This repository provides LoRA adapters fine-tuned on top of google/medgemma-1.5-4b-it for exploring temporal change detection in dermatoscopic image pairs.
The project investigates whether lightweight parameter-efficient fine-tuning can adapt a multimodal medical foundation model to a novel temporal reasoning task.
Model Details
Model Description
This repository contains LoRA adapters only, not a full model checkpoint.
Developed and shared by: Dung Claire Tran (@dunktra)
Research and experimentation with temporal reasoning in medical imaging
Evaluation of LoRA fine-tuning feasibility on multimodal medical foundation models
Educational and benchmarking purposes
Out-of-Scope Use
Clinical diagnosis or medical decision-making
Deployment in real-world healthcare settings without clinical validation
This model is not a medical device.
Limitations
Fine-tuning effects may not surface when using keyword-based label extraction
Binary classification may mask improvements in:
reasoning structure
explanatory language
uncertainty expression
Synthetic temporal data limits real-world generalization
Inherits all limitations of the base MedGemma model
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
How to Get Started with the Model
Use the code below to get started with the model.
from transformers import AutoModelForVision2Seq, AutoProcessor
from peft import PeftModel
import torch
base_model = AutoModelForVision2Seq.from_pretrained(
"google/medgemma-1.5-4b-it",
torch_dtype=torch.bfloat16,
device_map="auto"
)
model = PeftModel.from_pretrained(
base_model,
"dunktra/medgemma-temporal-lora"
)
processor = AutoProcessor.from_pretrained(
"dunktra/medgemma-temporal-lora"
)
Description: Synthetic before/after dermatoscopic image pairs labeled for temporal change
Splits:
Training: ~630 pairs
Validation: ~135 pairs
Test: 135 pairs
Note:The dataset consists of synthetic temporal pairs, not real longitudinal patient data.
Training Configuration
LoRA Rank (r): 8
LoRA Alpha: 16
Target Modules: q_proj, k_proj, v_proj, o_proj
LoRA Dropout: 0.05
Epochs: 3
Effective Batch Size: 16
Learning Rate: 2e-4
Precision: bfloat16
Frameworks: Transformers + PEFT
Evaluation
Metrics
Precision
Recall
F1 score (binary classification)
Results (Test Set: 135 temporal pairs)
Metric
Base MedGemma
Fine-Tuned (LoRA)
Change
F1 Score
0.8797
0.8797
+0.00%
Precision
0.7852
0.7852
+0.00%
Recall
1.0000
1.0000
+0.00%
LoRA fine-tuning did not yield measurable improvements under the current evaluation protocol.
Note: Although LoRA fine-tuning did not improve aggregate F1 on the held-out test set, analysis revealed that both the base and fine-tuned models collapsed to a high-recall regime, predicting “change” for all examples. This indicates that the primary performance bottleneck lies in task framing and decision extraction rather than model capacity. The experiment demonstrates stable LoRA adaptation without regression and highlights the importance of evaluation design in generative medical VLMs.
Qualitative Analysis
No test cases were found where the fine-tuned model corrected errors made by the base model.
Fine-tuning did not alter binary decision outcomes given the current response-parsing heuristic.
License
This adapter inherits the license and usage restrictions of:
google/medgemma-1.5-4b-it
Underlying datasets used by the base model
Non-commercial research use only.
Acknowledgements
Google MedGemma team
PEFT / Hugging Face ecosystem
Created for the MedGemma Impact Challenge 2026 – Novel Task Exploration.
