Polyps are a group of cells growing on the inner surface of the colon. Over time, some polyps can lead to colon cancer, which is often fatal if found in its later stages. Colon cancer can be prevented if the polyps are identified and removed in their early stages. Colonoscopy is a very effective screening method to remove polyps and it largely prevents colon cancer. However, some polyps may not be detected during a colonoscopy due to human error. Over the past two decades, many studies have been conducted on computer‐aided detection to reduce the miss rate of polyps. This study consists of two distinct parts, the detection of frames containing polyps and polyp segmentation. In the first section, a new convolutional neural network based on the VGG network is proposed. The proposed network has an accuracy of 86% on a newly collected dataset. In the polyp segmentation section, a fully convolutional network and an effective post‐processing algorithm are presented. An evaluation of the proposed polyp segmentation system on the ETIS‐LARIB database achieves an overall 82.00% F2 score, which outperforms the methods that participated in the sub‐challenge of MICCAI.

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We present techniques used to create a high performance application-specific instruction-set processor (ASIP) implementation of the Pattern-Based Directional Interpolation (PBDI) intra-field deinterlacing algorithm. The proposed techniques focus primarily on an efficient utilization of the available memory bandwidth. They include the use of Very Long Instruction Words (VLIW) and an appropriate choice of custom instructions and application-specific registers in order to form a processing pipeline. We report a speedup factor of 1351 in comparison with a software-only implementation of the algorithm running on a general-purpose 32-bit RISC processor.

In recent times, the performance of computer-aided diagnosis systems in classification of malignancies has significantly improved. Search and retrieval methods are specifically important as they assist physicians in making the right diagnosis in medical imaging owing to their ability of obtaining similar cases for a query image. Supervised classification algorithms are generally more accurate than unsupervised search-based classifications; however, the latter may more easily provide insights into the decision-making process by providing a group of similar cases and their corresponding metadata (i.e., diagnostic reports) and not simply a class probability. In this study, we propose a class-aware search operating on deep image embeddings to increase the accuracy of content-based search. We validate our methodology using two different publicly available datasets, one containing endometrial cancer images and the other containing colorectal cancer images. The proposed class-aware scenarios can enhance the accuracy of the searchbased classifier, thereby making them more feasible in practice. With search results providing access to the metadata of retrieved cases (i.e., pathology reports of evidently diagnosed cases), such a combination has clear benefits for assisting experts with explainable results.

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