Colab Showcase Structure — LDCT Project

0) Title & Setup

Title: Low-Dose CT Reconstruction with Diffusion Models Setup: import libraries, (optional) mount Drive, set global seed, print environment (Python, PyTorch, CUDA).

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1) Exploratory Data Analysis (EDA)

Goal: Demonstrate you understand the data and imaging characteristics—not just “plug & play” ML.

Dataset context (your setup):

• Source: OrganMNIST3D (MedMNIST v2/v3) — volumes are 28×28×28, grayscale, intensities scaled to [0,1] (proxy HU).
• We simulate low-dose (LD) from the clean slice (GT): mild blur → Poisson noise (photon statistics) → Gaussian readout noise → occasional down–up sampling.
• No DICOM metadata (e.g., KVP, Exposure, ConvolutionKernel, pixel spacing, slice thickness). Treat any “HU” work as intensity proxies; physical-dose analysis is out of scope for this dataset.

Dataset overview (what you actually check):

• Number of volumes and the number of extracted 2D slices (based on your sampling policy).
• Slice selection strategy (center-biased + random) and augmentation toggle.
• Note: per-patient IDs and clinical attributes are not provided in OrganMNIST3D.

Visual samples (you implemented):

• Random LD vs GT slice with windowing on [0,1].
• Error map (LD−GT) to show spatial noise structure.

Statistical analysis (you implemented):

• Intensity histogram (proxy HU) for GT (and optionally LD) to see distribution shifts.
• Noise estimate: standard deviation in a homogeneous ROI (e.g., circular mask)—compare LD vs GT (LD should have higher std).
• Line profile across a fixed row to visualize edge degradation and noise texture.

Pairing check (adapted to your pipeline):

• Because LD is simulated from GT at a given (volume_id, slice_id), pairing is guaranteed by construction.
• You validate pairing via index bookkeeping (e.g., asserting (vi, si) consistency), rather than DICOM tags like InstanceNumber or ImagePositionPatient.

Summary (expected insights with this dataset):

• LD is noisier than GT (↑ROI std, more high-frequency fluctuation in line profiles).
• No claims about slice thickness or pixel spacing (not available).
• This is a proxy study for LDCT: great for methods benchmarking, but not for radiation-dose physics.

📌 This EDA shows you understand the imaging problem and the dataset’s limitations (no metadata, tiny 28×28 slices), framing your later results correctly.

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2) Baseline Models (Supervised DL)

Goal: Prove you can implement standard restoration baselines and benchmark them.

• Models: UNet (2D image-to-image), DnCNN (denoising CNN), SwinIR-tiny (transformer for restoration).
• Training: L1 + SSIM loss, AdamW, early stopping.
• Metrics: PSNR, SSIM on validation.
• Visualization: LD vs UNet vs GT (plus optional error maps).

📌 Shows solid DL engineering + fair benchmarking before jumping to advanced generative methods.

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3) Advanced Model: Diffusion (Highlight AI/ML)

Goal: Showcase advanced generative modeling skills.

• Conditional Diffusion (DDPM + UNet backbone): Input: LD + timestep t. Output: predicted noise ε; iterative sampling to reconstruct x₀.

• Training: MSE noise loss, EMA, T = 1000 diffusion steps; inference with 250 sampling steps (default).

• Ablations:

  • Timesteps: 1000 vs 250 vs 50.
  • Loss: MSE vs MSE + SSIM.

• Sampling demo: visual snapshots (e.g., step 10 → 250 → final).

• Comparison table: Baselines vs Diffusion → PSNR, SSIM, LPIPS. Include runtime and parameter count (efficiency awareness).

📌 Clear highlight that you can implement SOTA generative methods and analyze their behavior in medical imaging.

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4) Evaluation & Visualization

• Side-by-side figure: LD | UNet | SwinIR | Diffusion | GT.

• Error maps: |pred − GT| for each model.

• Histogram (proxy HU): compare output intensity distributions vs GT.

• Quantitative table (test set): PSNR / SSIM / LPIPS, with params & runtimes.

• Discussion:

  • Diffusion preserves fine detail/edges better (lower structured residuals; histogram closer to GT).
  • Baselines (especially CNNs) often look smoother/blurrier—higher bias, lower variance.
  • Recognize trade-offs: diffusion wins on fidelity but costs sampling time; CNNs win on speed.

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5) Conclusion & Future Work

• Conclusion: Diffusion outperforms baselines on PSNR/SSIM (and typically LPIPS), reduces noise while preserving anatomical detail better than UNet/DnCNN/SwinIR in this proxy setting.

• Future work:

  • Extend to 3D (volumetric UNet / 3D DDPM).
  • Multimodal conditioning with dose/physics metadata (requires clinical DICOM).
  • Clinical validation on real LD/SD datasets; evaluate diagnostic endpoints and reader studies.

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6. (Bonus, kalau sempat) Demo Interaktif

• Gradio demo: upload LD slice → reconstruct dengan diffusion.
• Menunjukkan kamu bisa bikin aplikasi AI medis usable.

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📊 Colabs Structures

#	Section / Cells	Purpose (What it does)	Key Outputs / Artifacts	Notes & Dependencies
0	Title & Setup (0, 1a, 1b)	Set notebook title, install deps, print env (Python, Torch, CUDA), set seeds.	–	Run first. Confirms versions & device.
1	Core Imports & Config (2)	Import libs, define device/seed helpers.	–	Used by all later cells.
2	Data & LD Simulator (3)	Load OrganMNIST3D; create paired (LD, GT) slices via blur + Poisson + Gaussian + (optional) down–up sampling.	–	Produces LD–GT pairs; no DICOM metadata.
3	EDA Utilities (4)	Visual helpers: LD/GT display, error map (LD–GT), intensity histogram, ROI-noise std, line profile.	Inline plots	Uses dataset from (3).
4	Metrics (5)	Implement SSIM & PSNR (for training & eval).	–	Reused across baselines & diffusion.
5	Models (6)	Define UNet, DnCNN, SwinIR-tiny.	–	28×28 single-channel friendly.
6	Loss & Early Stop (7)	L1+SSIM loss; early stopping utility.	–	For baselines training.
7	Train/Validate Loop (8)	Generic training with AdamW (amp optional), validation PSNR/SSIM, curves, sample visualization.	runs/<model-*>/logs.json, inline curves	Saves best_ckpt.pt on val SSIM.
8	EDA Run (9)	Execute EDA: LD vs GT, error map, histogram, ROI noise, line profile.	Inline plots	Shows LD noisier than GT (proxy-HU).
9	Train Baselines (10)	Train UNet/DnCNN/SwinIR (quick epochs).	run dirs per model	Needed for comparisons if no ckpt exists.
10	Compare Baselines (11)	Summarize last-epoch val PSNR/SSIM; optional viz hooks.	pandas table	Uses histories from (10).
11	Diffusion Model (12)	Conditional DDPM-UNet (input: x_t, LD, t; output: ε), FiLM + sinusoidal t-emb.	–	Core model for diffusion.
12	DDPM Schedules & EMA (13)	Linear/cosine β schedule, q/p sampling helpers, EMA weights, sampling recorder.	–	Used by training & demo.
13	Train Diffusion (14)	Train with MSE noise loss (optional +SSIM), keep EMA, quick val PSNR/SSIM.	runs_diff/.../ddpm_ema.pt, logs	Produces EMA ckpt for inference.
14	Sampling Demo (15)	Sample 250 steps; snapshot step 10 → 250 → final; grid visualization.	figs/diffusion_grid_demo.png	Requires EMA from (14).
15	Ablations (16)	Compare sampling steps (1000/250/50); optional loss ablation (MSE vs MSE+SSIM).	printed table	Quick eval path.
16	LPIPS + Comparison (17)	(Optional) LPIPS with safe resize→224; table: Baselines vs Diffusion (PSNR/SSIM/LPIPS + params/runtime).	figs/comparison_baseline_vs_diffusion.csv	Downloads AlexNet weights once; graceful fallback if LPIPS unavailable.
17	Save & Export (18)	Persist demo grid & comparison CSV.	PNG/CSV files	For paper/report.
18	Side-by-side & Error Maps (19)	**LD	UNet	SwinIR	Diffusion	GT** montage + **	pred−GT	** error maps.	figs/side_by_side_test.png, figs/error_maps_test.png	Needs trained baselines (or ckpts) & diffusion EMA.
19	Histogram (proxy HU) (20)	Compare distributions (UNet/SwinIR/Diffusion vs GT) on test subset.	figs/hist_proxy_hu_test.png	Proxy-HU since MedMNIST is [0,1].
20	Test-set Metrics Table (21)	Full test PSNR/SSIM/LPIPS, with param counts.	figs/test_metrics_table.csv	Uses compute_lpips_batch if defined.
21	Discussion (Auto-notes) (22)	Auto-generate narrative: who’s best, histogram/error-map interpretation, params.	Printed notes	Paste into report.
22	Bootstrap CI (23)	95% CI for mean PSNR/SSIM via bootstrap (per-slice).	figs/bootstrap_ci_psnr_ssim.csv	Strengthens statistical claims.

Recommended run order (quick)

1. 0–1b (setup) → 2–8 (data, EDA utils & metrics) → 9 (EDA run)
2. 10–11 (baselines train & compare)
3. 12–15 (diffusion train + demo) → 16–17–18 (ablations & comparison & export)
4. 19–22 (final eval & discussion) → 23 (bootstrap CI)