machine-learning

name	machine-learning
description	machine_learning skill Use when this capability is needed.
metadata	{"author":"brolag"}

Machine_Learning

Machine Learning

Overview

PathML provides comprehensive machine learning capabilities for computational pathology, including pre-built models for nucleus detection and segmentation, PyTorch-integrated training workflows, public dataset access, and ONNX-based inference deployment. The framework seamlessly bridges image preprocessing with deep learning to enable end-to-end pathology ML pipelines.

Pre-Built Models

PathML includes state-of-the-art pre-trained models for nucleus analysis:

HoVer-Net

HoVer-Net (Horizontal and Vertical Network) performs simultaneous nucleus instance segmentation and classification.

Architecture:

• Encoder-decoder structure with three prediction branches:
  • Nuclear Pixel (NP) - Binary segmentation of nuclear regions
  • Horizontal-Vertical (HV) - Distance maps to nucleus centroids
  • Classification (NC) - Nucleus type classification

Nucleus types:

1. Epithelial
2. Inflammatory
3. Connective/Soft tissue
4. Dead/Necrotic
5. Background

Usage:

from pathml.ml import HoVerNet
import torch

# Load pre-trained model
model = HoVerNet(
    num_types=5,  # Number of nucleus types
    mode='fast',  # 'fast' or 'original'
    pretrained=True  # Load pre-trained weights
)

# Move to GPU if available
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = model.to(device)

# Inference on tile
tile_image = torch.from_numpy(tile.image).permute(2, 0, 1).unsqueeze(0).float()
tile_image = tile_image.to(device)

with torch.no_grad():
    output = model(tile_image)

# Output contains:
# - output['np']: Nuclear pixel predictions
# - output['hv']: Horizontal-vertical maps
# - output['nc']: Classification predictions

Post-processing:

from pathml.ml import hovernet_postprocess

# Convert model outputs to instance segmentation
instance_map, type_map = hovernet_postprocess(
    np_pred=output['np'],
    hv_pred=output['hv'],
    nc_pred=output['nc']
)

# instance_map: Each nucleus has unique ID
# type_map: Each nucleus assigned a type (1-5)

HACTNet

HACTNet (Hierarchical Cell-Type Network) performs hierarchical nucleus classification with uncertainty quantification.

Features:

• Hierarchical classification (coarse to fine-grained types)
• Uncertainty estimation for predictions
• Improved performance on imbalanced datasets

from pathml.ml import HACTNet

# Load model
model = HACTNet(
    num_classes_coarse=3,
    num_classes_fine=8,
    pretrained=True
)

# Inference
output = model(tile_image)
coarse_pred = output['coarse']  # Broad categories
fine_pred = output['fine']  # Specific cell types
uncertainty = output['uncertainty']  # Prediction confidence

Training Workflows

Dataset Preparation

PathML provides PyTorch-compatible dataset classes:

TileDataset:

from pathml.ml import TileDataset
from pathml.core import SlideDataset

# Create dataset from processed slides
tile_dataset = TileDataset(
    slide_dataset,
    tile_size=256,
    transform=None  # Optional augmentation transforms
)

# Access tiles
image, label = tile_dataset[0]

DataModule Integration:

from pathml.ml import PathMLDataModule

# Create train/val/test splits
data_module = PathMLDataModule(
    train_dataset=train_tile_dataset,
    val_dataset=val_tile_dataset,
    test_dataset=test_tile_dataset,
    batch_size=32,
    num_workers=4
)

# Use with PyTorch Lightning
trainer = pl.Trainer(max_epochs=100)
trainer.fit(model, data_module)

Training HoVer-Net

Complete workflow for training HoVer-Net on custom data:

import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from pathml.ml import HoVerNet
from pathml.ml.datasets import PanNukeDataModule

# 1. Prepare data
data_module = PanNukeDataModule(
    data_dir='path/to/pannuke',
    batch_size=8,
    num_workers=4,
    tissue_types=['Breast', 'Colon']  # Specific tissue types
)

# 2. Initialize model
model = HoVerNet(
    num_types=5,
    mode='fast',
    pretrained=False  # Train from scratch or use pretrained=True for fine-tuning
)

# 3. Define loss function
class HoVerNetLoss(nn.Module):
    def __init__(self):
        super().__init__()
        self.mse_loss = nn.MSELoss()
        self.bce_loss = nn.BCEWithLogitsLoss()
        self.ce_loss = nn.CrossEntropyLoss()

    def forward(self, output, target):
        # Nuclear pixel branch loss
        np_loss = self.bce_loss(output['np'], target['np'])

        # Horizontal-vertical branch loss
        hv_loss = self.mse_loss(output['hv'], target['hv'])

        # Classification branch loss
        nc_loss = .ce_loss(output[], target[])

        
        total_loss = np_loss + hv_loss +  * nc_loss
         total_loss, {: np_loss, : hv_loss, : nc_loss}

criterion = HoVerNetLoss()


optimizer = torch.optim.Adam(
    model.parameters(),
    lr=,
    weight_decay=
)

scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
    optimizer,
    mode=,
    factor=,
    patience=
)


device = torch.device(  torch.cuda.is_available()  )
model = model.to(device)

num_epochs = 
 epoch  (num_epochs):
    model.train()
    train_loss = 

     batch  data_module.train_dataloader():
        images = batch[].to(device)
        targets = {
            : batch[].to(device),
            : batch[].to(device),
            : batch[].to(device)
        }

        optimizer.zero_grad()
        outputs = model(images)
        loss, loss_dict = criterion(outputs, targets)

        loss.backward()
        optimizer.step()

        train_loss += loss.item()

    
    model.()
    val_loss = 
     torch.no_grad():
         batch  data_module.val_dataloader():
            images = batch[].to(device)
            targets = {
                : batch[].to(device),
                : batch[].to(device),
                : batch[].to(device)
            }
            outputs = model(images)
            loss, _ = criterion(outputs, targets)
            val_loss += loss.item()

    scheduler.step(val_loss)

    ()
    ()
    ()

    
     (epoch + ) %  == :
        torch.save({
            : epoch,
            : model.state_dict(),
            : optimizer.state_dict(),
            : val_loss,
        }, )

PyTorch Lightning Integration

PathML models integrate with PyTorch Lightning for streamlined training:

import pytorch_lightning as pl
from pathml.ml import HoVerNet
from pathml.ml.datasets import PanNukeDataModule

class HoVerNetModule(pl.LightningModule):
    def __init__(self, num_types=5, lr=1e-4):
        super().__init__()
        self.model = HoVerNet(num_types=num_types, pretrained=True)
        self.lr = lr
        self.criterion = HoVerNetLoss()

    def forward(self, x):
        return self.model(x)

    def training_step(self, batch, batch_idx):
        images = batch['image']
        targets = {
            'np': batch['np_map'],
            'hv': batch['hv_map'],
            'nc': batch['type_map']
        }
        outputs = self(images)
        loss, loss_dict = self.criterion(outputs, targets)

        # Log metrics
        self.log('train_loss', loss, prog_bar=True)
        for key, val in loss_dict.items():
            self.log(f'train_{key}_loss', val)

        return loss

     ():
        images = batch[]
        targets = {
            : batch[],
            : batch[],
            : batch[]
        }
        outputs = (images)
        loss, loss_dict = .criterion(outputs, targets)

        .log(, loss, prog_bar=)
         key, val  loss_dict.items():
            .log(, val)

         loss

     ():
        optimizer = torch.optim.Adam(.parameters(), lr=.lr)
        scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
            optimizer, mode=, factor=, patience=
        )
         {
            : optimizer,
            : {
                : scheduler,
                : 
            }
        }


data_module = PanNukeDataModule(data_dir=, batch_size=)
model = HoVerNetModule(num_types=, lr=)

trainer = pl.Trainer(
    max_epochs=,
    accelerator=,
    devices=,
    callbacks=[
        pl.callbacks.ModelCheckpoint(monitor=, mode=),
        pl.callbacks.EarlyStopping(monitor=, patience=)
    ]
)

trainer.fit(model, data_module)

Public Datasets

PathML provides convenient access to public pathology datasets:

PanNuke Dataset

PanNuke contains 7,901 histology image patches from 19 tissue types with nucleus annotations for 5 cell types.

from pathml.ml.datasets import PanNukeDataModule

# Load PanNuke dataset
pannuke = PanNukeDataModule(
    data_dir='path/to/pannuke',
    batch_size=16,
    num_workers=4,
    tissue_types=None,  # Use all tissue types, or specify list
    fold='all'  # 'fold1', 'fold2', 'fold3', or 'all'
)

# Access dataloaders
train_loader = pannuke.train_dataloader()
val_loader = pannuke.val_dataloader()
test_loader = pannuke.test_dataloader()

# Batch structure
for batch in train_loader:
    images = batch['image']  # Shape: (B, 3, 256, 256)
    inst_map = batch['inst_map']  # Instance segmentation map
    type_map = batch['type_map']  # Cell type map
    np_map = batch['np_map']  # Nuclear pixel map
    hv_map = batch['hv_map']  # Horizontal-vertical distance maps
    tissue_type = batch['tissue_type']  # Tissue category

Tissue types available: Breast, Colon, Prostate, Lung, Kidney, Stomach, Bladder, Esophagus, Cervix, Liver, Thyroid, Head & Neck, Testis, Adrenal, Pancreas, Bile Duct, Ovary, Skin, Uterus

TCGA Datasets

Access The Cancer Genome Atlas datasets:

from pathml.ml.datasets import TCGADataModule

# Load TCGA dataset
tcga = TCGADataModule(
    data_dir='path/to/tcga',
    cancer_type='BRCA',  # Breast cancer
    batch_size=32,
    tile_size=224
)

Custom Dataset Integration

Create custom datasets for PathML workflows:

from torch.utils.data import Dataset
import numpy as np
from pathlib import Path

class CustomPathologyDataset(Dataset):
    def __init__(self, data_dir, transform=None):
        self.data_dir = Path(data_dir)
        self.image_paths = list(self.data_dir.glob('images/*.png'))
        self.transform = transform

    def __len__(self):
        return len(self.image_paths)

    def __getitem__(self, idx):
        # Load image
        image_path = self.image_paths[idx]
        image = np.array(Image.open(image_path))

        # Load corresponding annotation
        annot_path = self.data_dir / 'annotations' / f'{image_path.stem}.npy'
        annotation = np.load(annot_path)

        # Apply transforms
        if self.transform:
            image = self.transform(image)

        return {
            'image': torch.from_numpy(image).permute(2, 0, 1).(),
            : torch.from_numpy(annotation).long(),
            : (image_path)
        }


dataset = CustomPathologyDataset()
dataloader = DataLoader(dataset, batch_size=, shuffle=, num_workers=)

Data Augmentation

Apply augmentations to improve model generalization:

import albumentations as A
from albumentations.pytorch import ToTensorV2

# Define augmentation pipeline
train_transform = A.Compose([
    A.RandomRotate90(p=0.5),
    A.Flip(p=0.5),
    A.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2, hue=0.1, p=0.5),
    A.GaussianBlur(blur_limit=(3, 7), p=0.3),
    A.ElasticTransform(alpha=1, sigma=50, alpha_affine=50, p=0.3),
    A.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
    ToTensorV2()
])

val_transform = A.Compose([
    A.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
    ToTensorV2()
])

# Apply to dataset
train_dataset = TileDataset(slide_dataset, transform=train_transform)
val_dataset = TileDataset(val_slide_dataset, transform=val_transform)

Model Evaluation

Metrics

Evaluate model performance with pathology-specific metrics:

from pathml.ml.metrics import (
    dice_coefficient,
    aggregated_jaccard_index,
    panoptic_quality
)

# Dice coefficient for segmentation
dice = dice_coefficient(pred_mask, true_mask)

# Aggregated Jaccard Index (AJI) for instance segmentation
aji = aggregated_jaccard_index(pred_inst, true_inst)

# Panoptic Quality (PQ) for joint segmentation and classification
pq, sq, rq = panoptic_quality(pred_inst, true_inst, pred_types, true_types)

print(f"Dice: {dice:.4f}")
print(f"AJI: {aji:.4f}")
print(f"PQ: {pq:.4f}, SQ: {sq:.4f}, RQ: {rq:.4f}")

Evaluation Loop

from pathml.ml.metrics import evaluate_hovernet

# Comprehensive HoVer-Net evaluation
model.eval()
all_preds = []
all_targets = []

with torch.no_grad():
    for batch in test_loader:
        images = batch['image'].to(device)
        outputs = model(images)

        # Post-process predictions
        for i in range(len(images)):
            inst_pred, type_pred = hovernet_postprocess(
                outputs['np'][i],
                outputs['hv'][i],
                outputs['nc'][i]
            )
            all_preds.append({'inst': inst_pred, 'type': type_pred})
            all_targets.append({
                'inst': batch['inst_map'][i],
                'type': batch['type_map'][i]
            })

# Compute metrics
results = evaluate_hovernet(all_preds, all_targets)

print(f"Detection F1: {results['detection_f1']:.4f}")
print(f"Classification Accuracy: {results['classification_acc']:.4f}")
print(f"Panoptic Quality: {results['pq']:.4f}")

ONNX Inference

Deploy models using ONNX for production inference:

Export to ONNX

import torch
from pathml.ml import HoVerNet

# Load trained model
model = HoVerNet(num_types=5, pretrained=True)
model.eval()

# Create dummy input
dummy_input = torch.randn(1, 3, 256, 256)

# Export to ONNX
torch.onnx.export(
    model,
    dummy_input,
    'hovernet_model.onnx',
    export_params=True,
    opset_version=11,
    input_names=['input'],
    output_names=['np_output', 'hv_output', 'nc_output'],
    dynamic_axes={
        'input': {0: 'batch_size'},
        'np_output': {0: 'batch_size'},
        'hv_output': {0: 'batch_size'},
        'nc_output': {0: 'batch_size'}
    }
)

ONNX Runtime Inference

import onnxruntime as ort
import numpy as np

# Load ONNX model
session = ort.InferenceSession('hovernet_model.onnx')

# Prepare input
input_name = session.get_inputs()[0].name
tile_image = preprocess_tile(tile)  # Normalize, transpose to (1, 3, H, W)

# Run inference
outputs = session.run(None, {input_name: tile_image})
np_output, hv_output, nc_output = outputs

# Post-process
inst_map, type_map = hovernet_postprocess(np_output, hv_output, nc_output)

Batch Inference Pipeline

from pathml.core import SlideData
from pathml.preprocessing import Pipeline
import onnxruntime as ort

def run_onnx_inference_pipeline(slide_path, onnx_model_path):
    # Load slide
    wsi = SlideData.from_slide(slide_path)
    wsi.generate_tiles(level=1, tile_size=256, stride=256)

    # Load ONNX model
    session = ort.InferenceSession(onnx_model_path)
    input_name = session.get_inputs()[0].name

    # Inference on all tiles
    results = []
    for tile in wsi.tiles:
        # Preprocess
        tile_array = preprocess_tile(tile.image)

        # Inference
        outputs = session.run(None, {input_name: tile_array})

        # Post-process
        inst_map, type_map = hovernet_postprocess(*outputs)

        results.append({
            'coords': tile.coords,
            'instance_map': inst_map,
            'type_map': type_map
        })

    return results

# Run on slide
results = run_onnx_inference_pipeline('slide.svs', 'hovernet_model.onnx')

Transfer Learning

Fine-tune pre-trained models on custom datasets:

from pathml.ml import HoVerNet

# Load pre-trained model
model = HoVerNet(num_types=5, pretrained=True)

# Freeze encoder layers for initial training
for name, param in model.named_parameters():
    if 'encoder' in name:
        param.requires_grad = False

# Fine-tune only decoder and classification heads
optimizer = torch.optim.Adam(
    filter(lambda p: p.requires_grad, model.parameters()),
    lr=1e-4
)

# Train for a few epochs
train_for_n_epochs(model, train_loader, optimizer, num_epochs=10)

# Unfreeze all layers for full fine-tuning
for param in model.parameters():
    param.requires_grad = True

# Continue training with lower learning rate
optimizer = torch.optim.Adam(model.parameters(), lr=1e-5)
train_for_n_epochs(model, train_loader, optimizer, num_epochs=50)

Best Practices

1. Use pre-trained models when available:

   • Start with pretrained=True for better initialization
   • Fine-tune on domain-specific data

2. Apply appropriate data augmentation:

   • Rotate, flip for orientation invariance
   • Color jitter to handle staining variations
   • Elastic deformation for biological variability

3. Monitor multiple metrics:

   • Track detection, segmentation, and classification separately
   • Use domain-specific metrics (AJI, PQ) beyond standard accuracy

4. Handle class imbalance:

   • Weighted loss functions for rare cell types
   • Oversampling minority classes
   • Focal loss for hard examples

5. Validate on diverse tissue types:

   • Ensure generalization across different tissues
   • Test on held-out anatomical sites

6. Optimize for inference:

   • Export to ONNX for faster deployment
   • Batch tiles for efficient GPU utilization
   • Use mixed precision (FP16) when possible

7. Save checkpoints regularly:

   • Keep best model based on validation metrics
   • Save optimizer state for training resumption

Common Issues and Solutions

Issue: Poor segmentation at nucleus boundaries

• Use HV maps (horizontal-vertical) to separate touching nuclei
• Increase weight of HV loss term
• Apply morphological post-processing

Issue: Misclassification of similar cell types

• Increase classification loss weight
• Add hierarchical classification (HACTNet)
• Augment training data for confused classes

Issue: Training unstable or not converging

• Reduce learning rate
• Use gradient clipping: torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)
• Check for data preprocessing issues

Issue: Out of memory during training

• Reduce batch size
• Use gradient accumulation
• Enable mixed precision training: torch.cuda.amp

Issue: Model overfits to training data

• Increase data augmentation
• Add dropout layers
• Reduce model capacity
• Use early stopping based on validation loss

Additional Resources

• PathML ML API: https://pathml.readthedocs.io/en/latest/api_ml_reference.html
• HoVer-Net Paper: Graham et al., "HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images," Medical Image Analysis, 2019
• PanNuke Dataset: https://warwick.ac.uk/fac/cross_fac/tia/data/pannuke
• PyTorch Lightning: https://www.pytorchlightning.ai/
• ONNX Runtime: https://onnxruntime.ai/

Usage

Invoke this skill with:

$machine_learning [arguments]

Or let Codex auto-select based on your prompt.

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Source: brolag/claude-code-templates — distributed by TomeVault.