A Systematic Comparison of Task Adaptation Techniques for Digital Histopathology.

Due to an insufficient amount of image annotation, artificial intelligence in computational histopathology usually relies on fine-tuning pre-trained neural networks. While vanilla fine-tuning has shown to be effective, research on computer vision has recently proposed improved algorithms, promising better accuracy. While initial studies have demonstrated the benefits of these algorithms for medical AI, in particular for radiology, there is no empirical evidence for improved accuracy in histopathology.

Deep learning in computational dermatopathology of melanoma: A technical systematic literature review.

Deep learning (DL) has become one of the major approaches in computational dermatopathology, evidenced by a significant increase in this topic in the current literature. We aim to provide a structured and comprehensive overview of peer-reviewed publications on DL applied to dermatopathology focused on melanoma. In comparison to well-published DL methods on non-medical images (e.

Validating Automatic Concept-Based Explanations for AI-Based Digital Histopathology.

Digital histopathology poses several challenges such as label noise, class imbalance, limited availability of labelled data, and several latent biases to deep learning, negatively influencing transparency, reproducibility, and classification performance. In particular, biases are well known to cause poor generalization. Proposed tools from explainable artificial intelligence (XAI), bias detection, and bias discovery suffer from technical challenges, complexity, unintuitive usage, inherent biases, or a semantic gap.

Evaluation of Open-Source and Pre-Trained Deep Convolutional Neural Networks Suitable for Player Detection and Motion Analysis in Squash.

In sport science, athlete tracking and motion analysis are essential for monitoring and optimizing training programs, with the goal of increasing success in competition and preventing injury. At present, contact-free, camera-based, multi-athlete detection and tracking have become a reality, mainly due to the advances in machine learning regarding computer vision and, specifically, advances in artificial convolutional neural networks (CNN), used for human pose estimation (HPE-CNN) in image sequences. Sport science in general, as well as coaches and athletes in particular, would greatly benefit from HPE-CNN-based tracking, but the sheer amount of HPE-CNNs available, as well as their complexity, pose a hurdle to the adoption of this new technology.

Delineation of Electrocardiograms Using Multiscale Parameter Estimation.

The continuing interest in unobtrusive electrocardiography requires the development of algorithms, compensating for an increased number of artifacts. In previous work, we proposed a framework for robust parameter estimation of signals following a piecewise Gaussian derivative model, well suited for describing all waves of a heartbeat. The framework is based on a numeric and analytic representation of applying the Wavelet Transform at arbitrary scale to the input model.

ECG delineation using a piecewise Gaussian derivative model with parameters estimated from scale-dependent algebraic expressions.

Automatic methods for the detection of characteristic points in electrocardiography signals support cardiologists in assessing the state of a patient's cardiovascular system. In this work, we apply a general method for parameter estimation to the specific problem of QRS complex, P-, and T-wave delineation, i.e.

Initial evaluation of prospective cardiac triggering using photoplethysmography signals recorded with a video camera compared to pulse oximetry and electrocardiography at 7T MRI.

Background: Accurate synchronization between magnetic resonance imaging data acquisition and a subject's cardiac activity ("triggering") is essential for reducing image artifacts but conventional, contact-based methods for this task are limited by several factors, including preparation time, patient inconvenience, and susceptibility to signal degradation. The purpose of this work is to evaluate the performance of a new contact-free triggering method developed with the aim to eventually replace conventional methods in non-cardiac imaging applications. In this study, the method's performance is evaluated in the context of 7 Tesla non-enhanced angiography of the lower extremities.

Rotational roadmapping: a new image-based navigation technique for the interventional room.

For decades, conventional 2D-roadmaping has been the method of choice for image-based guidewire navigation during endovascular procedures. Only recently have 3D-roadmapping techniques become available that are based on the acquisition and reconstruction of a 3D image of the vascular tree. In this paper, we present a new image-based navigation technique called RoRo (Rotational Roadmapping) that eliminates the guess-work inherent to the conventional 2D method, but does not require a 3D image.

An "optimal" k-needle placement strategy and its application to guiding transbronchial needle aspirations.

This article addresses the problem of finding an "optimal" strategy for placing k biopsy needles, given a large number of possible initial needle positions. We consider two variations of the problem: (1) Calculate the smallest set of needles necessary to guarantee a successful biopsy; and (2) Given a number k, calculate k needles such that the probability of a successful biopsy is maximized. Note that "needle" is used as shorthand for the parameter vector that specifies the needle placement.