M3D: Advancing 3D Medical Image Analysis with Multi-Modal Large Language Models

M3D is the pioneering and comprehensive series of work on the multi-modal large language model for 3D medical analysis, including:

M3D-Data: the largest-scale open-source 3D medical dataset, consists of 120K image-text pairs and 662K instruction-response pairs;
M3D-LaMed: the versatile multi-modal models with M3D-CLIP pretrained vision encoder, which are capable of tasks such as image-text retrieval, report generation, visual question answering, positioning and segmentation;
M3D-Bench: the most comprehensive automatic evaluation benchmark covers 8 tasks.

Notifications

📢 [2024.06.12]

🔥🔥🔥 We released an online demo. Welcome everyone to try it now!
We released a light but strong model, M3D-LaMed-Phi-3-4B. After simple testing, it outperformed M3D-LaMed-Llama-2-7B. We are conducting detailed experiments. Please try it first.
We found that the previous M3D-LaMed-Llama-2-7B model had problems in the segmentation task. We have fixed this problem and will re-release the new model in the next few days.

News

[x] [2024.06.14] We released a light but strong model M3D-LaMed-Phi-3-4B and an online demo.
[x] [2024.04.28] We released the data, code, and model.

Quickstart

Here, we can easily use our model based on Hugging Face.

import numpy as np
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
import simple_slice_viewer as ssv
import SimpleITK as sikt

device = torch.device('cuda') # 'cpu', 'cuda'
dtype = torch.bfloat16 # or bfloat16, float16, float32

model_name_or_path = 'GoodBaiBai88/M3D-LaMed-Phi-3-4B'
proj_out_num = 256

# Prepare your 3D medical image:
# 1. The image shape needs to be processed as 1*32*256*256, consider resize and other methods.
# 2. The image needs to be normalized to 0-1, consider Min-Max Normalization.
# 3. The image format needs to be converted to .npy 
# 4. Although we did not train on 2D images, in theory, the 2D image can be interpolated to the shape of 1*32*256*256 for input.
image_path = "./Data/data/examples/example_03.npy"

model = AutoModelForCausalLM.from_pretrained(
    model_name_or_path,
    torch_dtype=dtype,
    device_map='auto',
    trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(
    model_name_or_path,
    model_max_length=512,
    padding_side="right",
    use_fast=False,
    trust_remote_code=True
)

model = model.to(device=device)

# question = "Can you provide a caption consists of findings for this medical image?"
question = "What is liver in this image? Please output the segmentation mask."
# question = "What is liver in this image? Please output the box."

image_tokens = "<im_patch>" * proj_out_num
input_txt = image_tokens + question
input_id = tokenizer(input_txt, return_tensors="pt")['input_ids'].to(device=device)

image_np = np.load(image_path)
image_pt = torch.from_numpy(image_np).unsqueeze(0).to(dtype=dtype, device=device)

# generation = model.generate(image_pt, input_id, max_new_tokens=256, do_sample=True, top_p=0.9, temperature=1.0)
generation, seg_logit = model.generate(image_pt, input_id, seg_enable=True, max_new_tokens=256, do_sample=True, top_p=0.9, temperature=1.0)

generated_texts = tokenizer.batch_decode(generation, skip_special_tokens=True)
seg_mask = (torch.sigmoid(seg_logit) > 0.5) * 1.0

print('question', question)
print('generated_texts', generated_texts[0])

image = sikt.GetImageFromArray(image_np)
ssv.display(image)
seg = sikt.GetImageFromArray(seg_mask.cpu().numpy()[0])
ssv.display(seg)

Model

Model	Download Link
M3D-CLIP	HuggingFace, [ModelScope]()
M3D-LaMed-Phi-3-4B	HuggingFace, [ModelScope]()
M3D-LaMed-Llama-2-7B	HuggingFace, [ModelScope]()

Installation

git clone https://github.com/BAAI-DCAI/M3D.git
pip install -r requirements.txt

Data

M3D-Data supports the training and benchmark, which consist of 4 types of data:

Dataset	Type	Images	Texts	Download Link
M3D-Cap	3D image-text pairs	120,092	42,496	HuggingFace, ModelScope
M3D-VQA	3D images, questions, and answers	96,170	509,755	HuggingFace, ModelScope
M3D-Seg	3D images, category text, and segmentation masks	5,772	149,196	HuggingFace, ModelScope
M3D-RefSeg	3D images, questions, answers, and segmentation masks	210	2,778	HuggingFace, ModelScope

Please follow the instructions for each dataset to download and preprocess. You can find the preprocess file named m3d_xx_data_prepare.py in dataset description or Data/process/ for M3D-Cap, M3D-Seg and M3D-RefSeg. We recommend saving the downloaded and processed dataset to Data/data/.

Training

Pretrained Weights

To train M3D-LaMed, you need to prepare some pretrained weights for better performance and faster convergence.

Vision encoder

We recommend downloading the medical 3D ViT weight pretrained_ViT.bin from M3D-CLIP directly. Or you can also pretrain the 3D ViT by yourself by

sh LaMed/script/train_clip.sh

LLM

Phi-3-4B: Download and follow here. Llama-2-7B: Download and follow here.

Segmentation module

SegVol: Download and follow here.

Training

Our training consists of two steps.

Step 1: Pretrain
Step 2: Visual Instruction Tuning

Configuration

We suggest using accelerate to train. It was developed by Hugging Face and conveniently supports common training strategies such as distributed training, mixed precision, DeepSpeed, etc. It should be configured on first use:

accelerate config

Please follow the configuration guide and we can choose the appropriate training strategy. We recommend using bf16 and Deepspeed for acceleration, and the ZeRO type depends on your own situation.

If you don't know how to configure it, we provide a simple configuration default_config.yaml for your reference.

default_config.yaml

```bash compute_environment: LOCAL_MACHINE debug: false deepspeed_config: gradient_accumulation_steps: 1 zero3_init_flag: false zero_stage: 0 distributed_type: DEEPSPEED downcast_bf16: 'no' machine_rank: 0 main_training_function: main mixed_precision: bf16 num_machines: 1 num_processes: 8 rdzv_backend: static same_network: true tpu_env: [] tpu_use_cluster: false tpu_use_sudo: false use_cpu: false ```

Step 1: Pretrain

We should align vision and language with image-text data, that is, only train mm_projector and freeze the vision encoder and LLM.

Please update LLM path --model_name_or_path and vision encoder path --pretrain_vision_model, respectively. Update --output_dir to specify the output path of the model. Then run the script by:

sh LaMed/script/pretrain.sh

Step 2: Visual Instruction Tuning

Visual instruction tuning through multi-task data of image-text pairs, VQA, positioning and segmentation, that is, only perform LoRA training on LLM, and unfreeze all other models.

Please update LLM path --model_name_or_path, vision encoder path --pretrain_vision_model, model path saved by Step 1--pretrain_mm_mlp_adapter and segmentation module path --pretrain_seg_module, respectively. Update --output_dir to specify the output path of the model. Then run the script by:

sh LaMed/script/finetune_lora.sh

Merge LoRA Weight

Merge the LoRA weights of model_with_lora.bin, save the final model into your desired path in the Hugging Face format:

CUDA_VISIBLE_DEVICES="" python merge_lora_weights_and_save_hf_model.py \
  --version="" \
  --model_type="" \
  --model_with_lora="PATH_TO_model_with_lora.bin" \
  --output_dir="PATH_TO_SAVED_MODEL"

Benchmark

We propose the most comprehensive automatic evaluation benchmark covers 8 tasks in 3D medical, including image-text retrival, report generation, closed-ended VQA, open-ended VQA, referring expression comprehension, referring expression generation, semantic segmentation, referring expression segmentation.

Evaluation

We can directly evaluate each task by running:

CUDA_VISIBLE_DEVICES="" python Bench/eval/eval_TASK.py

We also provide a more accurate automatic evaluation of report generation tasks using LLM, after modifying the file_path, please run:

CUDA_VISIBLE_DEVICES="" python Bench/eval/eval_with_llm.py

Citation

If our dataset or project are helpful to you, please consider citing:

@misc{bai2024m3d,
      title={M3D: Advancing 3D Medical Image Analysis with Multi-Modal Large Language Models}, 
      author={Fan Bai and Yuxin Du and Tiejun Huang and Max Q. -H. Meng and Bo Zhao},
      year={2024},
      eprint={2404.00578},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@article{du2023segvol,
  title={SegVol: Universal and Interactive Volumetric Medical Image Segmentation},
  author={Du, Yuxin and Bai, Fan and Huang, Tiejun and Zhao, Bo},
  journal={arXiv preprint arXiv:2311.13385},
  year={2023}
}

Acknowledgement

We appreciate open source projects including: LLaVA, LISA, SegVol.

BAAI-DCAI / M3D

readme

M3D: Advancing 3D Medical Image Analysis with Multi-Modal Large Language Models

Notifications

News

Quickstart

Model

Installation

Data

Training

Pretrained Weights

Vision encoder

LLM

Segmentation module

Training

Configuration

Step 1: Pretrain

Step 2: Visual Instruction Tuning

Merge LoRA Weight

Benchmark

Evaluation

Citation

Acknowledgement