You can deploy this model and start earning money today!

3x6dll9ff/
diplom

Name: diplom
Availability: InStock

Views

No views yet

TriView3D — Multi-View 3D Cell Reconstruction

Reconstruction of 3D cell morphology from 3 orthogonal projections (top, bottom, side).

Architecture

Base CNN (`best_autoencoder.pt`)

Encoder2D: 2D ConvNet [3, 64, 64] → latent (256)
Decoder3D: 3D ConvTranspose → [1, 64, 64, 64] with skip connections from lifted views
Loss: BCE + Dice + Projection Consistency (BCE) + Boundary-aware BCE
Training: AdamW + 5-epoch LR warmup + complexity-weighted sampling + augmentation

Detail Refiner (`best_refiner.pt`)

Uncertainty-gated residual refinement on top of coarse CNN prediction
Input: coarse logits + lifted views → gating * residual
Skip connection version: compatible with skip-connected autoencoder

Conditional VAE (`best_vae.pt`)

Encoder: 2D ConvNet → mu (256) + logvar (256)
Decoder: 3D ConvTranspose with skip connections
Inference: best-of-K sampling (K=8) by reprojection error
Loss: Reconstruction + KL divergence

Classifier (`best_classifier.pt`)

MLP on latent vector (256) + morphometric features (5: volume, sphericity, convexity, surface_area, compactness)
Task: normal vs anomaly classification

Input Modes

tri: 3 channels (top_proj, bottom_proj, side_proj) — primary mode
quad: 4 channels (top_proj, bottom_proj, side_proj, front_proj)

Metrics (previous version, before skip connections + bug fixes)

Dice: ~0.90
IoU: ~0.82

Note: Models uploaded before the pipeline overhaul may have lower quality. Retrained models with skip connections, TTA, augmentation, and bug fixes are expected to achieve significantly higher Dice.

Usage

python

import torch
from src.autoencoder import TriViewAutoencoder
from src.reconstruction_utils import infer_in_channels_from_state_dict, infer_skip_channels_from_state_dict

state_dict = torch.load("best_autoencoder.pt", map_location="cpu")
if "model_state_dict" in state_dict:
    state_dict = state_dict["model_state_dict"]

in_channels = infer_in_channels_from_state_dict(state_dict)
skip_channels = infer_skip_channels_from_state_dict(state_dict)
latent_dim = int(state_dict["encoder.fc.1.weight"].shape[0])

model = TriViewAutoencoder(latent_dim=latent_dim, in_channels=in_channels, skip_channels=skip_channels)
model.load_state_dict(state_dict)
model.eval()

# Inference with TTA
import torch.nn.functional as F
x = ...  # [batch, 3, 64, 64] projections
pred = model(x)
pred_flipped = model(x.flip(-1)).flip(-1)
pred_avg = (pred + pred_flipped) / 2.0
volume = torch.sigmoid(pred_avg)  # [batch, 1, 64, 64, 64]

Pipeline Order

train_colab.ipynb → best_autoencoder.pt
train_refiner_colab.ipynb → best_refiner.pt (needs stage 1)
train_vae_colab.ipynb → best_vae.pt (independent)
train_classifier_colab.ipynb → best_classifier.pt (needs stage 1)

3x6dll9ff/diplom