Transformer-based approaches have shown good results in image captioning tasks. However, current approaches have a limitation in generating text from global features of an entire image. Therefore, we propose novel methods for generating better image captioning as follows: (1) The Global-Local Visual Extractor (GLVE) to capture both global features and local features. (2) The Cross Encoder-Decoder Transformer (CEDT) for injecting multiple-level encoder features into the decoding process. GLVE extracts not only global visual features that can be obtained from an entire image, such as size of organ or bone structure, but also local visual features that can be generated from a local region, such as lesion area. Given an image, CEDT can create a detailed description of the overall features by injecting both low-level and high-level encoder outputs into the decoder. Each method contributes to performance improvement and generates a description such as organ size and bone structure. The proposed model was evaluated on the IU X-ray dataset and achieved better performance than the transformer-based baseline results, by 5.6% in BLEU score, by 0.56% in METEOR, and by 1.98% in ROUGE-L.

Computed tomography (CT) is one of the most widely used non-invasive imaging modalities for medical diagnosis. In clinical practice, CT images are usually acquired with large slice thicknesses due to the high cost of memory storage and operation time, resulting in an anisotropic CT volume with much lower inter-slice resolution than in-plane resolution. Since such inconsistent resolution may lead to difficulties in disease diagnosis, deep learning-based volumetric super-resolution methods have been developed to improve inter-slice resolution. Most existing methods conduct single-image super-resolution on the through-plane or synthesize intermediate slices from adjacent slices; however, the anisotropic characteristic of 3D CT volume has not been well explored. In this paper, we propose a novel cross-view texture transfer approach for CT slice interpolation by fully utilizing the anisotropic nature of 3D CT volume. Specifically, we design a unique framework that takes high-resolution in-plane texture details as a reference and transfers them to low-resolution through-plane images. To this end, we introduce a multi-reference non-local attention module that extracts meaningful features for reconstructing through-plane high-frequency details from multiple in-plane images. Through extensive experiments, we demonstrate that our method performs significantly better in CT slice interpolation than existing competing methods on public CT datasets including a real-paired benchmark, verifying the effectiveness of the proposed framework. The source code of this work is available at https://github.com/khuhm/ACVTT.

Diffusion models, which are state-of-the-art generative models, have been widely applied to image restoration tasks. However, most image restoration methods based on diffusion models require a large amount of computational memory, making it difficult to use them with high-resolution images. Although patch-based diffusion models have emerged to address this problem, these models are limited in effectively mitigating boundary artifacts and producing results close to the ground truth. In this paper, we propose Patch-based Diffusion using Constrained Refinement (PD-CR) that refines the noise estimated by patch-based diffusion models to produce a restored image while keeping the luminance of the input degraded image. Leveraging patch-based diffusion models, the proposed method can handle a high-resolution image as input with minimal memory requirements. Our experiments on various image restoration tasks, such as image denoising and raindrop removal, demonstrate that the proposed method is better than or on par with the state-of-the-art methods.

The rising incidence of kidney cancer underscores the need for precise and reproducible diagnostic methods. In particular, renal cell carcinoma (RCC), the most prevalent type of kidney cancer, requires accurate nuclear grading for better prognostic prediction. Recent advances in deep learning have facilitated end-to-end diagnostic methods using contextual features in histopathological images. However, most existing methods focus only on image-level features or lack an effective process for aggregating nuclei prediction results, limiting their diagnostic accuracy. In this paper, we introduce a novel framework, Nuclei feature Assisted Patch-level RCC grading (NuAP-RCC), that leverages nuclei-level features for enhanced patch-level RCC grading. Our approach employs a nuclei-level RCC grading network to extract grade-aware features, which serve as node features in a graph. These node features are aggregated using graph neural networks to capture the morphological characteristics and distributions of the nuclei. The aggregated features are then combined with global image-level features extracted by convolutional neural networks, resulting in a final feature for accurate RCC grading. In addition, we present a new dataset for patch-level RCC grading. Experimental results demonstrate the superior accuracy and generalizability of NuAP-RCC across datasets from different medical institutions, achieving a 6.15% improvement in accuracy over the second-best model on the USM-RCC dataset.

Due to the significant power consumption of emissive display panels and the limited battery capacity of electronic devices, it is necessary to implement power-constrained display operations for video content. One of the effective power-constrained display techniques is power-constrained contrast enhancement (PCCE), which aims to reduce the power demands of the display while maintaining the quality of the content. However, PCCE has been mainly studied in the image domain. In this paper, we explore how PCCE can be applied to the video domain by revisiting the proportional-integral-derivative (PID) control. The proposed method consists of three key components: 1) The proportional term determines a power-saving ratio for each video frame based on its luminance, resulting in aggressive power-saving in bright video frames; 2) the integral term ensures that the target power-saving ratio is achieved over the entire video; 3) the differential term prevents abrupt temporal fluctuations in the power-saving ratio of consecutive video frames. The experimental results demonstrate that the proposed PID-based PCCE method achieves superior performance on two public video datasets. In particular, the proposed method achieves 5.8% improvements in VMAF compared to the previous method on the TVSum dataset.