Characterizing the Features of Mitotic Figures Using a Conditional Diffusion Probabilistic Model

Mitotic figure detection in histology images is a hard-to-define, yet clinically significant task, where labels are generated with pathologist interpretations and where there is no ``gold-standard'' independent ground-truth. However, it is well-established that these interpretation based labels are often unreliable, in part, due to differences in expertise levels and human subjectivity. In this paper, our goal is to shed light on the inherent uncertainty of mitosis labels and characterize the mitotic figure classification task in a human interpretable manner. We train a probabilistic diffusion model to synthesize patches of cell nuclei for a given mitosis label condition. Using this model, we can then generate a sequence of synthetic images that correspond to the same nucleus transitioning into the mitotic state. This allows us to identify different image features associated with mitosis, such as cytoplasm granularity, nuclear density, nuclear irregularity and high contrast between the nucleus and the cell body. Our approach offers a new tool for pathologists to interpret and communicate the features driving the decision to recognize a mitotic figure.Comment: Accepted for Deep Generative Models Workshop at Medical Image Computing and Computer Assisted Intervention (MICCAI) 202

Learning-Based Optimization of the Under-Sampling Pattern in MRI

The long scan times of Magnetic Resonance Imaging (MRI) create a bottleneck in patient care and acquisitions can be accelerated by under-sampling in k-space (i.e., the Fourier domain). In this thesis, we focus on the optimization of the sub-sampling pattern with a data-driven framework. Since the reconstruction quality of the models are shown to be strongly dependent on the sub-sampling pattern, we combine the two problems. For a provided sparsity constraint, our method optimizes the sub-sampling pattern and reconstruction model, using an end-to-end unsupervised learning strategy. Our algorithm is trained on full-resolution data that are under-sampled retrospectively, yielding a sub-sampling pattern and reconstruction model that are customized to the type of images represented in the data set. The proposed method, which we call LOUPE (Learning-based Optimization of the Under-sampling PattErn), was implemented by modifying a U-Net, a widely-used convolutional neural network architecture, that we append with the forward model that encodes the under-sampling process. Our experiments with T1- weighted structural brain MRI scans, PD and PDFS weighted knee MRI scans show that the optimized sub-sampling pattern can yield significantly more accurate reconstructions compared to standard random uniform, variable density or cartesian under-sampling schemes. The code is made available at: https: //github.com/cagladbahadir/LOUPE

Adherence to guideline-directed medical and device Therapy in outpAtients with heart failure with reduced ejection fraction: The ATA study

Objective: Despite recommendations from heart failure guidelines on the use of pharmacologic and device therapy in patients with heart failure with reduced ejection fraction (HFrEF), important inconsistencies in guideline adherence persist in practice. The aim of this study was to assess adherence to guideline-directed medical and device therapy for the treatment of patients with chronic HFrEF (left ventricular ejection fraction <= 40%)