. Unlike ViT that can only generate a single-resolution feature map, the goal of this module is, given an input image, to generate CNN-like multi-level features. These features provide high-resolution coarse features and low-resolution fine-grained features that usually boost the performance of semantic segmentation. More precisely, given an input image with a resolution of H × W × 3, we perform patch merging to obtain a hierarchical feature map Fi with a resolution of H 2 i+1 × 2 Wi+1 × Ci , where i ∈ {1, 2, 3, 4}, and Ci+1 is larger than Ci 翻译

与只能生成单一分辨率特征图的 ViT 不同，该模块的目标是在给定输入图像的情况下生成类似 CNN 的多级特征。这些特征提供高分辨率的粗略特征和低分辨率的细粒度特征，通常可以提高语义分割的性能。更具体地，给定分辨率为 H × W × 3 的输入图像，我们执行补丁合并以获得具有分辨率 H 2 i+1 × 2 Wi+1 × Ci 的分层特征图 Fi，其中 i ∈ {1, 2, 3, 4}，并且 Ci+1 大于 Ci。

相关问题

To overcome the shortcomings of radiomics methods, we developed a more advanced method, called deep learning-based radiomics (DLR). DLR obtains radiomics features by normalizing the information from a deep neural network designed for image segmentation. The main assumption of DLR is that once the image has been segmented accurately by the deep neural network, all the information about the segmented region will have already been installed in the network. Unlike current radiomics methods, in DLR, the high-throughput image features are directly extracted from the deep neural network. Because DLR does not involve extra feature extrac- tion operations, no extra errors will be introduced into the radiomics analysis because of feature calculations. The effectiveness of features is related only to the quality of segmentation. If the tumor has been segmented precisely, the accuracy and effectiveness of the image features can be guaranteed 解释

这段话提到了一个新的医学影像分析方法，叫做基于深度学习的放射组学（DLR）。与现有的放射组学方法不同，DLR直接从深度神经网络中提取高通量的图像特征，而不需要进行额外的特征提取操作，从而避免了因特征计算而引入额外的误差。DLR的主要假设是，一旦图像被深度神经网络准确地分割出来，所有与分割区域相关的信息都已经被嵌入到网络中。因此，DLR的特征有效性与分割的质量密切相关。如果肿瘤被准确地分割了，那么图像特征的准确性和有效性就能得到保证。

The starting configuration of this puzzle is a row of cells, with disks located on cells through . The goal is to move the disks to the end of the row using a constrained set of actions. At each step, a disk can only be moved to an adjacent empty cell, or to an empty cell two spaces away if another disk is located on the intervening square. Given these restrictions, it can be seen that in many cases, no movements will be possible for the majority of the disks. For example, from the starting position, the only two options are to move the last disk from cell to cell , or to move the second-to-last disk from cell to cell . 1. [15 points] Write a function solve_identical_disks(length, n) that returns an optimal solution to the above problem as a list of moves, where length is the number of cells in the row and n is the number of disks. Each move in the solution should be a twoelement tuple of the form (from, to) indicating a disk movement from the cell from to the cell to. As suggested by its name, this function should treat all disks as being identical. Your solver for this problem should be implemented using a breadth-first graph search. The exact solution produced is not important, as long as it is of minimal length. Unlike in the previous two sections, no requirement is made with regards to the manner in which puzzle configurations are represented. Before you begin, think carefully about which data structures might be best suited for the problem, as this choice may affect the efficiency of your search

Here's a possible implementation of the `solve_identical_disks` function using breadth-first graph search:

from collections import deque

def solve_identical_disks(length, n):
    # Initialize the starting configuration
    start = [True] * n + [False] * (length - n)

    # Define a function to generate all possible successor configurations
    def successors(config):
        for i in range(length):
            if not config[i]:
                if i + 1 < length and config[i + 1]:
                    # Move disk to adjacent empty cell
                    yield config[:i] + [True] + [False] + config[i + 2:]
                elif i + 2 < length and config[i + 2]:
                    # Move disk to empty cell two spaces away
                    yield config[:i] + [True] + config[i + 2:i + 3] + [False] + config[i + 3:]

    # Perform breadth-first graph search to find the goal configuration
    queue = deque([(start, [])])
    visited = set([tuple(start)])
    while queue:
        config, moves = queue.popleft()
        if config == [False] * (length - n) + [True] * n:
            return moves
        for successor in successors(config):
            if tuple(successor) not in visited:
                queue.append((successor, moves + [(config.index(True), successor.index(True))]))
                visited.add(tuple(successor))

    # If the goal configuration is not reachable, return None
    return None

The `start` variable represents the starting configuration as a list of `True` values for the cells occupied by disks and `False` values for the empty cells. The `successors` function generates all possible successor configurations for a given configuration by moving a disk to an adjacent empty cell or to an empty cell two spaces away. The `queue` variable is used to store the configurations to be explored, along with the list of moves required to reach them. The `visited` set is used to keep track of the configurations that have already been explored, in order to avoid revisiting them.

The function returns the list of moves required to reach the goal configuration, which is represented as a list of `False` values for the cells before the disks and `True` values for the cells occupied by the disks. If the goal configuration is not reachable, the function returns `None`.