post-process instance segmentation branches (NP and HV) to generate instance segmentation map. Args: nuclear_prediction: the outp

Process NC (type prediction) branch and combine it with instance segmentation It updates the instance_info with instance type and associated p

Args: inputs: model input data for inference. network: target model to execute inference. supports c

Args `batchdata`, `device`, `non_blocking` refer to the ignite API: https://pytorch.org/ignite/v0.4.8/generated/ignite.engine.create_

Args: data: dictionary data to be processed. num_examples: number of realizations to be processed and results combine

Compute the confidence map Args: data (NDArray): RF ultrasound data (one scanline per column) [H x W] 2D array Returns:

Args: array: the image to generate patches from.

``data`` is an element which often comes from an iteration over an iterable, such as :py:class:`torch.utils.data.Dataset`. This metho

``data`` often comes from an iteration over an iterable, such as :py:class:`torch.utils.data.Dataset`. To simplify the input

Args: data: Expects image/label to have dimensions (C, H, W) or (C, H, W, D), where C is the channel.

Apply the transform to `img`.

Apply the transform to `img`.

Apply the transform to `img`.

Apply the transform to `img`. Args: img: input image to shift intensity. factor: a factor to multiply the ra

Apply the transform to `img`.

Apply the transform to `img`.

Apply the transform to `img`. Raises: ValueError: When ``self.minv=None`` or ``self.maxv=None`` and ``self.factor=None``

Apply the transform to `img`. Args: img: the input tensor/array factor: factor scale by ``v = v * (1 + factor

Apply the transform to `img`.

Apply the transform to `img`.

Apply the transform to `img`.

Apply the transform to `img`, assuming `img` is a channel-first array if `self.channel_wise` is True,

Apply the transform to `img`.

Apply the transform to `img`.

Apply the transform to `img`.

Apply the transform to `img`. gamma: gamma value to adjust the contrast as function.

Apply the transform to `img`.

Apply the transform to `img`.

Args: mask_data: if mask data is single channel, apply to every channel of input image. if multiple channels, the

Args: img: array containing input data. Must be real and in shape [channels, spatial1, spatial2, ...]. Returns:

Args: img: numpy.ndarray containing input data. Must be real and in shape [channels, spatial1, spatial2, ...]. Returns:

Args: img: image with dimensions (C, H, W) or (C, H, W, D)

Apply transform to `img`. Assumes data is in channel-first form. Args: img: image with dimensions (C, H, W) or (C, H, W,

Args: img: image to remap.

Args: img: image to remap.

Compute confidence map from an ultrasound image. Args: img (ndarray or Tensor): Ultrasound image of shape [1, H, W] or [1, D, H,