Core structure updates: image and text featurizer

examples/medical_transcriptions_classification.py

@@ -0,0 +1,49 @@
+import numpy as np
+import torch
+
+from pyhealth.datasets import MedicalTranscriptionsDataset
+from pyhealth.datasets import get_dataloader
+from pyhealth.models import TransformersModel
+from pyhealth.trainer import Trainer
+
+root = "/srv/local/data/zw12/raw_data/MedicalTranscriptions"
+base_dataset = MedicalTranscriptionsDataset(root)
+
+sample_dataset = base_dataset.set_task()
+
+ratios = [0.7, 0.1, 0.2]
+index = np.arange(len(sample_dataset))
+np.random.shuffle(index)
+s1 = int(len(sample_dataset) * ratios[0])
+s2 = int(len(sample_dataset) * (ratios[0] + ratios[1]))
+train_index = index[: s1]
+val_index = index[s1: s2]
+test_index = index[s2:]
+train_dataset = torch.utils.data.Subset(sample_dataset, train_index)
+val_dataset = torch.utils.data.Subset(sample_dataset, val_index)
+test_dataset = torch.utils.data.Subset(sample_dataset, test_index)
+
+train_dataloader = get_dataloader(train_dataset, batch_size=32, shuffle=True)
+val_dataloader = get_dataloader(val_dataset, batch_size=32, shuffle=False)
+test_dataloader = get_dataloader(test_dataset, batch_size=32, shuffle=False)
+
+model = TransformersModel(
+    model_name="emilyalsentzer/Bio_ClinicalBERT",
+    dataset=sample_dataset,
+    feature_keys=["transcription"],
+    label_key="label",
+    mode="multiclass",
+)
+
+trainer = Trainer(model=model)
+
+print(trainer.evaluate(test_dataloader))
+
+trainer.train(
+    train_dataloader=train_dataloader,
+    val_dataloader=val_dataloader,
+    epochs=1,
+    monitor="accuracy"
+)
+
+print(trainer.evaluate(test_dataloader))

pyhealth/datasets/__init__.py

@@ -1,13 +1,17 @@
+from .base_dataset_v2 import BaseDataset
 from .base_ehr_dataset import BaseEHRDataset
 from .base_signal_dataset import BaseSignalDataset
+from .covid19_cxr import COVID19CXRDataset
 from .eicu import eICUDataset
+from .isruc import ISRUCDataset
+from .medical_transriptions import MedicalTranscriptionsDataset
 from .mimic3 import MIMIC3Dataset
 from .mimic4 import MIMIC4Dataset
 from .mimicextract import MIMICExtractDataset
 from .omop import OMOPDataset
-from .sleepedf import SleepEDFDataset
-from .isruc import ISRUCDataset
-from .shhs import SHHSDataset
 from .sample_dataset import SampleBaseDataset, SampleSignalDataset, SampleEHRDataset
-from .splitter import split_by_patient, split_by_visit
+from .sample_dataset_v2 import SampleDataset
+from .shhs import SHHSDataset
+from .sleepedf import SleepEDFDataset
+from .splitter import split_by_patient, split_by_visit, split_by_sample
 from .utils import collate_fn_dict, get_dataloader, strptime

pyhealth/datasets/base_dataset_v2.py

@@ -0,0 +1,74 @@
+import logging
+from abc import ABC, abstractmethod
+from typing import Optional, Dict
+
+from tqdm import tqdm
+
+from pyhealth.datasets.sample_dataset_v2 import SampleDataset
+from pyhealth.tasks.task_template import TaskTemplate
+
+logger = logging.getLogger(__name__)
+
+
+class BaseDataset(ABC):
+    """Abstract base dataset class."""
+
+    def __init__(
+        self,
+        root: str,
+        dataset_name: Optional[str] = None,
+        **kwargs,
+    ):
+        if dataset_name is None:
+            dataset_name = self.__class__.__name__
+        self.root = root
+        self.dataset_name = dataset_name
+        logger.debug(f"Processing {self.dataset_name} base dataset...")
+        self.patients = self.process()
+        # TODO: cache
+        return
+
+    def __str__(self):
+        return f"Base dataset {self.dataset_name}"
+
+    def __len__(self):
+        return len(self.patients)
+
+    @abstractmethod
+    def process(self) -> Dict:
+        raise NotImplementedError
+
+    @abstractmethod
+    def stat(self):
+        print(f"Statistics of {self.dataset_name}:")
+        return
+
+    @property
+    def default_task(self) -> Optional[TaskTemplate]:
+        return None
+
+    def set_task(self, task: Optional[TaskTemplate] = None) -> SampleDataset:
+        """Processes the base dataset to generate the task-specific sample dataset.
+        """
+        # TODO: cache?
+        if task is None:
+            # assert default tasks exist in attr
+            assert self.default_task is not None, "No default tasks found"
+            task = self.default_task
+
+        # load from raw data
+        logger.debug(f"Setting task for {self.dataset_name} base dataset...")
+
+        samples = []
+        for patient_id, patient in tqdm(
+            self.patients.items(), desc=f"Generating samples for {task.task_name}"
+        ):
+            samples.extend(task(patient))
+        sample_dataset = SampleDataset(
+            samples,
+            input_schema=task.input_schema,
+            output_schema=task.output_schema,
+            dataset_name=self.dataset_name,
+            task_name=task,
+        )
+        return sample_dataset

pyhealth/datasets/covid19_cxr.py

@@ -0,0 +1,144 @@
+import os
+from collections import Counter
+
+import pandas as pd
+
+from pyhealth.datasets.base_dataset_v2 import BaseDataset
+from pyhealth.tasks.covid19_cxr_classification import COVID19CXRClassification
+
+
+class COVID19CXRDataset(BaseDataset):
+    """Base image dataset for COVID-19 Radiography Database
+
+    Dataset is available at https://www.kaggle.com/datasets/tawsifurrahman/covid19-radiography-database
+
+    **COVID-19 data:
+    -----------------------
+    COVID data are collected from different publicly accessible dataset, online sources and published papers.
+    -2473 CXR images are collected from padchest dataset[1].
+    -183 CXR images from a Germany medical school[2].
+    -559 CXR image from SIRM, Github, Kaggle & Tweeter[3,4,5,6]
+    -400 CXR images from another Github source[7].
+
+    ***Normal images:
+    ----------------------------------------
+    10192 Normal data are collected from from three different dataset.
+    -8851 RSNA [8]
+    -1341 Kaggle [9]
+
+    ***Lung opacity images:
+    ----------------------------------------
+    6012 Lung opacity CXR images are collected from Radiological Society of North America (RSNA) CXR dataset  [8]
+
+    ***Viral Pneumonia images:
+    ----------------------------------------
+    1345 Viral Pneumonia data are collected from  the Chest X-Ray Images (pneumonia) database [9]
+
+    Please cite the follwoing two articles if you are using this dataset:
+    -M.E.H. Chowdhury, T. Rahman, A. Khandakar, R. Mazhar, M.A. Kadir, Z.B. Mahbub, K.R. Islam, M.S. Khan, A. Iqbal, N. Al-Emadi, M.B.I. Reaz, M. T. Islam, “Can AI help in screening Viral and COVID-19 pneumonia?” IEEE Access, Vol. 8, 2020, pp. 132665 - 132676.
+    -Rahman, T., Khandakar, A., Qiblawey, Y., Tahir, A., Kiranyaz, S., Kashem, S.B.A., Islam, M.T., Maadeed, S.A., Zughaier, S.M., Khan, M.S. and Chowdhury, M.E., 2020. Exploring the Effect of Image Enhancement Techniques on COVID-19 Detection using Chest X-ray Images. arXiv preprint arXiv:2012.02238.
+
+    **Reference:
+    [1] https://bimcv.cipf.es/bimcv-projects/bimcv-covid19/#1590858128006-9e640421-6711
+    [2] https://github.com/ml-workgroup/covid-19-image-repository/tree/master/png
+    [3] https://sirm.org/category/senza-categoria/covid-19/
+    [4] https://eurorad.org
+    [5] https://github.com/ieee8023/covid-chestxray-dataset
+    [6] https://figshare.com/articles/COVID-19_Chest_X-Ray_Image_Repository/12580328
+    [7] https://github.com/armiro/COVID-CXNet
+    [8] https://www.kaggle.com/c/rsna-pneumonia-detection-challenge/data
+    [9] https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia
+
+    Args:
+        dataset_name: name of the dataset.
+        root: root directory of the raw data. *You can choose to use the path to Cassette portion or the Telemetry portion.*
+        dev: whether to enable dev mode (only use a small subset of the data).
+            Default is False.
+        refresh_cache: whether to refresh the cache; if true, the dataset will
+            be processed from scratch and the cache will be updated. Default is False.
+
+    Attributes:
+        root: root directory of the raw data (should contain many csv files).
+        dataset_name: name of the dataset. Default is the name of the class.
+        dev: whether to enable dev mode (only use a small subset of the data).
+            Default is False.
+        refresh_cache: whether to refresh the cache; if true, the dataset will
+            be processed from scratch and the cache will be updated. Default is False.
+
+    Examples:
+        >>> dataset = COVID19CXRDataset(
+                root="/srv/local/data/zw12/raw_data/covid19-radiography-database/COVID-19_Radiography_Dataset",
+            )
+        >>> print(dataset[0])
+        >>> dataset.stat()
+        >>> dataset.info()
+    """
+
+    def process(self):
+        # process and merge raw xlsx files from the dataset
+        covid = pd.DataFrame(
+            pd.read_excel(f"{self.root}/COVID.metadata.xlsx")
+        )
+        covid["FILE NAME"] = covid["FILE NAME"].apply(
+            lambda x: f"{self.root}/COVID/images/{x}.png"
+        )
+        covid["label"] = "COVID"
+        lung_opacity = pd.DataFrame(
+            pd.read_excel(f"{self.root}/Lung_Opacity.metadata.xlsx")
+        )
+        lung_opacity["FILE NAME"] = lung_opacity["FILE NAME"].apply(
+            lambda x: f"{self.root}/Lung_Opacity/images/{x}.png"
+        )
+        lung_opacity["label"] = "Lung Opacity"
+        normal = pd.DataFrame(
+            pd.read_excel(f"{self.root}/Normal.metadata.xlsx")
+        )
+        normal["FILE NAME"] = normal["FILE NAME"].apply(
+            lambda x: x.capitalize()
+        )
+        normal["FILE NAME"] = normal["FILE NAME"].apply(
+            lambda x: f"{self.root}/Normal/images/{x}.png"
+        )
+        normal["label"] = "Normal"
+        viral_pneumonia = pd.DataFrame(
+            pd.read_excel(f"{self.root}/Viral Pneumonia.metadata.xlsx")
+        )
+        viral_pneumonia["FILE NAME"] = viral_pneumonia["FILE NAME"].apply(
+            lambda x: f"{self.root}/Viral Pneumonia/images/{x}.png"
+        )
+        viral_pneumonia["label"] = "Viral Pneumonia"
+        df = pd.concat(
+            [covid, lung_opacity, normal, viral_pneumonia],
+            axis=0,
+            ignore_index=True
+        )
+        df = df.drop(columns=["FORMAT", "SIZE"])
+        df.columns = ["path", "url", "label"]
+        for path in df.path:
+            assert os.path.isfile(os.path.join(self.root, path))
+        # create patient dict
+        patients = {}
+        for index, row in df.iterrows():
+            patients[index] = row.to_dict()
+        return patients
+
+    def stat(self):
+        super().stat()
+        print(f"Number of samples: {len(self.patients)}")
+        count = Counter([v['label'] for v in self.patients.values()])
+        print(f"Number of classes: {len(count)}")
+        print(f"Class distribution: {count}")
+
+    @property
+    def default_task(self):
+        return COVID19CXRClassification()
+
+
+if __name__ == "__main__":
+    dataset = COVID19CXRDataset(
+        root="/srv/local/data/zw12/raw_data/covid19-radiography-database/COVID-19_Radiography_Dataset",
+    )
+    print(list(dataset.patients.items())[0])
+    dataset.stat()
+    samples = dataset.set_task()
+    print(samples[0])

pyhealth/datasets/featurizers/__init__.py

@@ -0,0 +1,2 @@
+from .image import ImageFeaturizer
+from .value import ValueFeaturizer

pyhealth/datasets/featurizers/image.py

@@ -0,0 +1,23 @@
+
+import PIL.Image
+import torchvision.transforms as transforms
+
+
+class ImageFeaturizer:
+
+    def __init__(self):
+        self.transform = transforms.Compose([transforms.ToTensor()])
+
+    def encode(self, value):
+        image = PIL.Image.open(value)
+        image.load()  # to avoid "Too many open files" errors
+        image = self.transform(image)
+        return image
+
+
+if __name__ == "__main__":
+    sample_image = "/srv/local/data/zw12/raw_data/covid19-radiography-database/COVID-19_Radiography_Dataset/Normal/images/Normal-6335.png"
+    featurizer = ImageFeaturizer()
+    print(featurizer)
+    print(type(featurizer))
+    print(featurizer.encode(sample_image))

pyhealth/datasets/featurizers/value.py

@@ -0,0 +1,14 @@
+from dataclasses import dataclass
+
+
+@dataclass
+class ValueFeaturizer:
+
+    def encode(self, value):
+        return value
+
+
+if __name__ == "__main__":
+    featurizer = ValueFeaturizer()
+    print(featurizer)
+    print(featurizer.encode(2))