In this paper, a new method for automatic MR image inhomogeneity correction is proposed. This method, based on deep learning, uses unsupervised learning to estimate the bias corrected images minimizing a cost function based on the entropy of the corrupted image, the derivative of the estimated bias field and corrected image statistics. The proposed method has been compared with the state-of-the-art method N4 providing improved results.
In the stroke workflow, timely decision-making is crucial. Identifying, localizing, and measuring occlusive arterial thrombi during initial imaging is a critical step that triggers the choice of therapeutic treatment for optimizing vascular re-canalization. We present a recurrent model that segments the thrombus in patients suffering from a hyper-acute stroke. A cross-attention module is defined to merge the diffusion and susceptibility-weighted modalities available in Magnetic Resonance Imaging (MRI), which are fed to a modified version of the Convolutional Long-Short-Term Memory (CLSTM) model. It detects almost all the thrombi with a Dice higher than 0.6. The lesion segmentation prediction reduces the false positives to almost zero and the performance is comparable between distal and proximal occlusions.
The identification of mitotic figures (MFs) is a routine task in the histopathological assessment of tumor malignancy with known limitations for human observers. For a machine learning pipeline to robustly detect MFs, it must overcome a variety of conditions such as different scanners, staining protocols, tissue configurations, and organ types. In order to develop a deep learning-based algorithm that can cope with these challenges, there are two obstacles that need to be overcome: obtaining a large-scale dataset of MF annotations spread across different domains of interest, including whole slide images (WSIs) exhaustively annotated for MFs, and using the annotated MFs in an efficient training process to extract the most relevant features for classification. Our work attempts to address both of these challenges and establishes an MF detection pipeline trained solely on animal data, yet competitive on the mixed human/animal MIDOG22 dataset, and, in particular, on human breast cancer. First, we propose a processing pipeline that allows us to strengthen the true scanner robustness of our dataset by physically rescanning the glass slides of annotated WSIs and registering MF positions. To enable the use of such rescans for training, we propose a novel learning paradigm tailored for labels that match partially, which allows to account for ambiguous MF positions in the rescans caused by spurious, suboptimal fine-focus on potential MFs by the scanner. Second, we demonstrate how a multi-task learning approach for MF subtypes, including the prediction of atypical mitotic figures (AMFs), can significantly enhance a model's ability to distinguish MFs from imposters. Our algorithm, using a standard object detection pipeline, performs very competitively with an average test set F1 value across five runs of 0.80 on the MIDOG22 training set. We also demonstrate its ability to stratify overall survival on the TCGA-BRCA dataset based on mitotic density, though it falls short of reaching significance in stratifying survival based on AMFs.
Low-dose computed tomography (LDCT) enables imaging with minimal radiation exposure but typically results in noisy outputs. Deep learning algorithms have been emerging as popular tools for denoising LDCT images, where they typically rely on large data sets requiring data from multiple centers. However, LDCT images collected from different centers (clients) can present significant data heterogeneity, and the sharing of them between clients is also constrained by privacy regulations. In this work, we propose a personalized federated learning (FL) approach for enhancing model generalization across different organ images from multiple local clients while preserving data privacy. Empirically, we find that earlier FL methods tend to underperform single-set models on non-IID LDCT data due to the presence of data heterogeneity characterized by varying frequency patterns. To address this, we introduce a Federated Learning with Frequency Domain Decomposition (FedFDD) approach, which decomposes images into different frequency components and then updates high-frequency signals in an FL setting while preserving local low-frequency characteristics. Specifically, we leverage an adaptive frequency mask with discrete cosine transformation for the frequency domain decomposition. The proposed algorithm is evaluated on LDCT datasets of different organs and our experimental results show that FedFDD can surpass state-of-the-art FL methods as well as both localized and centralized models, especially on challenging LDCT denoising cases. Our code is available at https://github.com/xuhang2019/FedFDD.
X-ray imaging is a cornerstone in medical diagnosis, constituting a significant portion of the radiation dose encountered by patients. Excessive radiation poses health risks, particularly for pediatric patients, but despite the imperative to reduce radiation doses, conventional image processing methods for X-ray denoising often struggle with heuristic parameter calibration and prolonged execution times. Deep Learning solutions have emerged as promising alternatives, but their effectiveness varies, and challenges persist in preserving image quality. This paper presents an exploration of diffusion models for planar X-ray image denoising, a novel approach that to our knowledge has not been yet investigated in this domain. We perform real time denoising of Poisson noise while preserving image resolution and structural similarity. The results indicate that diffusion models show promise for planar X-ray image denoising, offering a potential improvement in the optimization of diagnostic utility amid dose reduction efforts.
