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Experiment 1:

  • 3D autoencoder baseline
  • GH200 GPU
  • 100 patches
  • 5 epochs
  • MSE loss
  • Upsample + Conv3D decoder
  • Loss decreased successfully
  • Reconstruction captures global morphology but remains blurry
  • Reconstruction captures large-scale morphology and intensity distribution
  • Fine-grained microstructural details remain blurred
  • Low MSE loss does not necessarily correspond to high perceptual fidelity
  • Possible causes: shallow architecture, limited epochs, MSE smoothing effects
  • Final training loss: 0.000804

Experiment 2:

  • Increased training duration from 5 epochs to 25 epochs
  • Same 3D autoencoder baseline architecture
  • GH200 GPU
  • 100 patches
  • MSE reconstruction loss
  • Upsample + Conv3D decoder
  • Training loss decreased significantly over epochs
  • Final MSE loss reached very low values (~0.0008)
  • Reconstruction remained blurry despite lower numerical loss
  • Global morphology captured successfully
  • Fine-grained microstructural details remain blurred
  • Low MSE loss does not necessarily correspond to high perceptual fidelity
  • Possible causes: shallow architecture, limited dataset size, MSE smoothing effects
  • Demonstrated limitation of pixel-wise reconstruction objective

Experiment 3:

  • Increased raw dataset from 100 TIFF slices to 500 TIFF slices
  • Generated 700 3D patches of size 64×64×64
  • GH200 GPU
  • 25 epochs
  • MSE reconstruction loss
  • Upsample + Conv3D decoder

Results:

  • Training completed on larger patch dataset
  • Loss curve and reconstruction figure saved
  • Checkpoint saved

Observation:

  • Compare reconstruction sharpness and loss behaviour against Experiment 2

Experiment 4:

  • Added LPIPS perceptual loss to baseline 3D autoencoder

  • Combined loss function: Total Loss = MSE + 0.1 × LPIPS

  • LPIPS applied slice-wise on middle 2D slices extracted from 3D volumes

  • Grayscale slices converted to 3-channel format for LPIPS compatibility

  • Dataset size: 700 patches generated from 500 TIFF slices

  • Training epochs: 25

  • GPU: NVIDIA GH200 120GB

  • Decoder: Upsample + Conv3D architecture

  • MSE loss continued decreasing steadily

  • LPIPS loss decreased from ~0.21 to ~0.04

  • Reconstruction quality improved noticeably compared to pure MSE training

  • Fine structural textures and local morphology became more visible

  • Reconstruction still slightly smooth but preserves significantly more microstructural detail

  • Demonstrates perceptual-loss-based improvement over standard reconstruction objective

  • Final losses:

    • Total Loss ≈ 0.00449
    • MSE Loss ≈ 0.00053
    • LPIPS Loss ≈ 0.03968

    Experiment 5:

  • Increased dataset from 700 patches to 1200 patches

  • Generated from 800 TIFF slices

  • 3D autoencoder with deeper encoder-decoder architecture

  • Combined MSE + LPIPS perceptual loss

  • GH200 GPU training

  • 25 epochs

  • Reconstruction quality improved noticeably compared to earlier experiments

  • Fine-grained textures and structural patterns are better preserved

  • LPIPS loss decreased from ~0.20 to ~0.03 during training

  • Reconstruction remains slightly blurry but captures morphology more accurately

  • Larger dataset improved generalization and perceptual fidelity

  • Final losses:

    • Total Loss ≈ 0.006
    • MSE ≈ 0.0007
    • LPIPS ≈ 0.056