Vision Transformers (ViT)s have recently become popular due to their
outstanding modeling capabilities, in particular for capturing long-range
information, and scalability to dataset and model sizes which has led to
state-of-the-art performance in various computer vision and medical image
analysis tasks. In this work, we introduce a unified framework consisting of
two architectures, dubbed UNetFormer, with a 3D Swin Transformer-based encoder
and Convolutional Neural Network (CNN) and transformer-based decoders. In the
proposed model, the encoder is linked to the decoder via skip connections at
five different resolutions with deep supervision. The design of proposed
architecture allows for meeting a wide range of trade-off requirements between
accuracy and computational cost. In addition, we present a methodology for
self-supervised pre-training of the encoder backbone via learning to predict
randomly masked volumetric tokens using contextual information of visible
tokens. We pre-train our framework on a cohort of $5050$ CT images, gathered
from publicly available CT datasets, and present a systematic investigation of
various components such as masking ratio and patch size that affect the
representation learning capability and performance of downstream tasks. We
validate the effectiveness of our pre-training approach by fine-tuning and
testing our model on liver and liver tumor segmentation task using the Medical
Segmentation Decathlon (MSD) dataset and achieve state-of-the-art performance
in terms of various segmentation metrics. To demonstrate its generalizability,
we train and test the model on BraTS 21 dataset for brain tumor segmentation
using MRI images and outperform other methods in terms of Dice score. Code:
this https URL

本文提出了一个统一的框架，它由两个体系结构组成，称为 UNetFormer，具有基于 3D Swin 变压器的编码器和卷积神经网络和变压器的解码器。该架构的设计允许在准确性和计算成本之间满足宽范围的权衡要求。使用 CT 图像进行自我监督预训练，使用 Medical Segmentation Decathlon（MSD）数据集进行肝和肝肿瘤分割任务的 Fine-tune 和测试，并使用 MRI 图像的 BraTS 21 数据集进行脑肿瘤分割，并在 Dice 评分方面优于其他方法。