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1#!/usr/bin/env python 

2# coding=utf-8 

3 

4"""Padchest TB dataset for computer-aided diagnosis 

5 

6A large chest x-ray image dataset with multi-label annotated reports. 

7This dataset includes more than 160,000 images from 67,000 patients that were  

8interpreted and reported by radiologists at Hospital San Juan (Spain) from 2009 

9to 2017, covering six different position views and additional information on 

10image acquisition and patient demography. 

11 

12We keep only "PA" images here and only the "Tuberculosis" subset with an 

13equivalent number of "normal" images. 

14 

15* Reference: [PADCHEST-2019]_ 

16* Original resolution: variable, original size 

17* Labels: [PADCHEST-2019]_ 

18* Split reference: 64%/16%/20% 

19* Protocol ``default``: 

20 

21 * Training samples: 160 

22 * Validation samples: 40 

23 * Test samples: 50 

24 

25* Protocol `ìdiap``: 

26 * Images path adapted to Idiap infrastructure 

27 

28* Labels: DensenetRS predictions 

29""" 

30 

31import os 

32import pkg_resources 

33 

34import bob.extension 

35 

36from ..dataset import JSONDataset 

37from ..loader import make_delayed 

38 

39_protocols = [ 

40 pkg_resources.resource_filename(__name__, "tb_idiap.json"), 

41] 

42 

43 

44def _raw_data_loader(sample): 

45 return dict( 

46 data=sample["data"], 

47 label=sample["label"] 

48 ) 

49 

50 

51def _loader(context, sample): 

52 # "context" is ignored in this case - database is homogeneous 

53 # we returned delayed samples to avoid loading all images at once 

54 return make_delayed(sample, _raw_data_loader, key=sample["filename"]) 

55 

56 

57dataset = JSONDataset( 

58 protocols=_protocols, 

59 fieldnames=("filename", "label", "data"), 

60 loader=_loader, 

61) 

62"""Padchest dataset object"""