Datasets:

bumbledeep
/

odir

patient_id int64 0 4.78k	age int64 1 91	sex stringclasses 2 values	label stringclasses 8 values	label_code int64 0 7
0	69	female	normal	0
1	57	male	normal	0
2	42	male	diabetes	1
4	53	male	diabetes	1
5	50	female	diabetes	1
6	60	male	diabetes	1
7	60	female	diabetes	1
8	59	male	normal	0
9	54	male	other_diseases_abnormalities	7
10	70	male	normal	0
11	60	female	diabetes	1
13	60	female	pathological_myopia	6
14	55	male	other_diseases_abnormalities	7
15	50	male	other_diseases_abnormalities	7
16	54	female	pathological_myopia	6
17	57	male	other_diseases_abnormalities	7
18	58	male	pathological_myopia	6
19	45	male	diabetes	1
21	76	female	other_diseases_abnormalities	7
23	47	male	hypertension	5
24	75	female	cataract	3
26	63	female	diabetes	1
27	33	male	other_diseases_abnormalities	7
28	63	female	hypertension	5
29	59	male	normal	0
31	62	male	normal	0
32	64	female	hypertension	5
33	60	female	other_diseases_abnormalities	7
34	61	male	other_diseases_abnormalities	7
35	68	female	normal	0
37	41	male	normal	0
38	75	male	normal	0
40	62	female	other_diseases_abnormalities	7
42	89	male	other_diseases_abnormalities	7
43	35	male	age_related_macular_degeneration	4
44	55	female	diabetes	1
45	54	male	hypertension	5
46	66	male	pathological_myopia	6
47	60	female	normal	0
48	69	female	age_related_macular_degeneration	4
49	47	female	normal	0
50	59	female	diabetes	1
51	60	male	other_diseases_abnormalities	7
52	67	male	other_diseases_abnormalities	7
53	65	female	age_related_macular_degeneration	4
54	66	female	diabetes	1
55	62	male	age_related_macular_degeneration	4
56	63	female	other_diseases_abnormalities	7
58	74	male	other_diseases_abnormalities	7
60	54	female	other_diseases_abnormalities	7
61	59	male	normal	0
62	50	male	hypertension	5
64	80	male	other_diseases_abnormalities	7
65	56	male	normal	0
66	62	male	normal	0
67	56	male	diabetes	1
68	72	female	other_diseases_abnormalities	7
71	56	female	age_related_macular_degeneration	4
72	78	female	other_diseases_abnormalities	7
73	67	male	normal	0
74	68	male	normal	0
75	68	male	other_diseases_abnormalities	7
77	67	male	other_diseases_abnormalities	7
78	46	male	normal	0
79	72	female	diabetes	1
81	66	male	diabetes	1
82	33	male	other_diseases_abnormalities	7
83	61	female	other_diseases_abnormalities	7
84	51	female	normal	0
85	67	female	other_diseases_abnormalities	7
86	56	female	pathological_myopia	6
87	41	female	diabetes	1
88	71	female	other_diseases_abnormalities	7
89	60	female	diabetes	1
90	53	female	diabetes	1
91	28	male	other_diseases_abnormalities	7
93	70	male	diabetes	1
94	55	male	other_diseases_abnormalities	7
95	46	male	hypertension	5
96	56	female	diabetes	1
97	64	female	other_diseases_abnormalities	7
98	69	male	normal	0
99	62	female	normal	0
100	59	male	normal	0
101	45	female	other_diseases_abnormalities	7
102	73	female	age_related_macular_degeneration	4
103	55	female	normal	0
105	61	female	normal	0
106	57	female	pathological_myopia	6
107	68	female	diabetes	1
108	54	female	normal	0
110	69	male	normal	0
111	64	male	diabetes	1
112	57	female	cataract	3
113	67	female	other_diseases_abnormalities	7
114	48	male	normal	0
115	66	female	normal	0
116	61	male	hypertension	5
117	65	female	other_diseases_abnormalities	7
118	48	male	diabetes	1

End of preview. Expand in Data Studio

Dataset Card for Dataset Name

All the images of the dataset come from this kaggle dataset. Some minor modifications have been made to the metadata. All credit goes to the original authors and the contributor on Kaggle.

Dataset Details

Dataset Description

Ocular Disease Intelligent Recognition (ODIR) is a structured ophthalmic database of 5,000 patients with age, color fundus photographs from left and right eyes and doctors' diagnostic keywords from doctors.

This dataset is meant to represent "real-life" set of patient information collected by Shanggong Medical Technology Co., Ltd. from different hospitals/medical centers in China. In these institutions, fundus images are captured by various cameras in the market, such as Canon, Zeiss and Kowa, resulting into varied image resolutions.

Created by:
- Peking University
- National Institute of Health Data Science at Peking University (NIHDS-PKU)
- Institute of Artificial Intelligence at Peking University（IAI-PKU）
- Shanggong Medical Technology Co., Ltd
- Advanced Institute of Information Technology at Peking University(AIIT-PKU)
Shared by: Larxel
License: MIT

Dataset Sources

Repository: kaggle repo.
Paper: I didn't find an associated paper, but I believe the dataset was first presented here.

Uses

Direct Use

Eye disease classification (single label). Feature extraction (unsupervised or self supervised learning).

Out-of-Scope Use

[More Information Needed]

Dataset Structure

Based on the information provided by the original authors: The 5,000 patients in this challenge are divided into training, off-site testing and on-site testing subsets. Almost 4,000 cases are used in training stage while others are for testing stages.

The proportion of images per category in training and testing datasets is given in the following table: