This paper introduces a groundbreaking classification model called the
Controllable Ensemble Transformer and CNN (CETC) for the analysis of medical
images. The CETC model combines the powerful capabilities of convolutional
neural networks (CNNs) and transformers to effectively capture both local and
global features present in medical images. The model architecture comprises
three main components: a convolutional encoder block (CEB), a
transposed-convolutional decoder block (TDB), and a transformer classification
block (TCB). The CEB is responsible for capturing multi-local features at
different scales and draws upon components from VGGNet, ResNet, and MobileNet
as backbones. By leveraging this combination, the CEB is able to effectively
detect and encode local features. The TDB, on the other hand, consists of
sub-decoders that decode and sum the captured features using ensemble
coefficients. This enables the model to efficiently integrate the information
from multiple scales. Finally, the TCB utilizes the SwT backbone and a
specially designed prediction head to capture global features, ensuring a
comprehensive understanding of the entire image. The paper provides detailed
information on the experimental setup and implementation, including the use of
transfer learning, data preprocessing techniques, and training settings. The
CETC model is trained and evaluated using two publicly available COVID-19
datasets. Remarkably, the model outperforms existing state-of-the-art models
across various evaluation metrics. The experimental results clearly demonstrate
the superiority of the CETC model, emphasizing its potential for accurately and
efficiently analyzing medical images.

本文介绍了一种名为可控集成变换器和 CNN（CETC）的突破性分类模型，用于医学图像的分析。该模型通过结合卷积神经网络（CNN）和变换器的强大功能，有效捕捉医学图像中的局部和全局特征。模型架构包括三个主要组件：卷积编码器块（CEB），转置卷积解码器块（TDB）和变换器分类块（TCB）。实验结果明显表明了 CETC 模型的优越性，并强调其在准确高效地分析医学图像方面的潜力。

使用集成的 CNN-Transformer 在医学图像中捕捉局部和全局特征

Capturing Local and Global Features in Medical Images by Using Ensemble  CNN-Transformer

Chest radiographs are the most commonly performed radiological examinations
for lesion detection. Recent advances in deep learning have led to encouraging
results in various thoracic disease detection tasks. Particularly, the
architecture with feature pyramid network performs the ability to recognise
targets with different sizes. However, such networks are difficult to focus on
lesion regions in chest X-rays due to their high resemblance in vision. In this
paper, we propose a dual attention supervised module for multi-label lesion
detection in chest radiographs, named DualAttNet. It efficiently fuses global
and local lesion classification information based on an image-level attention
block and a fine-grained disease attention algorithm. A binary cross entropy
loss function is used to calculate the difference between the attention map and
ground truth at image level. The generated gradient flow is leveraged to refine
pyramid representations and highlight lesion-related features. We evaluate the
proposed model on VinDr-CXR, ChestX-ray8 and COVID-19 datasets. The
experimental results show that DualAttNet surpasses baselines by 0.6% to 2.7%
mAP and 1.4% to 4.7% AP50 with different detection architectures. The code for
our work and more technical details can be found at
this https URL

本文提出一种名为 “DualAttNet” 的双重注意力监督模块，用于胸部 X 光片的多标签病变检测，并对 VinDr-CXR、ChestX-ray8 和 COVID-19 数据集进行评估。实验结果表明，DualAttNet 在不同检测体系结构下的 mAP 和 AP50 超过基线 by 0.6% to 2.7% 和 1.4% to 4.7%。

DualAttNet：基于图像级和细粒度疾病注意力的协同融合，用于胸部 X 线多标签病变检测

DualAttNet: Synergistic Fusion of Image-level and Fine-Grained Disease  Attention for Multi-Label Lesion Detection in Chest X-rays

Despite recent progress in improving the performance of misinformation
detection systems, classifying misinformation in an unseen domain remains an
elusive challenge. To address this issue, a common approach is to introduce a
domain critic and encourage domain-invariant input features. However, early
misinformation often demonstrates both conditional and label shifts against
existing misinformation data (e.g., class imbalance in COVID-19 datasets),
rendering such methods less effective for detecting early misinformation. In
this paper, we propose contrastive adaptation network for early misinformation
detection (CANMD). Specifically, we leverage pseudo labeling to generate
high-confidence target examples for joint training with source data. We
additionally design a label correction component to estimate and correct the
label shifts (i.e., class priors) between the source and target domains.
Moreover, a contrastive adaptation loss is integrated in the objective function
to reduce the intra-class discrepancy and enlarge the inter-class discrepancy.
As such, the adapted model learns corrected class priors and an invariant
conditional distribution across both domains for improved estimation of the
target data distribution. To demonstrate the effectiveness of the proposed
CANMD, we study the case of COVID-19 early misinformation detection and perform
extensive experiments using multiple real-world datasets. The results suggest
that CANMD can effectively adapt misinformation detection systems to the unseen
COVID-19 target domain with significant improvements compared to the
state-of-the-art baselines.

本文提出了一种用于早期误报检测（CANMD）的对比适应网络，它通过伪标记和标签纠正组件相结合，有效地适应了未知 COVID-19 目标领域，与现有技术相比，取得了显著的改进。