Medical image classification by incorporating clinical variables and learned features.

Medical image classification plays an important role in medical imaging. In this work, we present a novel approach to enhance deep learning models in medical image classification by incorporating clinical variables without overwhelming the information. Unlike most existing deep neural network models that only consider single-pixel information, our method captures a more comprehensive view.

Few-shot learning for inference in medical imaging with subspace feature representations.

Unlike in the field of visual scene recognition, where tremendous advances have taken place due to the availability of very large datasets to train deep neural networks, inference from medical images is often hampered by the fact that only small amounts of data may be available. When working with very small dataset problems, of the order of a few hundred items of data, the power of deep learning may still be exploited by using a pre-trained model as a feature extractor and carrying out classic pattern recognition techniques in this feature space, the so-called few-shot learning problem. However, medical images are highly complex and variable, making it difficult for few-shot learning to fully capture and model these features.

Discrepancy-based diffusion models for lesion detection in brain MRI.

Diffusion probabilistic models (DPMs) have exhibited significant effectiveness in computer vision tasks, particularly in image generation. However, their notable performance heavily relies on labelled datasets, which limits their application in medical images due to the associated high-cost annotations. Current DPM-related methods for lesion detection in medical imaging, which can be categorized into two distinct approaches, primarily rely on image-level annotations.

Remote Sensing Image Ship Matching Utilising Line Features for Resource-Limited Satellites.

The existing image matching methods for remote sensing scenes are usually based on local features. The most common local features like SIFT can be used to extract point features. However, this kind of methods may extract too many keypoints on the background, resulting in low attention to the main object in a single image, increasing resource consumption and limiting their performance.

Data Augmentation in Classification and Segmentation: A Survey and New Strategies.

In the past decade, deep neural networks, particularly convolutional neural networks, have revolutionised computer vision. However, all deep learning models may require a large amount of data so as to achieve satisfying results. Unfortunately, the availability of sufficient amounts of data for real-world problems is not always possible, and it is well recognised that a paucity of data easily results in overfitting.

Electron tomography of prolamellar bodies and their transformation into grana thylakoids in cryofixed Arabidopsis cotyledons.

The para-crystalline structures of prolamellar bodies (PLBs) and light-induced etioplast-to-chloroplast transformation have been investigated via electron microscopy. However, such studies suffer from chemical fixation artifacts and limited volumes of 3D reconstruction. Here, we examined Arabidopsis thaliana cotyledon cells by electron tomography (ET) to visualize etioplasts and their conversion into chloroplasts.

Electron Tomography Analysis of Thylakoid Assembly and Fission in Chloroplasts of a Single-Cell C4 plant, Bienertia sinuspersici.

Bienertia sinuspersici is a single-cell C4 plant species of which chlorenchyma cells have two distinct groups of chloroplasts spatially segregated in the cytoplasm. The central vacuole encloses most chloroplasts at the cell center and confines the rest of the chloroplasts near the plasma membrane. Young chlorenchyma cells, however, do not have large vacuoles and their chloroplasts are homogenous.

Applying physical science techniques and CERN technology to an unsolved problem in radiation treatment for cancer: the multidisciplinary 'VoxTox' research programme.

The VoxTox research programme has applied expertise from the physical sciences to the problem of radiotherapy toxicity, bringing together expertise from engineering, mathematics, high energy physics (including the Large Hadron Collider), medical physics and radiation oncology. In our initial cohort of 109 men treated with curative radiotherapy for prostate cancer, daily image guidance computed tomography (CT) scans have been used to calculate delivered dose to the rectum, as distinct from planned dose, using an automated approach. Clinical toxicity data have been collected, allowing us to address the hypothesis that delivered dose provides a better predictor of toxicity than planned dose.