Daniele Ravì

University College London | UCL · Department of Computer Science

Recent advances in MRI have led to the creation of large datasets. With the increase in data volume, it has become difficult to locate previous scans of the same patient within these datasets (a process known as re-identification). To address this issue, we propose an AI-powered medical imaging retrieval framework called DeepBrainPrint, which is de...

Transposed convolution is crucial for generating high-resolution outputs, yet has received little attention compared to convolution layers. In this work we revisit transposed convolution and introduce a novel layer that allows us to place information in the image selectively and choose the `stroke breadth' at which the image is synthesized, whilst...

Large medical imaging data sets are becoming increasingly available. A common challenge in these data sets is to ensure that each sample meets minimum quality requirements devoid of significant artefacts. Despite a wide range of existing automatic methods having been developed to identify imperfections and artefacts in medical imaging, they mostly...

With the huge expansion of artificial intelligence in medical imaging, many clinical warehouses, medical centres and research communities, have organized patients’ data in well-structured datasets. These datasets are one of the key elements to train AI-enabled solutions. Additionally, the value of such datasets depends on the quality of the underly...

Accurate and realistic simulation of high-dimensional medical images has become an important research area relevant to many AI-enabled healthcare applications. However, current state-of-the-art approaches lack the ability to produce satisfactory high-resolution and accurate subject-specific images. In this work, we present a deep learning framework...

Eye‐tracking technology is an innovative tool that holds promise for enhanced dementia screening, offering the potential of brief and quantitative assessment of cognitive functions. Critically, instruction‐less eye‐tracking tests may ameliorate some of the issues with complex test instructions and linguistic variations associated with traditional c...

Eye‐tracking technology is an innovative tool that holds promise for enhanced dementia screening, offering the potential of brief and quantitative assessment of cognitive functions. Critically, instruction‐less eye‐tracking tests may ameliorate some of the issues with complex test instructions and linguistic variations associated with traditional c...

Eye-tracking technology is an innovative tool that holds promise for enhancing dementia screening. In this work, we introduce a novel way of extracting salient features directly from the raw eye-tracking data of a mixed sample of dementia patients during a novel instruction-less cognitive test. Our approach is based on self-supervised representatio...

Purpose: Probe-based confocal laser endomicroscopy (pCLE) enables performing an optical biopsy via a probe. pCLE probes consist of multiple optical fibres arranged in a bundle, which taken together generate signals in an irregularly sampled pattern. Current pCLE reconstruction is based on interpolating irregular signals onto an over-sampled Cartes...

Accurate and realistic simulation of medical images is a growing area of research relevant to many healthcare applications. However, current image simulators have been unsuccessful when deployed on longitudinal clinical data --- for example, disease progression modelling designed to generate 3D MRI sequences (4D). Failures are typically due to inab...

Purpose: Probe-based Confocal Laser Endomicroscopy (pCLE) enables performing an optical biopsy, providing real-time microscopic images, via a probe. pCLE probes consist of multiple optical fibres arranged in a bundle, which taken together generate signals in an irregularly sampled pattern. Current pCLE reconstruction is based on interpolating irreg...

Simulating images representative of neurodegenerative diseases is important for predicting patient outcomes and for validation of computational models of disease progression. This capability is valuable for secondary prevention clinical trials where outcomes and screening criteria involve neuroimaging. Traditional computational methods are limited...

Simulating images representative of neurodegenerative diseases is important for predicting patient outcomes and for validation of computational models of disease progression. This capability is valuable for secondary prevention clinical trials where outcomes and screening criteria involve neuroimaging. Traditional computational methods are limited...

Simulating images representative of neurodegenerative diseases is important for predicting patient outcomes and for validation of computational models of disease progression. This capability is valuable for secondary prevention clinical trials where outcomes and screening criteria involve neuroimaging. Traditional computational methods are limited...

Simulating images representative of neurodegenerative diseases is important for predicting patient outcomes and for validation of computational models of disease progression. This capability is valuable for secondary prevention clinical trials where outcomes and screening criteria involve neuroimaging. Traditional computational methods are limited...

Diffusion-Weighted Magnetic Resonance Imaging (DW-MRI) explores the random motion of diffusing water molecules in biological tissue and can provide information on the tissue structure at a microscopic scale. DW-MRI is used in many applications both in the brain and other parts of the body such as the breast and prostate, and novel computational met...

Diffusion-Weighted Magnetic Resonance Imaging (DW-MRI) explores the random motion of diffusing water molecules in biological tissue and can provide information on the tissue structure at a microscopic scale. DW-MRI is used in many applications both in the brain and other parts of the body such as the breast and prostate, and novel computational met...

The use of hyperspectral imaging for medical applications is becoming more common in recent years. One of the main obstacles that researchers find when developing hyperspectral algorithms for medical applications is the lack of specific, publicly available, hyperspectral medical data. The work described in this paper was developed within the framew...

In recent years, endomicroscopy has become increasingly used for diagnostic purposes and interventional guidance. It can provide intraoperative aids for real-time tissue characterization and can help to perform visual investigations aimed for example to discover epithelial cancers. Due to physical constraints on the acquisition process, endomicrosc...

In recent years, endomicroscopy imaging has become increasingly used for diagnostic purposes. It can provide intraoperative aids for real-time tissue characterization and can help to perform visual investigations aimed to discover epithelial cancers. However, accurate diagnosis and correct treatments are partially hampered by the low numbers of inf...

Purpose: Probe-based confocal laser endomicroscopy (pCLE) is a recent imaging modality that allows performing in vivo optical biopsies. The design of pCLE hardware, and its reliance on an optical fibre bundle, fundamentally limits the image quality with a few tens of thousands fibres, each acting as the equivalent of a single-pixel detector, assem...

Surgery for brain cancer is a major problem in neurosurgery. The diffuse infiltration into the surrounding normal brain by these tumors makes their accurate identification by the naked eye difficult. Since surgery is the common treatment for brain cancer, an accurate radical resection of the tumor leads to improved survival rates for patients. Howe...

Confusion matrix results of the SVM supervised classification with polynomial kernel applying the 10-fold cross validation method to each patient. (DOCX)

Confusion matrix results of the SVM supervised classification with linear kernel applying the 10-fold cross validation method to each patient. (DOCX)

Confusion matrix results of the SVM supervised classification with RBF kernel applying the 10-fold cross validation method to each patient. (DOCX)

Confusion matrix results of the SVM supervised classification with sigmoid kernel applying the 10-fold cross validation method to each patient. (DOCX)

Hyperspectral imaging (HSI) allows for the acquisition of large numbers of spectral bands throughout the electromagnetic spectrum (within and beyond the visual range) with respect to the surface of scenes captured by sensors. Using this information and a set of complex classification algorithms, it is possible to determine which material or substan...

The recent successes of AI have captured the wildest imagination of both the scientific communities and the general public. Robotics and AI amplify human potentials, increase productivity and are moving from simple reasoning towards human-like cognitive abilities. Current AI technologies are used in a set area of applications, ranging from healthca...

Recent advances in hyperspectral imaging have made it a promising solution for intra-operative tissue characterization, with the advantages of being non-contact, non-ionizing and non-invasive. Working with hyperspectral images in vivo, however, is not straightforward as the high dimensionality of the data makes real-time processing challenging. In...

With a massive influx of multimodality data, the role of data analytics in health informatics has grown rapidly in the last decade. This has also prompted increasing interests in the generation of analytical, data driven models based on machine learning in health informatics. Deep learning, a technique with its foundation in artificial neural netwo...

The increasing popularity of wearable devices in recent years means that a diverse range of physiological and functional data can now be captured continuously for applications in sports, wellbeing, and healthcare. This wealth of information requires efficient methods of classification and analysis where deep learning is a promising technique for la...

Assessment of food intake has a wide range of applications in public health and lifestyle related chronic disease management. In this paper, we propose a real-time food recognition platform combined with daily activity and energy expenditure estimation. In the proposed method, food recognition is based on hierarchical classification using multiple...

The detection of near duplicate images in large databases, such as the ones of popular social networks, digital investigation archives, and surveillance systems, is an important task for a number of image forensics applications. In digital investigation, hashing techniques are commonly used to index large quantities of images for the detection of c...

Content-aware image resizing techniques allow to take into account the visual content of images during the resizing process. The basic idea beyond these algorithms is the removal of vertical and/or horizontal paths of pixels (i.e., seams) containing low salient information. In this paper, we present a method which exploits the gradient vector flow...

Processing method of a digital image to filter red and/or golden eye artifacts, the digital image comprising a plurality of pixel each comprising at least one digital value represented on a plurality of bits, the method comprising: a step of selecting at least one patch of pixels of the digital image comprising pixels potentially representative of...

Processing method of a digital image to filter red and/or golden eye artifacts, the digital image comprising a plurality of pixel each comprising at least one digital value represented on a plurality of bits, the method comprising: a step of selecting at least one patch of pixels of the digital image comprising pixels potentially representative of...

Red eye artifact is caused by the flash light reflected off a person's retina. This effect often occurs when the flash light is very close to the camera lens, as in most compact imaging devices. To reduce these artifacts, most cameras have a red eye flash mode which fires a series of preflashes prior to picture capturing. The major disadvantage of...

Content-aware image resizing is an effective technique that allows to take into account the visual content of images during the resizing process. The basic idea beyond these al-gorithms is the resizing of an image by considering vertical and/or horizontal paths of pixels (i.e., seams) which contain low salient information. In this paper we exploit...

Embedded imaging devices such as digital still and video cameras, mobile phones, personal digital assistants, and visual sensors for surveillance and automotive applications make use of the single-sensor technology approach. An electronic sensor (Charge Coupled Device/Complementary Metal-Oxide-Semiconductor) is used to acquire the spatial variation...

Computer Vision enables mobile devices to extract the meaning of the observed scene from the information acquired with the onboard sensor cameras. Nowadays, there is a growing interest in Computer Vision algorithms able to work on mobile platform (e.g., phone camera, point-and-shot-camera, etc.). Indeed, bringing Computer Vision capabilities on mob...

Scene recognition is extremely useful to improve different tasks involved in the Image Generation Pipeline of single sensor mobile devices (e.g., white balancing, autoexposure, etc). This demo showcases our scene recognition engine implemented on a Nokia N900 smartphone. The engine exploits an image representation directly obtainable in the IGP of...

Image retrieval from large databases, such as popular social networks, collections of surveillance images and videos, or digital investigation archives, is a very important task for a number of applications. In digital investigation, hashing techniques are commonly used to index large quantities of images to detect copies from different archives. I...

Red-eye artifact is a well-known problem in digital photography. Since the large diffusion of mobile devices with embedded camera and flashgun, automatic detection and correction of red-eyes have become an important task. In this paper we describe a technique that makes use of three steps to identify and correct red-eyes. First, red-eye candidates...

Since the large diffusion of digital camera and mo-bile devices with embedded camera and flashgun, the red-eyes artifacts have de-facto become a critical prob-lem. The technique herein described makes use of three main steps to identify and remove red-eyes. First, red eyes candidates are extracted from the input image by using an image filtering pi...

This paper proposes a method to recognize scene categories using bags of visual words obtained by hierarchically partitioning into subregion the input images. Specifically, for each subregion the Textons distribution and the extension of the corresponding subregion are taken into account. The bags of visual words computed on the subregions are weig...

Since the large diffusion of digital camera and mobile devices with embedded camera and flashgun, the redeyes artifacts have de facto become a critical problem. The technique herein described makes use of three main steps to identify and remove red eyes. First, red-eye candidates are extracted from the input image by using an image filtering pipeli...

This paper proposes a method to recognize scene categories using bags of visual words obtained hierarchically partitioning into sub- region the input images. Specically, for each subregion the Textons dis- tribution and the extension of the corresponding subregion are taken into account. The bags of visual words computed on the subregions are weigh...

The proposed paper describes a compression test analysis of JBIG standard algorithm. The aim of such work is to proof the ef- fectiveness of this standard for images acquired through scanners and processed into a printer pipeline. The main issue of printer pipelines is the necessity to use a memory buer to store scanned images for multiple prints....

This paper proposes a method to recognize scene categories using bags of visual words obtained hierarchically partitioning into subregion the input images. Specifically, for each subregions the texton histogram and the extension of the sub-region is taken into account. The bags of visual words, obtained in this way, are weighted and used in a simil...