ABSTRACT With the advancement of modern camera technology, the resolution and quality of images have been significantly improved. High‐resolution images can provide more detailed and clearer information, but they may also introduce more noise, which interferes with the detection and localization of salient objects. To address this issue, existing high‐resolution salient object detection methods either design complex network structures or adopt multi‐modal fusion. However, these approaches often consume significant computing and storage resources. This leads to redundancy of irrelevant features and loss of critical details. In this paper, we propose a network called bidirectionally guided multi‐scale feature decoding network for high‐resolution salient object detection. The model incorporates a bidirectional guidance method to explore the complementarity between encoding and decoding features, thereby achieving a comprehensive combination and enhancement of features. Additionally, in the decoder, multi‐scale encoding features are obtained and utilized sequentially to enhance feature learning and improve the accuracy of salient object detection. Specifically, our model consists of an encoder, a guided multi‐scale feature enhancement (GMFE) module, a guided feature fusion (GFF) module, and a multi‐scale feature decoder (MFD) module. First, multi‐scale encoding features are extracted through the encoder. These features are then fed into the GMFE module to enhance the multi‐scale encoding features under the guidance of saliency map derived from the decoding features of the previous layer. Subsequently, in the GFF module, the enhanced encoding features are fused with the decoding features from the previous layer. Finally, in the MFD module, the bidirectionally guided multi‐scale encoding features is integrated to generate an accurate saliency map. Experiments on two high‐resolution and two low‐resolution datasets demonstrate that our model outperforms on high‐resolution datasets while maintaining competitive performance on low‐resolution datasets, underscoring its effectiveness across varying image qualities.

The COVID-19 pandemic has spread globally for several months. Because its transmissibility and high pathogenicity seriously threaten people's lives, it is crucial to accurately and quickly detect COVID-19 infection. Many recent studies have shown that deep learning (DL) based solutions can help detect COVID-19 based on chest CT scans. However, most existing work focuses on 2D datasets, which may result in low quality models as the real CT scans are 3D images. Besides, the reported results span a broad spectrum on different datasets with a relatively unfair comparison. In this paper, we first use three state-of-the-art 3D models (ResNet3D101, DenseNet3D121, and MC3\_18) to establish the baseline performance on three publicly available chest CT scan datasets. Then we propose a differentiable neural architecture search (DNAS) framework to automatically search the 3D DL models for 3D chest CT scans classification and use the Gumbel Softmax technique to improve the search efficiency. We further exploit the Class Activation Mapping (CAM) technique on our models to provide the interpretability of the results. The experimental results show that our searched models (CovidNet3D) outperform the baseline human-designed models on three datasets with tens of times smaller model size and higher accuracy. Furthermore, the results also verify that CAM can be well applied in CovidNet3D for COVID-19 datasets to provide interpretability for medical diagnosis. Code: https://github.com/HKBU-HPML/CovidNet3D.