Multimodal Ontology Fusion
Overview
Recent work shows that combining visual concepts with medical domain knowledge significantly improves explainable medical image classification. Multimodal approaches that integrate imaging, molecular, and clinical data provide holistic insights into disease processes.
1. Ontology Alignment Strategies
A. Hierarchical Alignment Approach
Concept: Create a unified ontology hierarchy that bridges visual features with medical concepts.
from owlready2 import *
import rdflib
from rdflib import Graph, Namespace, RDF, RDFS, OWL
# Define namespace hierarchy
medical_ns = Namespace("http://medical-onto.org/")
image_ns = Namespace("http://image-classification.org/")
fusion_ns = Namespace("http://fusion-onto.org/")
# Create fusion ontology
fusion_onto = get_ontology("http://fusion-onto.org/multimodal")
class VisualConcept(Thing):
"""Base class for image-derived concepts"""
namespace = fusion_onto
class MedicalConcept(Thing):
"""Base class for medical domain concepts"""
namespace = fusion_onto
class ImagedMedicalEntity(VisualConcept, MedicalConcept):
"""Fusion class linking visual and medical concepts"""
namespace = fusion_onto
# Define bridging properties
class visuallyRepresents(ObjectProperty):
"""Links visual features to medical concepts"""
domain = [VisualConcept]
range = [MedicalConcept]
class hasImageFeature(ObjectProperty):
"""Links medical entities to their visual features"""
domain = [MedicalConcept]
range = [VisualConcept]B. Cross-Domain Mapping
Strategy: Map equivalent concepts across ontologies using semantic similarity.
class OntologyMapper:
"""Maps concepts between image and medical ontologies"""
def __init__(self, image_onto_path: str, medical_onto_path: str):
self.image_onto = get_ontology(image_onto_path).load()
self.medical_onto = get_ontology(medical_onto_path).load()
self.mappings = {}
def create_semantic_mappings(self):
"""Create mappings based on semantic similarity"""
mappings = {
# Visual → Medical mappings
"circular_structure": "cell_nucleus",
"irregular_boundary": "malignant_growth_pattern",
"high_density_region": "hypercellular_area",
"color_variation": "tissue_heterogeneity",
"texture_pattern": "histological_pattern"
}
for visual_concept, medical_concept in mappings.items():
self._create_mapping_axiom(visual_concept, medical_concept)
def _create_mapping_axiom(self, visual_term: str, medical_term: str):
"""Create OWL axiom linking concepts"""
# Implementation would create equivalence or subsumption axioms
pass2. Multi-Modal Data Integration
A. Pathomic Fusion Approach
import torch
import torch.nn as nn
class MultiModalOntologyFusion(nn.Module):
"""Integrates image classification with medical ontological knowledge"""
def __init__(self, image_feature_dim: int, ontology_embedding_dim: int, num_medical_concepts: int):
super().__init__()
# Image feature encoder
self.image_encoder = nn.Sequential(
nn.Linear(image_feature_dim, 512),
nn.ReLU(),
nn.Dropout(0.3),
nn.Linear(512, 256)
)
# Medical ontology encoder
self.medical_encoder = nn.Sequential(
nn.Linear(num_medical_concepts, 256),
nn.ReLU(),
nn.Dropout(0.3),
nn.Linear(256, 128)
)
# Cross-modal attention
self.cross_attention = nn.MultiheadAttention(
embed_dim=256, num_heads=8, batch_first=True
)
# Fusion layers
self.fusion_layers = nn.Sequential(
nn.Linear(256 + 128, 256),
nn.ReLU(),
nn.Dropout(0.4),
nn.Linear(256, 64)
)
# Classification head
self.classifier = nn.Linear(64, 1) # Binary classification
def forward(self, image_features: torch.Tensor, medical_concepts: torch.Tensor) -> torch.Tensor:
# Encode modalities
img_encoded = self.image_encoder(image_features)
med_encoded = self.medical_encoder(medical_concepts)
# Cross-modal attention (image attending to medical concepts)
img_attended, _ = self.cross_attention(
img_encoded.unsqueeze(1),
med_encoded.unsqueeze(1),
med_encoded.unsqueeze(1)
)
img_attended = img_attended.squeeze(1)
# Fusion
fused = torch.cat([img_attended, med_encoded], dim=1)
fused_features = self.fusion_layers(fused)
# Classification
output = self.classifier(fused_features)
return outputB. SPARQL-Based Feature Alignment
from SPARQLWrapper import SPARQLWrapper, JSON
class SemanticFeatureAligner:
"""Aligns image features with medical concepts using SPARQL"""
def __init__(self, endpoint_url: str):
self.sparql = SPARQLWrapper(endpoint_url)
self.sparql.setReturnFormat(JSON)
def align_visual_to_medical(self, visual_features: List[str], medical_domain: str) -> Dict[str, List[str]]:
"""Find medical concepts related to visual features"""
alignments = {}
for feature in visual_features:
query = f"""
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX skos: <http://www.w3.org/2004/02/skos/core#>
SELECT DISTINCT ?medical_concept ?similarity WHERE {{
?visual_concept rdfs:label "{feature}" .
?visual_concept skos:related ?medical_concept .
?medical_concept rdf:type <{medical_domain}> .
OPTIONAL {{ ?mapping ont:similarity ?similarity }}
}}
ORDER BY DESC(?similarity)
LIMIT 5
"""
self.sparql.setQuery(query)
results = self.sparql.query().convert()
alignments[feature] = [
result['medical_concept']['value']
for result in results['results']['bindings']
]
return alignments3. Explainable AI Integration
A. Explainable Multi-Dimensional Alignment
class ExplainableMultiModalFusion:
"""Provides interpretable fusion of image and medical ontology features"""
def __init__(self):
self.concept_network = self._build_concept_network()
self.attention_maps = {}
def generate_explanations(self,
image_features: torch.Tensor,
medical_concepts: torch.Tensor,
prediction: torch.Tensor) -> Dict[str, Any]:
explanations = {
"concept_alignment": self._explain_concept_alignment(
image_features, medical_concepts
),
"feature_importance": self._compute_feature_importance(
image_features, prediction
),
"ontology_reasoning": self._explain_ontological_reasoning(
medical_concepts, prediction
)
}
return explanations4. Evaluation Metrics
A. Multi-Modal Evaluation Framework
class MultiModalEvaluator:
"""Evaluates fusion of image and medical ontologies"""
def __init__(self):
self.metrics = {
'accuracy': [],
'precision': [],
'recall': [],
'f1_score': [],
'concept_alignment_score': []
}
def evaluate(self,
predictions: torch.Tensor,
targets: torch.Tensor,
concept_alignment_scores: Optional[Dict] = None):
"""Compute evaluation metrics"""
# Standard classification metrics
self.metrics['accuracy'].append(accuracy_score(targets, predictions > 0.5))
precision, recall, f1, _ = precision_recall_fscore_support(
targets, predictions > 0.5, average='binary'
)
self.metrics['precision'].append(precision)
self.metrics['recall'].append(recall)
self.metrics['f1_score'].append(f1)
# Concept alignment metrics if available
if concept_alignment_scores:
self.metrics['concept_alignment_score'].append(
np.mean(list(concept_alignment_scores.values()))
)
def get_summary(self) -> Dict[str, float]:
"""Return mean of all metrics"""
return {
metric: np.mean(values)
for metric, values in self.metrics.items()
if values # Only include metrics that have values
}5. Practical Applications
A. Medical Image Analysis
- Pathology: Combine histological features with disease ontologies
- Radiology: Fuse imaging findings with anatomical ontologies
- Ophthalmology: Link retinal features with eye disease ontologies
B. Industrial Applications
- Manufacturing: Combine visual inspection with quality control ontologies
- Autonomous Vehicles: Fuse sensor data with traffic rule ontologies
- Robotics: Link visual perception with task ontologies
6. Challenges and Future Directions
Key Challenges
- Semantic Gap: Bridging low-level visual features with high-level concepts
- Scalability: Handling large-scale ontologies efficiently
- Explainability: Making fusion decisions interpretable to domain experts
- Data Scarcity: Limited annotated multimodal datasets
Future Directions
- Self-supervised Learning: Leveraging unlabeled multimodal data
- Neuro-Symbolic Integration: Combining neural networks with symbolic reasoning
- Federated Learning: Privacy-preserving distributed learning across institutions
- Cross-modal Pretraining: Learning joint representations from multiple modalities