The paper addresses data processing support that is required in capsule gastrointestinal endoscopy. First, capsule position estimation method using standard MPEG-7 image features (descriptors) is discussed. The proposed approach makes use of vector quantization, principal component analysis and neural networks. Next, new algorithms dedicated for virtual colonoscopy (VC) human body inspection are described. The VC images can be registered with endoscopic ones and help in capsule localization and navigation. Finally, an original, low- complexity, efficient image compression method, based on integer-to-integer 4x4 DCT transform, is presented and experimentally verified.

We present the experiment results to use the YOLOv3 neural network architecture to automatically detect tumor cells in cytological samples taken from the skin in canines. A rich dataset of 1219 smeared sample images with 28,149 objects was gathered and annotated by the vet doctor to perform the experiments. It covers three types of common round cell neoplasms: mastocytoma, histiocytoma, and lymphoma. The dataset has been thoroughly described in the paper and is publicly available. The YOLOv3 neural network architecture was trained using various schemes involving original dataset modification and the different model parameters. The experiments showed that the prototype model achieved 0.7416 mAP, which outperforms the state-of-the-art machine learning and human estimated results. We also provided a series of analyses that may facilitate ML-based solutions by casting more light on some aspects of its performance. We also presented the main discrepancies between ML-based and human-based diagnoses. This outline may help depict the scenarios and how the automated tools may support the diagnosis process.

The primary objective of this research was to enhance the quality of semantic segmentation in cytology images by incorporating super-resolution (SR) architectures. An additional contribution was the development of a novel dataset aimed at improving imaging quality in the presence of inaccurate focus. Our experimental results demonstrate that the integration of SR techniques into the segmentation pipeline can lead to a significant improvement of up to 25% in the mean average precision (mAP) metric. These findings suggest that leveraging SR architectures holds great promise for advancing the state-of-the-art in cytology image analysis.