Med-MAT: On the Compositional Generalization of Multimodal LLMs for Medical Imaging

✨ Latest News

• [12/31/2024]: Release the paper On the Compositional Generalization of Multimodal LLMs for Medical Imaging, in which we found that multimodal LLMs can use compositional generalization to understand unseen medical images.
• [12/27/2024]: Release the classification datasets in Med-MAT. The detection/segmentation datasets are coming soon.

⚡ Introduction

Welcome to the repository of Med-MAT, a VQA dataset consisting of 106 open-source medical datasets, which we hope will advance generalization experiments and aid in training powerful medical multimodal large language models (MLLMs).

Through this dataset, we have demonstrated that Compositional Generalization (CG) is one of the key mechanisms for MLLMs to understand unseen images, enabling them to handle unfamiliar images and achieve data-efficient training.

Here is a list of what has been released:

1. QA Pairs for 106 Medical Datasets: Image-label pairs converted into VQA pairs for MLLM training.
2. QA Pairs for 53 Aggregated Subsets: Datasets categorized by Modality, Anatomical Area, and Task (MAT), with identical entries merged into subsets.
3. Image Download Links: Some datasets cannot be shared due to licensing. Users can download them to specified directories.

💭 QA Pairs Construction

To enable MLLMs to directly train and test on Med-MAT, the image-label pairs were converted into a Visual Question-Answering (VQA) format. The process involves the following steps:

1. Task Definition: Each subset was manually assigned 6 instructions to guide the MLLM in answering the task related to the subset.
2. Conversion to VQA Format: All image-label pairs were converted into single-choice questions with up to four answer options.
3. Distractor Selection: Distractor options were randomly drawn from other labels within the subset to ensure variety.
4. Final Dataset: The resulting dataset consisted of VQA pairs, where each image is paired with a question and four options, one of which is correct.

📚 Data

You can access the QA pairs of Med-MAT in this page.

The tables below record the download URLs for the images and QA pairs for each dataset and subset. If you only wish to use part of Med-MAT, you can selectively download the corresponding data.

Original_Medical_Datasets

Click to view the details of 106 Medical Datasets

No.	Name with link	Modality	Area	Task	QA
1	Intel and MobileODT Cervical Screening	Co	Cervix	Cervix Type in Screening	HF
2	CT Kindney Dataset	CT	Kidney	Normal or Cyst or Tumor	HF
3	SARS-COV-2 Ct-Scan	CT	Lung	COVID19, Classification Dataset	HF
4	COVID CT COVID-CT	CT	Lung	COVID19, Classification Dataset.	HF
5	Chest CT-Scan	CT	Lung	Cancer, 3 Cancer Categories, Multiple Classification Dataset	HF
6	COVID-19-CT SCAN IMAGES	CT	Lung	COVID19, Classification	HF
7	Head CT	CT	Brain	Head Hemorrhage	HF
8	CT of Brain	CT	Brain	Head Cancer	HF
9	MED-NODE	Der	Skin	Melanoma or Naevus	HF
10	ISIC 2020	Der	Skin	Melanoma, Benign or Malignant	HF
11	PED-UFES-20	Der	Skin	Skin Multi Classification	HF
12	Web-scraped Skin Image	Der	Skin	Skin Desease Multi Classification	HF
13	ISBI 2016	Der	Skin	Skin Lesion Classification	HF
14	ISIC 2019	Der	Skin	Skin Desease Multi Classification	HF
15	Skin Cancer ISIC	Der	Skin	Skin Cancer Multi Classification	HF
16	Dental Condition Dataset	DP	Teeth	Teeth condition classification	HF
17	Oral Cancer Dataset	DP	Teeth	Oral cancer Classification	HF
18	The Nerthus Dataset	End	Intestine	Cleanliness level	HF
19	Endoscopic Bladder Tissue	End	Bladder	Canser Degree Classification	HF
20	Kvasir	End	Intestine	Multi Disease Classification	HF
21	ACRIMA	FP	Fundus	Glaucoma	HF
22	Augemnted ocular diseases AOD	FP	Fundus	Multi Classification of eye diseases	HF
23	JSIEC	FP	Fundus	Multi Classification of eye diseases	HF
24	Multi-Label Retinal Diseases	FP	Fundus	Multi Classification of eye diseases	HF
25	RFMiD 2.0	FP	Fundus	Multi Classification of eye diseases	HF
26	ToxoFundus(Data Processed Paper)	FP	Fundus	Ocular toxoplasmosis	HF
27	ToxoFundus(Data Raw 6class All)	FP	Fundus	Ocular toxoplasmosis	HF
28	Adam dataset	FP	Fundus	Age-related Macular Degeneration	HF
29	APTOS 2019 Blindness	FP	Fundus	Blindness Level Identification 0~4	HF
30	DRIMBD	FP	Fundus	Quality Testing of Retinal Images	HF
31	Glaucoma Detection	FP	Fundus	Glaucoma Classification	HF
32	AIROGS	FP	Fundus	Glaucoma Classification	HF
33	ICPR-HEp-2	Mic	Cell	Multi Classification	HF
34	SICAPv2	Mic	Cell	Cancer Degree Classification	HF
35	Blood Cell Images	Mic	Cell	Blood Cell Classificaion (Multi)	HF
36	BreakHis	Mic	Cell	Cell type and beginormag	HF
37	Chaoyang	Mic	Cell	Multi Classification of pathologists	HF
38	HuSHeM	Mic	Cell	Sperm Head Morphology Classificaion	HF
39	Bone Marrow Cell Classification	Mic	Cell	Bone Marrow Cell Classification	HF
40	NCT-CRC-HE-100K	Mic	Cell	Multi Classification	HF
41	Malignant Lymphoma Classification	Mic	Cell	Multi Classification	HF
42	Histopathologic Cancer Detection	Mic	Cell	Cancer Classification	HF
43	LC25000	Mic	Cell	Multi Classification of Lung and Colon	HF
44	Brain Tumor 17 Classes	MRI	Brain	Multi Classification	HF
45	Tumor Classification	MRI	Brain	Pituitary or Glioma or Meningioma or Notumor	HF
46	Malignant Lymphoma Classification	OCT	Retina	Multi Classification of eye diseases	HF
47	Retinal OCT-C8	OCT	Retina	Multi Classification of eye diseases	HF
48	BUSI	US	Breast	Breast Cancer	HF
49	Digital Knee X-Ray Images	X-Ray	Bones	Degree Classification of Knee	HF
50	Bone Fracture Multi-Region X-ray Data	X-Ray	Bones	Fractured Classification	HF
51	Fracture detection	X-Ray	Bones	Fractured Classification	HF
52	The vertebrae X-ray image	X-Ray	Bones	Vertebrae	HF
53	Knee Osteoarthritis Dataset	X-Ray	Bones	Knee Osteoarthritis with severity grading	HF
54	Shenzhen Chest X-Ray Set	X-Ray	Lung	COVID19, Classification Dataset.	HF
55	Chest X-ray PD	X-Ray	Lung	COVID and Pneumonia	HF
56	COVID-19 CHEST X-RAY DATABASE	X-Ray	Lung	COVID and Pneumonia	HF
	COVIDGR	X-Ray	Lung	COVID19, Classification	HF
58	MIAS	X-Ray	Breast	Multi Classification of Breast	HF
59	Tuberculosis Chest X-Ray Database	X-Ray	Lung	Tuberculosis	HF
60	Pediatric Pneumonia Chest X-Ray	X-Ray	Lung	Pneumonia Classification	HF
61	Random Sample of NIH Chest X-Ray Dataset	X-Ray	Chest	Multi Classificaiton of Chest	HF
62	CoronaHack-Chest X-Ray	X-Ray	Lung	Pnemonia Classifcition with Virus type	HF
63	Brain Tumor Dataset	X-Ray	Brain	Tumor Classification	HF
64	Fitzpatrick 17k (Nine Labels)	Der	Skin	Multi Classification	HF
65	BioMediTech	Mic	Cell	Multi Classification	HF
66	Diabetic retinopathy	FP	Fundus	Diabetic Retinopathy Level	HF
67	Leukemia	Mic	Cell	Cancer Classification	HF
68	ODIR-5K	FP	Fundus	Multiple Labels Classification	HF
69	Arthrosis	X-Ray	Bones	Bone Age Classification	HF
70	HSA-NRL	Mic	Cell	Multi Classification of pathologists	HF
71	ISIC 2018 (Task 3)	Der	Skin	Multi Classification	HF
72	ISIC 2017 (Task 3)	Der	Skin	Multi Classification	HF
73	ChestX-Det	X-Ray	Chest	Multi Classification	HF
74	Monkeypox Skin Lesion Dataset	Der	Skin	Only Monkeypox	HF
75	Cataract Dataset	FP	Fundus	Multi Classification	HF
76	ChestX-rays IndianaUniversity	X-Ray	Chest	Multi-label Classification	HF
77	CheXpert v1.0 small	X-Ray	Chest	Multi-label Classification	HF
78	CBIS-DDSM	X-Ray	Breast	Multi Classification	HF
79	NLM-TB	X-Ray	Lung	Tuberculosis	HF
80	ChestXray-NIHCC	X-Ray	Chest	Multi-label Classification	HF
81	COVIDx CXR-4	X-Ray	Lung	COVID19, Classification	HF
82	VinDr-Mammo	X-Ray	Breast	Multi-label Classification	HF
83	PBC dataset normal DIB	Mic	Cell	Multi Classification	HF
84	Human Protein Atlas	Mic	Cell	Multi-label Classification (Only green)	HF
85	RSNA Pneumonia Detection Challenge 2018	X-Ray	Chest	Multi-label Classification	HF
86	VinDr-SpineXR	X-Ray	Bones	Multi Classification of Bones Diseases	HF
87	VinDr-PCXR	X-Ray	Chest	Multi-label Classification	HF
88	PH2	Der	Skin	Melanoma Segmentation	TODO
89	ISBI 2016 (Task3B)	Der	Skin	Melanoma Segmentation	TODO
90	ISIC 2016 (Task 1)	Der	Skin	Melanoma Segmentation	TODO
91	ISIC 2017	Der	Skin	Melanoma Segmentation	TODO
92	CVC-ClinicDB	End	Intestine	Polyp Segmentation	TODO
93	Kvasir-SEG	End	Intestine	Polyp segmentation	TODO
94	m2caiseg	End	Intestine	Surgical Instrument Segmentation	TODO
95	EDD 2020	End	Intestine	Multiple Diseases Segmentation in Intestine	TODO
96	SICAPv2	Mic	Cell	Cancer Cells Segmentation	TODO
97	BUSI	Ultrasound	Breast	Cancer Segmentation	TODO
98	TN3K	Ultrasound	Thyroid	Thyroid Nodule Segmentation	TODO
99	NLM-TB	X-Ray	Lung	Lung Segmentation (With left or right)	TODO
100	VinDr-SpineXR	X-Ray	Bones	Spinal X-ray Anaomaly Detection	TODO
101	VinDr-PCXR	X-Ray	Chest	Multiple Diseases Segmentation in Chest	TODO
102	ChestX-Det	X-Ray	Chest	Multiple Diseases Segmentation in Chest	TODO
103	UW-Madison Gl Tract Image Segmentation	MRI	Intestine	Surgical Instrument Segmentation	TODO
104	Duke Liver Dataset MRI v1	MRI	Liver	Liver Segmentation	TODO
105	Duke Liver Dataset MRI v2	MRI	Liver	Liver Segmentation	TODO
106	SIIM-ACR Pneumothorax Segmentation	X-Ray	Lung	Pneumothorax Segmentation	TODO
107	FIVES	FP	Fundus	Fundus Vascular Segmentation	TODO
108	RIM-ONE DL	FP	Fundus	Optic Disc and Cup Segmentation	TODO
109	PALM19	FP	Fundus	Optic Disc Segmentation	TODO

Aggregated_Subsets

Click to view the details of 53 Subsets

No.	Modality	Area	Task	QA
01	Co	Cervix	Cervical Picture Quality Evaluation	HF
02	CT	Kidney	Kidney Diseases Classification	HF
03	CT	Lung	COVID-19 Classification	HF
04	CT	Lung	Lung Cancer Classification	HF
05	CT	Brain	Brain Hemorrhage Classification	HF
06	CT	Brain	Brain Cancer Classification	HF
07	Der	Skin	Melanoma Type Classification	HF
08	Der	Skin	Skin Diseases Classification	HF
09	DP	Mouth	Teeth Condition Classification	HF
10	DP	Mouth	Oral Cancer Classification	HF
11	End	Intestine	Intestine Cleanliness Level	HF
12	End	Bladder	Cancer Degree Classification	HF
13	End	Intestine	Intestine Diseases Classification	HF
14	FP	Fundus	Eye Diseases Classification	HF
15	FP	Fundus	Multiple-labels Eye Diseases Classification	HF
16	FP	Fundus	Blindness Level	HF
17	FP	Fundus	Retinal Images Quality Evaluation	HF
18	Mic	Cell	Cell Type Classification	HF
19	Mic	Cell	Prostate Cancer Degree Classification	HF
20	Mic	Cell	Multiple-labels Blood Cell Classification	HF
21	Mic	Cell	Cancer Classification	HF
22	MRI	Brain	Head Diseases Classification	HF
23	OCT	Retina	Retina Diseases Classification	HF
24	US	Breast	Breast Cancer Classification	HF
25	X-ray	Bones	Degree Classification of Knee	HF
26	X-ray	Bones	Fractured Classification	HF
27	X-ray	Bones	Vertebrae Diseases Classification	HF
28	X-ray	Lung	COVID-19 and Pneumonia Classification	HF
29	X-ray	Breast	Breast Diseases Classification	HF
30	X-ray	Lung	Tuberculosis Classification	HF
31	X-ray	Chest	Multiple-labels Chest Classification	HF
32	X-ray	Brain	Tumor Classification	HF
33	Mic	Cell	Multi-labels Diseases	HF
34	FP	Fundus	Level Identification	HF
35	X-ray	Bones	Level Identification	HF
36	X-ray	Bones	Spinal lesion Classification	HF
37	X-ray	Breast	Multi-labels Diseases	HF
38	Der	Skin	Lesion Det/Seg	TODO
39	End	Intestine	PolyP Det/Seg	TODO
40	End	Intestine	Surgical Procedures Det/Seg	TODO
41	End	Intestine	Multi-labels Det/Seg	TODO
42	Mic	Cell	Cancer Cell Det/Seg	TODO
43	US	Chest	Cancer Det/Seg	TODO
44	US	Thyroid	Thyroid Nodule Region Det/Seg	TODO
45	MRI	Intestine	Multi-labels Det/Seg	TODO
46	MRI	Liver	Liver Det/Seg	TODO
47	X-ray	Lung	Lung Det/Seg	TODO
48	X-ray	Lung	Pneumothorax Det/Seg	TODO
49	X-ray	Bones	Spinal Anomaly Det	TODO
50	X-ray	Chest	Multi-labels Det	TODO
51	FP	Fundus	Vessel Seg	TODO
52	FP	Fundus	Optic Disc and Cup Seg	TODO
53	FP	Fundus	Optic Disc Seg	TODO

After downloading the images to the "med-mat" folder and placing the corresponding JSON files as shown, you can easily access Med-MAT.

┬─ med-mat
│   ├─ CT_Kindney_Dataset
│   └─ ... (unzipped datasets)
└─ Aggregated_Subsets
│   ├─ Subset--01-train.json
│   ├─ Subset--02-train.json
│   └─ ... (other subsets)
└─ Original_Medical_Datasets
    ├─ Ori--01-train.json
    ├─ Ori--02-train.json
    └─ ... (other medical datasets)

⚒️ Data Construction

Here’s a sample from Med-MAT:

• caption: The original label from the collected medical datasets.
• image: Path to the corresponding image.
• Question and Answer: Caption-based QA pairs.
• Question-choice and Answer-choice: Multiple-choice QA pairs.
• data-no: Number of its original medical dataset.

{
    "id": 1,
    "caption": "Cyst",
    "image": "med-mat/CT_Kindney_Dataset/CT-KIDNEY-DATASET-Normal-Cyst-Tumor-Stone/CT-KIDNEY-DATASET-Normal-Cyst-Tumor-Stone/Cyst/Cyst- (561).jpg",
    "Question": "Review this kidney CT scan and determine the possible condition it represents.",
    "Answer": "Cyst",
    "Question-choice": "Review this kidney CT scan and determine the possible condition it represents.\nA: Stone\nB: Cyst\nC: Normal\nD: Tumor\nAnswer with the option's letter from the given choices directly.",
    "Answer-choice": "B",
    "data-no": "2"
}

Acknowledgement

We appreciate the previous efforts in open-sourcing the medical imaging datasets used in this project.

Please be sure to credit them when citing these datasets.

📖 Citation

@misc{cai2024compositionalgeneralizationmultimodalllms,
      title={On the Compositional Generalization of Multimodal LLMs for Medical Imaging}, 
      author={Zhenyang Cai and Junying Chen and Rongsheng Wang and Weihong Wang and Yonglin Deng and Dingjie Song and Yize Chen and Zixu Zhang and Benyou Wang},
      year={2024},
      eprint={2412.20070},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2412.20070}, 
}