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+# MidasMap: Automated Immunogold Particle Detection for TEM Synapse Images
+
+---
+
+## The Problem
+
+Neuroscientists use **immunogold labeling** to visualize receptor proteins at synapses in transmission electron microscopy (TEM) images.
+
+- **6nm gold beads** label AMPA receptors (panAMPA)
+- **12nm gold beads** label NR1 (NMDA) receptors
+- **18nm gold beads** label vGlut2 (vesicular glutamate transporter)
+
+**Manual counting is slow and subjective.** Each image takes 30-60 minutes to annotate. With hundreds of synapses per experiment, this becomes a bottleneck.
+
+### The Challenge
+- Particles are **tiny** (4-10 pixels radius) on 2048x2115 images
+- Contrast delta is only **11-39 intensity units** on a 0-255 scale
+- Large dark vesicles look similar to gold particles to naive detectors
+- Only **453 labeled particles** across 10 training images
+
+---
+
+## Previous Approaches (GoldDigger et al.)
+
+| Approach | Result |
+|----------|--------|
+| CenterNet (initial attempt) | "Detection quality remained poor" |
+| U-Net heatmap | Macro F1 = 0.005-0.017 |
+| GoldDigger/cGAN | "No durable breakthrough" |
+| Aggressive filtering | "FP dropped but TP dropped harder" |
+
+**Core issue:** Previous systems failed due to:
+1. Incorrect coordinate conversion (microns treated as normalized values)
+2. Broken loss function (heatmap peaks not exactly 1.0)
+3. Overfitting to fixed training patches
+
+---
+
+## MidasMap Architecture
+
+```
+Input: Raw TEM Image (any size)
+         |
+    [Sliding Window → 512x512 patches]
+         |
+    ResNet-50 Encoder (pretrained on CEM500K: 500K EM images)
+         |
+    BiFPN Neck (bidirectional feature pyramid, 2 rounds, 128ch)
+         |
+    Transposed Conv Decoder → stride-2 output
+         |
+    +------------------+-------------------+
+    |                  |                   |
+    Heatmap Head       Offset Head
+    (2ch sigmoid)      (2ch regression)
+    6nm channel        sub-pixel x,y
+    12nm channel       correction
+    |                  |
+    +------------------+-------------------+
+         |
+    Peak Extraction (max-pool NMS)
+         |
+    Cross-class NMS + Mask Filter
+         |
+    Output: [(x, y, class, confidence), ...]
+```
+
+### Key Design Decisions
+
+**CEM500K Backbone:** ResNet-50 pretrained on 500,000 electron microscopy images via self-supervised learning. The backbone already understands EM structures (membranes, vesicles, organelles) before seeing any gold particles. This is why the model reaches F1=0.93 in just 5 epochs.
+
+**Stride-2 Output:** Standard CenterNet uses stride 4. At stride 4, a 6nm bead (4-6px radius) collapses to 1 pixel — too small to detect reliably. At stride 2, the same bead occupies 2-3 pixels, enough for Gaussian peak detection.
+
+**CornerNet Focal Loss:** With positive:negative pixel ratio of 1:23,000, standard BCE would learn to predict all zeros. The focal loss uses `(1-p)^alpha` weighting to focus on hard examples and `(1-gt)^beta` penalty reduction near peaks.
+
+**Raw Image Input:** No preprocessing. The CEM500K backbone was trained on raw EM images. Any heavy preprocessing (top-hat, CLAHE) creates a domain gap and hurts performance. The model learns to distinguish particles from vesicles through training data, not handcrafted filters.
+
+---
+
+## Training Strategy
+
+### 3-Phase Training with Discriminative Learning Rates
+
+| Phase | Epochs | What's Trainable | Learning Rate |
+|-------|--------|-------------------|---------------|
+| **1. Warm-up** | 40 | BiFPN + heads only | 1e-3 |
+| **2. Deep unfreeze** | 40 | + layer3 + layer4 | 1e-5 to 5e-4 |
+| **3. Full fine-tune** | 60 | All layers | 1e-6 to 2e-4 |
+
+```
+Loss Curve (final model):
+
+Phase 1          Phase 2          Phase 3
+|                |                |
+1.4 |\           |                |
+    | \          |                |
+1.0 |  \         |                |
+    |   ----     |                |
+0.8 |       \    |                |
+    |        \   |                |
+0.6 |         \--+---             |
+    |            |   \            |
+0.4 |            |    \---        |
+    |            |        \-------+---
+0.2 |            |                |
+    +---+---+----+---+---+----+---+---+--> Epoch
+    0   10  20   40  50  60   80  100 140
+```
+
+### Data Augmentation
+- Random 90-degree rotations (EM is rotation-invariant)
+- Horizontal/vertical flips
+- Conservative brightness/contrast (+-8% — preserves the subtle particle signal)
+- Gaussian noise (simulates shot noise)
+- **Copy-paste augmentation**: real bead crops blended onto training patches
+- **70% hard mining**: patches centered on particles, 30% random
+
+### Overfitting Prevention
+- **Unique patches every epoch**: RNG reseeded per sample so the model never sees the same patch twice
+- **Early stopping**: patience=20 epochs, monitoring validation F1
+- **Weight decay**: 1e-4 on all parameters
+
+---
+
+## Critical Bugs Found and Fixed
+
+### Bug 1: Coordinate Conversion
+**Problem:** CSV files labeled "XY in microns" were assumed to be normalized [0,1] coordinates. They were actual micron values.
+
+**Effect:** All particle annotations were offset by 50-80 pixels from the real locations. The model was learning to detect particles where none existed.
+
+**Fix:** Multiply by 1790 px/micron (verified against researcher's color overlay TIFs across 7 synapses).
+
+### Bug 2: Heatmap Peak Values
+**Problem:** Gaussian peaks were centered at float coordinates, producing peak values of 0.78-0.93 instead of exactly 1.0.
+
+**Effect:** The CornerNet focal loss uses `pos_mask = (gt == 1.0)` to identify positive pixels. With no pixels at exactly 1.0, the model had **zero positive training signal**. It literally could not learn.
+
+**Fix:** Center Gaussians at the integer grid point (always produces 1.0). Sub-pixel precision is handled by the offset regression head.
+
+### Bug 3: Overfitting on Fixed Patches
+**Problem:** The dataset generated 200 random patches once at initialization. Every epoch replayed the same patches.
+
+**Effect:** On fast CUDA GPUs, the model memorized all patches in ~17 epochs (loss crashed from 1.6 to 0.002). Validation F1 peaked at 0.66 and degraded.
+
+**Fix:** Reseed RNG per `__getitem__` call so every patch is unique.
+
+---
+
+## Results
+
+### Leave-One-Image-Out Cross-Validation (10 folds, 5 seeds each)
+
+| Fold | Avg F1 | Best F1 | Notes |
+|------|--------|---------|-------|
+| S27 | **0.990** | 0.994 | |
+| S8 | **0.981** | 0.988 | |
+| S25 | **0.972** | 0.977 | |
+| S29 | **0.956** | 0.966 | |
+| S1 | **0.930** | 0.940 | |
+| S4 | **0.919** | 0.972 | |
+| S22 | **0.907** | 0.938 | |
+| S13 | **0.890** | 0.912 | |
+| S7 | 0.799 | 1.000 | Only 3 particles (noisy metric) |
+| S15 | 0.633 | 0.667 | Only 1 particle (noisy metric) |
+
+**Mean F1 = 0.943** (8 folds with sufficient annotations)
+
+### Per-class Performance (S1 fold, best threshold)
+
+| Class | Precision | Recall | F1 |
+|-------|-----------|--------|----|
+| 6nm (AMPA) | 0.895 | **1.000** | **0.944** |
+| 12nm (NR1) | 0.833 | **1.000** | **0.909** |
+
+**100% recall** on both classes — every particle is found. Only errors are a few false positives.
+
+### Generalization to Unseen Images
+
+Tested on 15 completely unseen images from a different imaging session. Detections land correctly on particles with no manual tuning. The model successfully detects both 6nm and 12nm particles on:
+- Wild-type (Wt2) samples
+- Heterozygous (Het1) samples
+- Different synapse regions (D1, E3, S1, S10, S12, S18)
+
+---
+
+## System Components
+
+```
+MidasMap/
+  config/config.yaml        # All hyperparameters
+  src/
+    preprocessing.py        # Data loading (10 synapses, 453 particles)
+    model.py                # CenterNet: ResNet-50 + BiFPN + heads (24.4M params)
+    loss.py                 # CornerNet focal loss + offset regression
+    heatmap.py              # GT generation + peak extraction + NMS
+    dataset.py              # Patch sampling, augmentation, copy-paste
+    postprocess.py          # Mask filter, cross-class NMS
+    ensemble.py             # D4 TTA + sliding window inference
+    evaluate.py             # Hungarian matching, F1/precision/recall
+    visualize.py            # Overlay visualizations
+  train.py                  # LOOCV training (--fold, --seed)
+  train_final.py            # Final deployable model (all data)
+  predict.py                # Inference on new images
+  evaluate_loocv.py         # Full evaluation runner
+  app.py                    # Gradio web dashboard
+  slurm/                    # HPC job scripts
+  tests/                    # 36 unit tests
+```
+
+---
+
+## Dashboard
+
+MidasMap includes a web-based dashboard (Gradio) for interactive use:
+
+1. **Upload** any TEM image (.tif)
+2. **Adjust** confidence threshold and NMS parameters
+3. **View** detections overlaid on the image
+4. **Inspect** per-class heatmaps
+5. **Analyze** confidence distributions and spatial patterns
+6. **Export** results as CSV (particle_id, x_px, y_px, x_um, y_um, class, confidence)
+
+```
+python app.py --checkpoint checkpoints/final/final_model.pth
+# Opens at http://localhost:7860
+```
+
+---
+
+## Future Directions
+
+1. **Spatial analytics**: distance to synaptic cleft, nearest-neighbor analysis, Ripley's K-function
+2. **Size regression head**: predict actual bead diameter instead of binary classification
+3. **18nm detection**: extend to vGlut2 particles (3-class model)
+4. **Active learning**: flag low-confidence detections for human review
+5. **Cross-protocol generalization**: fine-tune on cryo-EM or different staining protocols
+
+---
+
+## Technical Summary
+
+- **Model**: CenterNet with CEM500K-pretrained ResNet-50, BiFPN neck, stride-2 output
+- **Training**: 3-phase with discriminative LRs, 140 epochs, 453 particles / 10 images
+- **Evaluation**: Leave-one-image-out CV, Hungarian matching, F1 = 0.943
+- **Inference**: Sliding window (512x512, 128px overlap), ~10s per image on GPU
+- **Output**: Per-particle (x, y, class, confidence) with optional heatmap visualization