biodatasets Module

biodatasets module provides classes to load public biomedical datasets in a PyTorch friendly manner.

ChestXrayDataset class

class farabio.data.biodatasets.ChestXrayDataset(root: str = '.', download: bool = False, mode: str = 'train', shape: int = 256, transform: Optional[Callable] = None, target_transform: Optional[Callable] = None, show: bool = True)[source]

PyTorch friendly ChestXrayDataset class

Dataset is loaded using Kaggle API. For further information on raw dataset and pneumonia detection, please refer to [1].

References

1

https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia

Examples

>>> valid_dataset = ChestXrayDataset(root=_path, download=True, mode="val", show=True)
../_images/ChestXrayDataset.png
visualize_batch()[source]

DSB18Dataset class

class farabio.data.biodatasets.DSB18Dataset(root: str = '.', download: bool = False, mode: str = 'train', shape: int = 512, transform: Optional[Callable] = None, target_transform: Optional[Callable] = None, show: bool = True)[source]

PyTorch friendly DSB18Dataset class

Dataset is loaded using Kaggle API. For further information on raw dataset and nuclei segmentation, please refer to [1].

References

1

https://www.kaggle.com/c/data-science-bowl-2018/overview

Examples

>>> train_dataset = DSB18Dataset(_path, transform=None, download=False, show=True)
../_images/DSB18Dataset.png
visualize_batch()[source]

HistocancerDataset class

class farabio.data.biodatasets.HistocancerDataset(root: str = '.', mode: str = 'train', transform: Optional[Callable] = None, target_transform: Optional[Callable] = None, download: bool = False, show: bool = True)[source]

PyTorch friendly HistocancerDataset class

Dataset is loaded using Kaggle API. For further information on raw dataset and tumor classification, please refer to [1].

References

1

<https://www.kaggle.com/c/histopathologic-cancer-detection/data>`_

Examples

>>> train_dataset = HistocancerDataset(root=".", download=False, mode="train")
../_images/HistocancerDataset.png
visualize_batch()[source]

RANZCRDataset class

class farabio.data.biodatasets.RANZCRDataset(root: str = '.', mode: str = 'train', shape: int = 256, transform: Optional[Callable] = None, target_transform: Optional[Callable] = None, download: bool = False, show: bool = True)[source]

PyTorch friendly RANZCRDataset class

Dataset is loaded using Kaggle API. For further information on raw dataset and catheters presence, please refer to [1].

References

1

https://www.kaggle.com/c/ranzcr-clip-catheter-line-classification/data

Examples

>>> train_dataset = RANZCRDataset(_path_ranzcr, show=True, shape=512)
../_images/RANZCRDataset.png
visualize_batch()[source]

RetinopathyDataset class

class farabio.data.biodatasets.RetinopathyDataset(root: str = '.', mode: str = 'train', shape: int = 256, transform: Optional[Callable] = None, target_transform: Optional[Callable] = None, download: bool = False, show: bool = True)[source]

PyTorch friendly RetinopathyDataset class

Dataset is loaded using Kaggle API. For further information on raw dataset and blindness detection, please refer to [1].

References

1

<https://www.kaggle.com/c/aptos2019-blindness-detection/data>`_

Examples

>>> train_dataset = RetinopathyDataset(".", mode="train", show=True)
../_images/RetinopathyDataset.png
visualize_batch()[source]