Generate accurate, high-quality medical videos for clinical education and documentation by leveraging large-scale annotated medical datasets with domain-specific fine-tuning on video diffusion models.
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Generate accurate, high-quality medical videos for clinical education and documentation by leveraging large-scale annotated medical datasets with domain-specific fine-tuning on video diffusion models.
Medical Video Generation: Synthesizing Clinically Accurate Surgical and Imaging Videos
Medical video generation is critical for surgical training, patient education, and clinical documentation. However, general-purpose video generation models fail on medical content because they lack domain-specific knowledge of anatomical structures, procedural sequences, and clinical accuracy requirements. Standard video synthesis models trained on general web data cannot reliably generate medically accurate content—they may produce anatomically impossible procedures or omit critical steps.
MedGen addresses this through two key innovations: a large-scale, carefully curated medical video dataset (MedVideoCap-55K) with detailed captions, and domain-adapted fine-tuning of video diffusion models using LoRA. This approach enables generation of clinically accurate medical videos while maintaining computational efficiency, outperforming general models on medical content while matching commercial systems in quality.
Core Concept
Medical video generation requires training diffusion models on domain-specific data that captures the diversity of clinical contexts. Rather than collecting raw videos, the approach focuses on curation and annotation quality: filtering out artifacts, subtitles, and encoding issues; generating detailed multimodal captions describing procedures and imaging findings; and evaluating outputs through medical domain experts.
The key insight is that medical accuracy emerges from combining three elements: (1) sufficient quantity of diverse medical clips spanning clinical practice, imaging, and education, (2) rigorous quality filtering to remove technical artifacts that would confuse models, and (3) detailed text descriptions that ground video generation in clinical terminology and procedural knowledge.
Architecture Overview
Dataset Curation Pipeline: Source collection from diverse medical domains → optical character recognition (OCR) filtering to remove subtitles → aesthetic quality assessment → technical encoding validation → duplicate removal
Caption Generation: Multimodal language models process videos to generate detailed clinical descriptions capturing procedures, anatomical regions, and clinical context
Video Diffusion Model Backbone: HunyuanVideo or similar video generation foundation model serving as base architecture
Parameter-Efficient Fine-tuning: LoRA adapters applied to attention layers to efficiently inject medical knowledge without retraining the full model
Evaluation Framework: Automated metrics (Med-VBench) plus human evaluation by radiologists and clinicians assessing medical accuracy, text-image alignment, and visual quality
Implementation
The following demonstrates how to build a medical video generation system using a base diffusion model and domain-specific fine-tuning.
Step 1: Dataset Preparation and Filtering
This code applies quality filtering stages to remove unsuitable medical videos before training.
import cv2
import numpy as np
from pytesseract import pytesseract
from PIL import Image
classMedicalVideoFilter:
def__init__(self, ocr_threshold=0.3, quality_min=0.6):
self.ocr_threshold = ocr_threshold
self.quality_min = quality_min
defdetect_subtitles(self, frame):
"""Use OCR to detect subtitle presence in video frames."""
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
text = pytesseract.image_to_string(gray)
# Check if substantial text detected (likely subtitles)returnlen(text.split()) > self.ocr_threshold * frame.shape[0]
defassess_technical_quality(self, video_path, sample_frames=10):
"""Evaluate technical encoding quality using sharpness and noise metrics."""
cap = cv2.VideoCapture(video_path)
frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
indices = np.linspace(0, frame_count - 1, sample_frames, dtype=int)
sharpness_scores = []
for idx in indices:
cap.set(cv2.CAP_PROP_POS_FRAMES, idx)
ret, frame = cap.read()
if ret:
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# Laplacian variance measures sharpness
sharpness = cv2.Laplacian(gray, cv2.CV_64F).var()
sharpness_scores.append(sharpness)
cap.release()
mean_sharpness = np.mean(sharpness_scores) if sharpness_scores else0return mean_sharpness / 1000# Normalize to 0-1 rangedeffilter_batch(self, video_paths):
"""Filter videos based on subtitle presence and technical quality."""
filtered = []
for path in video_paths:
cap = cv2.VideoCapture(path)
has_subtitle = False# Sample frames for subtitle detection
frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
for i inrange(0, min(frame_count, 30), 10):
cap.set(cv2.CAP_PROP_POS_FRAMES, i)
ret, frame = cap.read()
if ret andself.detect_subtitles(frame):
has_subtitle = Truebreak
cap.release()
ifnot has_subtitle:
quality = self.assess_technical_quality(path)
if quality > self.quality_min:
filtered.append(path)
return filtered
Step 2: Caption Generation Using Multimodal LLMs
This generates detailed medical descriptions for filtered videos using multimodal language models.
from transformers import AutoProcessor, LlavaForConditionalGeneration
import torch
classMedicalCaptionGenerator:
def__init__(self, model_name="llava-1.5-7b-hf"):
self.processor = AutoProcessor.from_pretrained(model_name)
self.model = LlavaForConditionalGeneration.from_pretrained(
model_name, torch_dtype=torch.float16, device_map="auto"
)
self.device = self.model.device
defgenerate_caption(self, video_path, num_frames=8):
"""Extract frames and generate medical description."""import cv2
cap = cv2.VideoCapture(video_path)
frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
frame_indices = np.linspace(0, frame_count - 1, num_frames, dtype=int)
frames = []
for idx in frame_indices:
cap.set(cv2.CAP_PROP_POS_FRAMES, idx)
ret, frame = cap.read()
if ret:
frames.append(Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)))
cap.release()
# Aggregate frames into a grid for captioningfrom PIL import Image as PILImage
grid = PILImage.new('RGB', (640, 480 * num_frames // 2))
for i, frame inenumerate(frames):
frame_resized = frame.resize((320, 240))
grid.paste(frame_resized, ((i % 2) * 320, (i // 2) * 240))
prompt = "Describe this medical video in clinical detail. Include: 1) anatomical structures visible, 2) medical procedures or findings, 3) clinical context."
inputs = self.processor(prompt, grid, return_tensors="pt").to(self.device)
with torch.no_grad():
output = self.model.generate(**inputs, max_new_tokens=256, temperature=0.7)
caption = self.processor.decode(output[0], skip_special_tokens=True)
return caption
Step 3: LoRA Fine-tuning for Medical Domain Adaptation
This applies parameter-efficient fine-tuning to inject medical knowledge into a base video diffusion model.
from diffusers import DiffusionPipeline
from peft import get_peft_model, LoraConfig, TaskType
import torch
from torch.utils.data import Dataset, DataLoader
classMedicalVideoDataset(Dataset):
def__init__(self, video_paths, captions, num_frames=16):
self.video_paths = video_paths
self.captions = captions
self.num_frames = num_frames
def__len__(self):
returnlen(self.video_paths)
def__getitem__(self, idx):
import cv2
cap = cv2.VideoCapture(self.video_paths[idx])
frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
indices = np.linspace(0, frame_count - 1, self.num_frames, dtype=int)
frames = []
for frame_idx in indices:
cap.set(cv2.CAP_PROP_POS_FRAMES, frame_idx)
ret, frame = cap.read()
if ret:
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
frame = torch.tensor(frame).permute(2, 0, 1).float() / 255.0
frames.append(frame)
cap.release()
frames = torch.stack(frames) if frames else torch.zeros(self.num_frames, 3, 512, 512)
caption = self.captions[idx]
return {"video": frames, "caption": caption}
classMedicalVideoFinetuner:
def__init__(self, base_model="hunyuan-video", learning_rate=1e-4):
self.pipeline = DiffusionPipeline.from_pretrained(base_model)
self.learning_rate = learning_rate
# Configure LoRA for efficient adaptation
lora_config = LoraConfig(
r=16,
lora_alpha=32,
target_modules=["to_q", "to_v"],
lora_dropout=0.05,
bias="none",
task_type=TaskType.CAUSAL_LM
)
self.model = get_peft_model(self.pipeline.unet, lora_config)
self.optimizer = torch.optim.AdamW(self.model.parameters(), lr=learning_rate)
deffinetune(self, train_loader, num_epochs=3):
"""Fine-tune diffusion model on medical video data."""self.model.train()
for epoch inrange(num_epochs):
total_loss = 0for batch in train_loader:
videos = batch["video"].to(self.model.device)
captions = batch["caption"]
# Encode text descriptions
text_embeddings = self.pipeline.encode_prompt(captions)[0]
# Add noise to videos and train denoiser
timesteps = torch.randint(0, 1000, (videos.shape[0],))
noise = torch.randn_like(videos)
noisy_videos = self.pipeline.scheduler.add_noise(videos, noise, timesteps)
# Predict noise and compute loss
pred_noise = self.model(noisy_videos, timesteps, text_embeddings).sample
loss = torch.nn.functional.mse_loss(pred_noise, noise)
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
total_loss += loss.item()
print(f"Epoch {epoch + 1}/{num_epochs}, Loss: {total_loss / len(train_loader):.4f}")
Practical Guidance
Hyperparameters and Configuration
Parameter
Recommended Value
Range
Notes
LoRA Rank (r)
16-32
8-64
Higher rank captures more medical specificity but increases compute
LoRA Alpha
2x rank
rank-4x rank
Controls LoRA strength; 2x is conservative baseline
Learning Rate
1e-4
1e-5 to 1e-3
Lower rates prevent catastrophic forgetting of base knowledge
Num Captions per Video
1-3
1-5
Multiple angles help but diminishing returns after 3
Video Frames per Clip
16-32
8-64
Higher resolution captures fine procedural details
Quality Filter Threshold
0.6-0.8
0.4-0.9
More aggressive filtering (0.8+) improves quality but reduces data
When to Use
Medical education platforms needing synthetic surgical procedure videos
Clinical documentation where procedural video examples are needed for training
Research applications requiring diverse, controlled medical scenarios for testing
Rare procedure simulation when actual footage is unavailable or ethically sensitive
When NOT to Use
Do not use for diagnostic decision-making without expert human validation
Avoid using generated videos as ground truth for clinical research without human review
Not suitable for applications requiring pixel-perfect anatomical accuracy in rare pathology
Do not deploy as a replacement for real clinical footage in high-stakes training contexts without validation
Common Pitfalls
Insufficient medical curation: Generic video filtering removes important clinical context. Use domain expert review for quality assessment rather than purely automated metrics.
Overfitting to specific procedures: Training on limited procedural domains (e.g., only laparoscopy) without diversity creates poor generalization. Ensure dataset spans multiple specialties.
Ignoring caption quality: Low-quality captions from automated generation propagate errors to generated videos. Validate captions with clinicians before large-scale training.
Ignoring evaluation rigor: Automated metrics miss clinically important errors. Always include human evaluation by medical professionals, not just benchmark scores.
Data contamination: Ensure source videos don't contain sensitive patient information before using in training. Implement privacy review in the filtering pipeline.
