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Deep learning techniques offer improvements in computer-aided diagnosis systems. However, acquiring image domain annotations is challenging due to the knowledge and commitment required of expert pathologists. Pathologists often identify regions in whole slide images with diagnostic relevance rather than examining the entire slide, with a positive correlation between the time spent on these critical image regions and diagnostic accuracy. In this paper, a heatmap is generated to represent pathologists viewing patterns during diagnosis and used to guide a deep learning architecture during training. The proposed system outperforms traditional approaches based on color and texture image characteristics, integrating pathologists domain expertise to enhance region of interest detection without needing individual case annotations. Evaluating our best model, a U-Net model with a pre-trained ResNet-18 encoder, on a skin biopsy whole slide image dataset for melanoma diagnosis, shows its potential in detecting regions of interest, surpassing conventional methods with an increase of 20%, 11%, 22%, and 12% in precision, recall, F1-score, and Intersection over Union, respectively. In a clinical evaluation, three dermatopathologists agreed on the models effectiveness in replicating pathologists diagnostic viewing behavior and accurately identifying critical regions. Finally, our study demonstrates that incorporating heatmaps as supplementary signals can enhance the performance of computer-aided diagnosis systems. Without the availability of eye tracking data, identifying precise focus areas is challenging, but our approach shows promise in assisting pathologists in improving diagnostic accuracy and efficiency, streamlining annotation processes, and aiding the training of new pathologists.

To enhance the regional feature preserving ability and guaranteed inclusion of complete objects in the final fused images, this paper proposes a new image fusion method based on visual saliency objects and fuzzy region attributes. In this approach, the complementary feature encoders are used to extract the features of infrared and visible images, respectively. In the feature fusion layer, to avoid the loss of target features caused by pixel-level fusion and region rigid division, this paper introduces the fuzzy set theory and salient target detection into the design of the fusion strategy. Following the k-means clustering and the energy scale of the infrared scene, the essential attributes of the areas are established. Meanwhile, a salient object segmentation algorithm based on a convolution neural network is adopted to extract the salient region of the source image. Then, feature maps are fused using fuzzy region memberships and salient feature mapping. Finally, the fused image is reconstructed by a redundant feature decoder. The proposed method focuses on the fusion of color scene images and multi-wavelength infrared (thermal and near-infrared) images. Experiments are performed on public RGB-T and RGB-NIR datasets. Compared with several start-of-the-art methods, the evaluation of visual and objective metrics demonstrates that the proposed fusion method has superior performance and is more in line with human sensory characteristics. [ABSTRACT FROM AUTHOR]

To effectively preserve thermal targets in infrared (IR) images and texture information in visible (VIS) images, this paper proposes a saliency detection-based and multi-scale oriented features image fusion method. Firstly, an edge guidance network is used to extract salient targets from IR images in order to generate salient target masks, which provide direction for fusing various types of information and increase the network’s generalization capability. Secondly, a particular loss function in each region is developed in conjunction with the salient target masks to direct the network to perform feature extraction. It can extract salient target characteristics and background texture information selectively while maintaining the integrity of the salient target and background regions efficiently. Finally, an oriented fusion approach based on feature hierarchy is proposed, which minimizes information dropout by integrating deep features on distinct scales with direction. Extensive experiments are carried out in this research using available datasets such as TNO and RoadScene. The method outperforms 10 state-of-the-art methods in the most of the evaluation metrics. This demonstrates that proposed fusion method not only preserves the salient target information of IR images, but also retains more VIS image features. [ABSTRACT FROM AUTHOR]

In this paper, to guide the model's training process to explicitly present a progressive trend, we first introduce the concept of Divide-and-Conquer into Salient Object Detection (SOD) tasks, called DC-Net. Our DC-Net guides multiple encoders to solve different subtasks and then aggregates the feature maps with different semantic information obtained by multiple encoders into the decoder to predict the final saliency map. The decoder of DC-Net consists of newly designed two-level Residual nested-ASPP (ResASPP2) modules, which improve the sparse receptive field existing in ASPP and the disadvantage that the U-shape structure needs downsampling to obtain a large receptive field. Based on the advantage of Divide-and-Conquer's parallel computing, we parallelize DC-Net through reparameterization, achieving competitive performance on five LR-SOD and five HR-SOD datasets under high efficiency (60 FPS and 55 FPS). Codes and results are available: https://github.com/PiggyJerry/DC-Net. • Divide-and-Conquer for salient object detection. • Parallel versions of ResNet and Swin-Transformer are implemented by reparameterization. • A new multi-scale contextual module improves the disadvantage of ASPP and U-shape structure. • Edge maps with different edge widths can impact the model performance. • The pipeline for high-resolution and high-quality segmentation for the meticulous fields of medical, aviation, and military. [ABSTRACT FROM AUTHOR]