Datasets:
Formats:
parquet
Sub-tasks:
multi-input-text-classification
Languages:
English
Size:
100K - 1M
License:
Image Index stringlengths 16 16 | Texts stringlengths 84 210 | View Position stringclasses 2
values | Image Features sequencelengths 512 512 | Text Features sequencelengths 512 512 | Atelectasis int32 0 1 | Cardiomegaly int32 0 1 | Effusion int32 0 1 | Infiltration int32 0 1 | Mass int32 0 1 | Nodule int32 0 1 | Pneumonia int32 0 1 | Pneumothorax int32 0 1 | Consolidation int32 0 1 | Edema int32 0 1 | Emphysema int32 0 1 | Fibrosis int32 0 1 | Hernia int32 0 1 | Pleural_Thickening int32 0 1 | No_Finding int32 0 1 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
00000001_000.png | This photo of a chest x-ray shows a Cardiomegaly finding. The image is taken from a PA view. | PA | [
-0.047554731369018555,
-0.024417875334620476,
-0.2809467911720276,
-0.025858107954263687,
-0.014165667816996574,
0.003804249921813607,
-0.023984646424651146,
0.016693774610757828,
-0.0011217523133382201,
-0.021765759214758873,
0.04472145810723305,
0.01795242913067341,
-0.029904279857873917,
... | [
-0.014336158521473408,
-0.051427945494651794,
-0.03088880330324173,
-0.025827940553426743,
-0.019897861406207085,
0.016288522630929947,
0.011414722539484501,
-0.0012368570314720273,
0.02699963003396988,
-0.05022412911057472,
-0.011788640171289444,
0.011947030201554298,
-0.018385400995612144,... | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
00000001_001.png | This photo of a chest x-ray shows multiple findings including Cardiomegaly and Emphysema. The image is taken from a PA view. | PA | [
-0.04611440375447273,
-0.0379432812333107,
-0.28582221269607544,
-0.019533470273017883,
-0.023110153153538704,
0.01052901428192854,
-0.033620916306972504,
0.008291111327707767,
-0.007207818329334259,
-0.026053784415125847,
0.029763756319880486,
0.02085862122476101,
-0.03322592377662659,
0.... | [
-0.03448459878563881,
-0.05419719219207764,
-0.03807877376675606,
-0.030541833490133286,
-0.011128371581435204,
0.020796822383999825,
-0.0021311778109520674,
-0.01328555028885603,
0.022600580006837845,
-0.01882605440914631,
0.008783583529293537,
-0.0052074259147048,
-0.013202871195971966,
... | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
00000001_002.png | This photo of a chest x-ray shows multiple findings including Cardiomegaly and Effusion. The image is taken from a PA view. | PA | [
-0.046720653772354126,
-0.040042001754045486,
-0.29038652777671814,
-0.028128746896982193,
-0.029858386144042015,
0.03533126786351204,
-0.033193349838256836,
0.009468233212828636,
0.0028854617848992348,
-0.024626603350043297,
0.024211443960666656,
0.02468465454876423,
-0.012263563461601734,
... | [
-0.02680680900812149,
-0.05335159972310066,
-0.041031938046216965,
-0.008345304988324642,
-0.019419901072978973,
0.057277169078588486,
-0.02209486998617649,
-0.030611135065555573,
0.031314410269260406,
-0.04379505664110184,
-0.028816401958465576,
0.01494523137807846,
-0.02790152095258236,
... | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
00000002_000.png | This photo of a chest x-ray shows no findings available. The image is taken from a PA view. | PA | [
-0.05449114367365837,
-0.002965022111311555,
-0.2812284827232361,
-0.020111070945858955,
-0.016491612419486046,
0.0380941778421402,
-0.012179896235466003,
0.03852112218737602,
-0.01537646446377039,
0.004737014416605234,
0.01630519889295101,
-0.007682950235903263,
-0.0011849789880216122,
-0... | [
-0.0198361799120903,
-0.040187571197748184,
-0.03979671001434326,
0.006723976694047451,
-0.0030163845513015985,
0.009550536051392555,
0.005970212165266275,
0.011782718822360039,
0.0056159100495278835,
-0.02836361713707447,
-0.003135020611807704,
-0.019343528896570206,
-0.02240701951086521,
... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
00000003_001.png | This photo of a chest x-ray shows a Hernia finding. The image is taken from a PA view. | PA | [
-0.0029371855780482292,
0.03051556833088398,
-0.2535656690597534,
-0.025174815207719803,
-0.0022402536123991013,
0.026561236009001732,
0.0007812470430508256,
-0.00048237049486488104,
-0.021610625088214874,
-0.0076167527586221695,
0.020935960114002228,
-0.022095663473010063,
-0.03685499727725... | [
-0.02361217513680458,
0.0213457178324461,
-0.031623583287000656,
-0.01281907968223095,
-0.023813370615243912,
0.055228397250175476,
0.0399709977209568,
0.02986587956547737,
-0.00025465746875852346,
-0.04409777745604515,
0.01704263500869274,
0.023485258221626282,
-0.026028048247098923,
-0.0... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
00000003_002.png | This photo of a chest x-ray shows a Hernia finding. The image is taken from a PA view. | PA | [
-0.022250283509492874,
0.03808863088488579,
-0.2374359369277954,
-0.01571289636194706,
0.020297953858971596,
0.04692203924059868,
0.015179627574980259,
0.022372310981154442,
-0.04126177728176117,
0.0019121458753943443,
0.02253575064241886,
-0.010380223393440247,
-0.05097575858235359,
-0.07... | [
-0.02361217513680458,
0.0213457178324461,
-0.031623583287000656,
-0.01281907968223095,
-0.023813370615243912,
0.055228397250175476,
0.0399709977209568,
0.02986587956547737,
-0.00025465746875852346,
-0.04409777745604515,
0.01704263500869274,
0.023485258221626282,
-0.026028048247098923,
-0.0... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
00000003_003.png | This photo of a chest x-ray shows multiple findings including Hernia and Infiltration. The image is taken from a PA view. | PA | [
-0.021311284974217415,
0.0355827771127224,
-0.2669873535633087,
-0.013546047732234001,
-0.0018805214203894138,
0.004954432602971792,
0.012503527104854584,
0.010205771774053574,
-0.024577399715781212,
-0.008344548754394054,
0.01751410961151123,
-0.0035172258503735065,
-0.04443413391709328,
... | [
-0.03479829803109169,
0.030155794695019722,
-0.0435178168118,
-0.0272881630808115,
-0.008127078413963318,
0.07979650050401688,
0.03583281859755516,
0.013478685170412064,
-0.014633982442319393,
-0.02047635242342949,
0.029456809163093567,
0.026505662128329277,
-0.026806427165865898,
-0.05781... | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
00000003_004.png | This photo of a chest x-ray shows a Hernia finding. The image is taken from a PA view. | PA | [
-0.02961430326104164,
0.013943638652563095,
-0.28719431161880493,
-0.016743140295147896,
0.007997116073966026,
0.031386133283376694,
-0.009863189421594143,
0.035565316677093506,
-0.026128582656383514,
-0.006340420804917812,
0.0010202032281085849,
-0.01986430399119854,
-0.031731944531202316,
... | [
-0.02361217513680458,
0.0213457178324461,
-0.031623583287000656,
-0.01281907968223095,
-0.023813370615243912,
0.055228397250175476,
0.0399709977209568,
0.02986587956547737,
-0.00025465746875852346,
-0.04409777745604515,
0.01704263500869274,
0.023485258221626282,
-0.026028048247098923,
-0.0... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
00000003_005.png | This photo of a chest x-ray shows a Hernia finding. The image is taken from a PA view. | PA | [-0.03054821863770485,0.031076371669769287,-0.2557828426361084,-0.0308160949498415,-0.00539532024413(...TRUNCATED) | [-0.02361217513680458,0.0213457178324461,-0.031623583287000656,-0.01281907968223095,-0.0238133706152(...TRUNCATED) | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
00000003_006.png | This photo of a chest x-ray shows a Hernia finding. The image is taken from a PA view. | PA | [-0.0376569963991642,0.020924098789691925,-0.25126561522483826,-0.035456590354442596,0.0032629261258(...TRUNCATED) | [-0.02361217513680458,0.0213457178324461,-0.031623583287000656,-0.01281907968223095,-0.0238133706152(...TRUNCATED) | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
End of preview. Expand in Data Studio
NIH-CXR14-BiomedCLIP-Features Dataset
This dataset is derived from the NIH Chest X-ray Dataset (NIH-CXR14) and processed using the BiomedCLIP-PubMedBERT_256-vit_base_patch16_224 model from Microsoft. It contains image and text features extracted from chest X-ray images and their corresponding textual findings.
Dataset Description
The original NIH-CXR14 dataset comprises 112,120 chest X-ray images with disease labels from 30,805 unique patients. This processed dataset includes:
- Image Features: Extracted using the vision encoder of BiomedCLIP (512 dimensions).
- Text Features: Extracted using the text encoder of BiomedCLIP (512 dimensions).
- Finding Labels: The original disease labels, processed and converted into a multi-label format.
- Image Index: Unique identifiers for each image.
- View Position: The view position of the X-ray (e.g., PA, AP).
- Processed Text: A grammatically correct text prompt generated from the finding labels, designed for use with the BiomedCLIP model.
Processing Steps
- Data Loading: The original NIH-CXR14 image and text datasets were loaded.
- Text Preprocessing:
- Problematic characters (
|) were replaced with commas. - "No Finding" labels were converted to "No_Finding".
- Finding labels were split into individual findings.
- Grammatically correct text prompts were generated based on the finding labels and view position.
- Problematic characters (
- Feature Extraction:
- Images and text prompts were preprocessed using the BiomedCLIP preprocessors.
- Image and text features were extracted using the BiomedCLIP model.
- Data Storage:
- Extracted features, image indices, view positions, raw texts, and finding labels were stored in Parquet files.
- The dataset was chunked into multiple Parquet files for efficient storage and retrieval.
Dataset Structure
The dataset is organized into Parquet files, each containing the following columns:
Image Index: String, unique identifier for each image.Image Features: List of floats, image features extracted by BiomedCLIP.Text Features: List of floats, text features extracted by BiomedCLIP.View Position: String, view position of the X-ray.Texts: String, processed text prompts.[Finding Label]: Integer (0 or 1), multi-label representation of each finding.
Usage
This dataset can be used for various tasks, including:
- Multi-label classification: Using the extracted features to predict disease findings.
- Retrieval: Retrieving relevant X-ray images based on text queries or vice versa.
- Fine-tuning: Fine-tuning models for medical image analysis tasks.
Installation
To load the dataset, you can use the datasets library from Hugging Face:
from datasets import load_dataset
dataset = load_dataset("Yasintuncer/NIH-CXR14-BiomedCLIP-Features")
- Downloads last month
- 38