diff --git a/examples/healthcare/data/bloodmnist.py b/examples/healthcare/data/bloodmnist.py
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+#
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+#
+
+import os
+import json
+from glob import glob
+import numpy as np
+from PIL import Image
+
+
+class Compose(object):
+    """Compose several transforms together.
+
+    Args:
+        transforms: list of transforms to compose.
+
+    Example:
+        >>> transforms.Compose([
+        >>>     transforms.ToTensor(),
+        >>>     transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+        >>> ])
+
+    """
+
+    def __init__(self, transforms):
+        self.transforms = transforms
+
+    def forward(self, img):
+        """
+        Args:
+            img (PIL Image or numpy array): Image to be processed.
+
+        Returns:
+            PIL Image or numpy array: Processed image.
+        """
+        for t in self.transforms:
+            img = t.forward(img)
+        return img
+
+    def __repr__(self):
+        format_string = self.__class__.__name__ + '('
+        for t in self.transforms:
+            format_string += '\n'
+            format_string += '    {0}'.format(t)
+        format_string += '\n)'
+        return format_string
+
+
+class ToTensor(object):
+    """Convert a ``PIL Image`` to ``numpy.ndarray``.
+
+    Converts a PIL Image (H x W x C) in the range [0, 255] to a ``numpy.array`` of shape
+    (C x H x W) in the range [0.0, 1.0]
+    if the PIL Image belongs to one of the modes (L, LA, P, I, F, RGB, YCbCr, RGBA, CMYK, 1).
+
+    In the other cases, tensors are returned without scaling.
+
+    .. note::
+        Because the input image is scaled to [0.0, 1.0], this transformation should not be used when
+        transforming target image masks.
+    """
+
+    def forward(self, pic):
+        """
+        Args:
+            pic (PIL Image): Image to be converted to array.
+
+        Returns:
+            Array: Converted image.
+        """
+        if not isinstance(pic, Image.Image):
+            raise TypeError('pic should be PIL Image. Got {}'.format(type(pic)))
+
+        # Handle PIL Image
+        mode_to_nptype = {'I': np.int32, 'I;16': np.int16, 'F': np.float32}
+        img = np.array(pic, mode_to_nptype.get(pic.mode, np.uint8), copy=True)
+
+        if pic.mode == '1':
+            img = 255 * img
+
+        # Put it from HWC to CHW format
+        img = np.transpose(img, (2, 0, 1))
+
+        if img.dtype == np.uint8:
+            return np.array(np.float32(img) / 255.0, dtype=np.float)
+        else:
+            return np.float(img)
+
+    def __repr__(self):
+        return self.__class__.__name__ + '()'
+
+
+class Normalize(object):
+    """Normalize a ``numpy.array`` image with mean and standard deviation.
+
+    This transform does not support PIL Image.
+    Given mean: ``(mean[1],...,mean[n])`` and std: ``(std[1],..,std[n])`` for ``n``
+    channels, this transform will normalize each channel of the input
+    ``numpy.array`` i.e.,
+    ``output[channel] = (input[channel] - mean[channel]) / std[channel]``
+
+    .. note::
+        This transform acts out of place, i.e., it does not mutate the input array.
+
+    Args:
+        mean (Sequence): Sequence of means for each channel.
+        std (Sequence): Sequence of standard deviations for each channel.
+        inplace(bool, optional): Bool to make this operation in-place.
+
+    """
+
+    def __init__(self, mean, std, inplace=False):
+        super().__init__()
+        self.mean = mean
+        self.std = std
+        self.inplace = inplace
+
+    def forward(self, img: np.ndarray):
+        """
+        Args:
+            img (Numpy ndarray): Array image to be normalized.
+
+        Returns:
+            d_res (Numpy ndarray): Normalized Tensor image.
+        """
+        if not isinstance(img, np.ndarray):
+            raise TypeError('Input img should be a numpy array. Got {}.'.format(type(img)))
+
+        if not img.dtype == np.float:
+            raise TypeError('Input array should be a float array. Got {}.'.format(img.dtype))
+
+        if img.ndim < 3:
+            raise ValueError('Expected array to be an array image of size (..., C, H, W). Got img.shape = '
+                             '{}.'.format(img.shape))
+
+        if not self.inplace:
+            img = img.copy()
+
+        dtype = img.dtype
+        mean = np.array(self.mean, dtype=dtype)
+        std = np.array(self.std, dtype=dtype)
+        if (std == 0).any():
+            raise ValueError('std evaluated to zero after conversion to {}, leading to division by zero.'.format(dtype))
+        s_res = np.subtract(img, mean[:, None, None])
+        d_res = np.divide(s_res, std[:, None, None])
+
+        return d_res
+
+    def __repr__(self):
+        return self.__class__.__name__ + '(mean={0}, std={1})'.format(self.mean, self.std)
+
+
+class ClassDataset(object):
+    """Fetch data from file and generate batches.
+
+    Load data from folder as PIL.Images and convert them into batch array.
+
+    Args:
+        img_folder (Str): Folder path of the training/validation images.
+        transforms (Transform):  Preprocess transforms.
+    """
+
+    def __init__(self, img_folder, transforms):
+        super(ClassDataset, self).__init__()
+
+        self.img_list = list()
+        self.transforms = transforms
+
+        classes = os.listdir(img_folder)
+        for i in classes:
+            images = glob(os.path.join(img_folder, i, "*"))
+            for img in images:
+                self.img_list.append((img, i))
+
+    def __len__(self) -> int:
+        return len(self.img_list)
+
+    def __getitem__(self, index: int):
+        img_path, label_str = self.img_list[index]
+        img = Image.open(img_path)
+        img = self.transforms.forward(img)
+        label = np.array(label_str, dtype=np.int32)
+
+        return img, label
+
+    def batchgenerator(self, indexes, batch_size, data_size):
+        """Generate batch arrays from transformed image list.
+
+        Args:
+            indexes (Sequence): current batch indexes list, e.g. [n, n + 1, ..., n + batch_size]
+            batch_size (int):
+            data_size (Tuple): input image size of shape (C, H, W)
+
+        Return:
+            batch_x (Numpy ndarray): batch array of input images (B, C, H, W)
+            batch_y (Numpy ndarray): batch array of ground truth lables (B,)
+        """
+        batch_x = np.zeros((batch_size,) + data_size)
+        batch_y = np.zeros((batch_size,) + (1,), dtype=np.int32)
+        for idx, i in enumerate(indexes):
+            sample_x, sample_y = self.__getitem__(i)
+            batch_x[idx, :, :, :] = sample_x
+            batch_y[idx, :] = sample_y
+
+        return batch_x, batch_y
+
+
+def load(dir_path="tmp/bloodmnist"):
+    # Dataset loading
+    train_path = os.path.join(dir_path, "train")
+    val_path = os.path.join(dir_path, "val")
+    cfg_path = os.path.join(dir_path, "param.json")
+
+    with open(cfg_path, 'r') as load_f:
+        num_class = json.load(load_f)["num_classes"]
+
+    # Define pre-processing methods (transforms)
+    transforms = Compose([
+        ToTensor(),
+        Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+    ])
+    train_dataset = ClassDataset(train_path, transforms)
+    val_dataset = ClassDataset(val_path, transforms)
+    return train_dataset, val_dataset, num_class