noisy or poorly calibrated raw data may yield uninformative augmentations
• Contrastive learning assumes sufficient variation in ion image intensity; uniformly low-intensity images may not benefit from this approach

Evidence

• [other] T_ISO augmentation extends T_COL (which includes color jitter, filtering, Poisson noise, and random missing value) by introducing an additional intensity-dependent missing value process specifically for the ISO mode to generate two augmented images from an original ion image.: "T_ISO augmentation extends T_COL (which includes color jitter, filtering, Poisson noise, and random missing value) by introducing an additional intensity-dependent missing value process specifically"
• [readme] the T_COL including color jitter, filtering, Poisson noise, and random missing value is carried in COL mode, while T_ISO introduces an additional process of intensity-dependent missing value in ISO mode.: "the T_COL including color jitter, filtering, Poisson noise, and random missing value is carried in COL mode, while T_ISO introduces an additional process of intensity-dependent missing value in ISO"
• [readme] Two augmented images are propagated through a pair of ResNet18-based encoders that shared parameters, then output two 512-dimensional representation vectors: "Two augmented images are propagated through a pair of ResNet18-based encoders that shared parameters, then output two 512-dimensional representation vectors"
• [readme] A contrastive loss is employed to maximize similarity with a stop-gradient operation to prevent collapsing during training.: "A contrastive loss is employed to maximize similarity with a stop-gradient operation to prevent collapsing during training"
• [readme] isotope ions from a same molecule: "isotope ions from a same molecule respectively"