sda-qec-diffusion-quantum-medical

SDA-QEC (Simplified Diffusion Augmentation with Quantum-Enhanced Classification) methodology for medical image diagnosis under severe class imbalance. Integrates lightweight diffusion augmentation for minority class rebalancing with quantum feature layers for high-dimensional discrimination. Use when: medical image classification with imbalanced data, quantum-enhanced feature mapping, diffusion data augmentation for healthcare AI. Trigger words: SDA-QEC, diffusion augmentation, quantum-enhanced classification, medical image imbalance, minority class rebalancing, Hilbert space feature mapping.

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Repositorio: hiyenwong/ai_collection
Última actividad en el origen: 10 de julio de 2026 a las 10:08
Idioma detectado de SKILL.md: inglés
Estrellas: 2
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SDA-QEC: Simplified Diffusion Augmentation with Quantum-Enhanced Classification

Source

Paper: Generative Diffusion Augmentation with Quantum-Enhanced Discrimination for Medical Image Diagnosis

arXiv: 2601.18556v1 (2026-01-26)

Authors: Jingsong Xia, Siqi Wang

Categories: cs.CV, cs.LG

Methodology

A two-stage framework that addresses class imbalance in medical imaging through generative augmentation followed by quantum-enhanced feature discrimination.

Core Architecture

┌──────────────────────────────────────────────┐ │ Stage 1: Simplified Diffusion Augmentor │ │ ┌────────────────────────────────────────┐ │ │ │ Lightweight diffusion model │ │ │ │ - Generates synthetic minority samples │ │ │ │ - Rebalances training distribution │ │ │ └────────────────────────────────────────┘ │ │ ↓ │ │ Balanced dataset (original + synthetic) │ └──────────────────────────────────────────────┘ ↓ ┌──────────────────────────────────────────────┐ │ Stage 2: Quantum-Enhanced Classifier │ │ ┌────────────────────────────────────────┐ │ │ │ MobileNetV2 backbone │ │ │ │ + Quantum Feature Layer │ │ │ │ - High-dimensional Hilbert space map │ │ │ │ - Enhanced discriminative capability │ │ │ └────────────────────────────────────────┘ │ │ ↓ │ │ 98.33% accuracy, 98.78% AUC, 98.33% F1 │ │ Sensitivity=98.33%, Specificity=98.33% │ └──────────────────────────────────────────────┘

SDA-QEC: Simplified Diffusion Augmentation with Quantum-Enhanced Classification

Source

Paper: Generative Diffusion Augmentation with Quantum-Enhanced Discrimination for Medical Image Diagnosis

arXiv: 2601.18556v1 (2026-01-26)

Authors: Jingsong Xia, Siqi Wang

Categories: cs.CV, cs.LG

Methodology

A two-stage framework that addresses class imbalance in medical imaging through generative augmentation followed by quantum-enhanced feature discrimination.

Core Architecture

import torch import torch.nn as nn from diffusers import DDPMPipeline class SimplifiedDiffusionAugmentor: """Lightweight diffusion model for minority class augmentation.""" def __init__(self, num_classes, target_ratio=1.0): self.models = {c: DDPMPipeline(...) for c in range(num_classes)} self.target_ratio = target_ratio def augment(self, dataset, minority_classes): """Generate synthetic samples for minority classes.""" for cls in minority_classes: n_needed = self._calculate_needed(dataset, cls) synthetic = self.models[cls].generate(n_needed) dataset.add_samples(synthetic, cls) return dataset class QuantumFeatureLayer(nn.Module): """Quantum feature mapping for enhanced discrimination.""" def __init__(self, input_dim, num_qubits): super().__init__() self.num_qubits = num_qubits # Quantum circuit parameterization self.rotation_params = nn.Parameter(torch.randn(num_qubits)) self.entangling_angles = nn.Parameter(torch.randn(num_qubits // 2)) def forward(self, x): # Map classical features to quantum state # Apply parameterized rotations and entanglement # Measure in computational basis for discriminative features quantum_features = self._apply_quantum_circuit(x) return quantum_features class SDA_QEC(nn.Module): """Complete SDA-QEC architecture.""" def __init__(self, num_classes, num_qubits=4): super().__init__() self.backbone = mobilenet_v2(pretrained=True) self.backbone.classifier = nn.Identity() self.quantum_layer = QuantumFeatureLayer(1280, num_qubits) self.classifier = nn.Linear(num_qubits, num_classes) self.augmentor = SimplifiedDiffusionAugmentor(num_classes) def forward(self, x): features = self.backbone(x) q_features = self.quantum_layer(features) return self.classifier(q_features)

sda-qec-diffusion-quantum-medical

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SDA-QEC: Simplified Diffusion Augmentation with Quantum-Enhanced Classification

Source

Methodology

Core Architecture

SDA-QEC: Simplified Diffusion Augmentation with Quantum-Enhanced Classification

Source

Methodology

Core Architecture

Key Components

Performance Results

Implementation Pattern

Best Practices

Application Domains

Activation Keywords

sda-qec-diffusion-quantum-medical

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Más de este repositorio

Más de este repositorio

SDA-QEC: Simplified Diffusion Augmentation with Quantum-Enhanced Classification

Source

Methodology

Core Architecture

SDA-QEC: Simplified Diffusion Augmentation with Quantum-Enhanced Classification

Source

Methodology

Core Architecture

Key Components

Performance Results

Implementation Pattern

Best Practices

Application Domains

Activation Keywords