Zoom-in-Net: Deep Mining Lesions for Diabetic Retinopathy Detection

We propose a convolution neural network based algorithm for simultaneously diagnosing diabetic retinopathy and highlighting suspicious regions. Our contributions are two folds: 1) a network termed Zoom-in-Net which mimics the zoom-in process of a clinician to examine the retinal images. Trained with only image-level supervisions, Zoomin-Net can generate attention maps which highlight suspicious regions, and predicts the disease level accurately based on both the whole image and its high resolution suspicious patches. 2) Only four bounding boxes generated from the automatically learned attention maps are enough to cover 80% of the lesions labeled by an experienced ophthalmologist, which shows good localization ability of the attention maps. By clustering features at high response locations on the attention maps, we discover meaningful clusters which contain potential lesions in diabetic retinopathy. Experiments show that our algorithm outperform the state-of-the-art methods on two datasets, EyePACS and Messidor.Comment: accepted by MICCAI 201

Combining Fine- and Coarse-Grained Classifiers for Diabetic Retinopathy Detection

Visual artefacts of early diabetic retinopathy in retinal fundus images are usually small in size, inconspicuous, and scattered all over retina. Detecting diabetic retinopathy requires physicians to look at the whole image and fixate on some specific regions to locate potential biomarkers of the disease. Therefore, getting inspiration from ophthalmologist, we propose to combine coarse-grained classifiers that detect discriminating features from the whole images, with a recent breed of fine-grained classifiers that discover and pay particular attention to pathologically significant regions. To evaluate the performance of this proposed ensemble, we used publicly available EyePACS and Messidor datasets. Extensive experimentation for binary, ternary and quaternary classification shows that this ensemble largely outperforms individual image classifiers as well as most of the published works in most training setups for diabetic retinopathy detection. Furthermore, the performance of fine-grained classifiers is found notably superior than coarse-grained image classifiers encouraging the development of task-oriented fine-grained classifiers modelled after specialist ophthalmologists.Comment: Pages 12, Figures

An Explainable AI-Based Computer Aided Detection System for Diabetic Retinopathy Using Retinal Fundus Images

Diabetic patients have a high risk of developing diabetic retinopathy (DR), which is one of the major causes of blindness. With early detection and the right treatment patients may be spared from losing their vision. We propose a computer-aided detection system, which uses retinal fundus images as input and it detects all types of lesions that define diabetic retinopathy. The aim of our system is to assist eye specialists by automatically detecting the healthy retinas and referring the images of the unhealthy ones. For the latter cases, the system offers an interactive tool where the doctor can examine the local lesions that our system marks as suspicious. The final decision remains in the hands of the ophthalmologists. Our approach consists of a multi-class detector, that is able to locate and recognize all candidate DR-defining lesions. If the system detects at least one lesion, then the image is marked as unhealthy. The lesion detector is built on the faster R-CNN ResNet 101 architecture, which we train by transfer learning. We evaluate our approach on three benchmark data sets, namely Messidor-2, IDRiD, and E-Ophtha by measuring the sensitivity (SE) and specificity (SP) based on the binary classification of healthy and unhealthy images. The results that we obtain for Messidor-2 and IDRiD are (SE: 0.965, SP: 0.843), and (SE: 0.83, SP: 0.94), respectively. For the E-Ophtha data set we follow the literature and perform two experiments, one where we detect only lesions of the type micro aneurysms (SE: 0.939, SP: 0.82) and the other when we detect only exudates (SE: 0.851, SP: 0.971). Besides the high effectiveness that we achieve, the other important contribution of our work is the interactive tool, which we offer to the medical experts, highlighting all suspicious lesions detected by the proposed system.<br/

Image-based Analysis of Patterns Formed in Drying Drops

Image processing and pattern recognition offer a useful and versatile method for optically characterizing drops of a colloidal solution during the drying process and in its final state. This paper exploits image processing techniques applied to cross-polarizing microscopy to probe birefringence and the bright-field microscopy to examine the morphological patterns. The bio-colloidal solution of interest is a mixture of water, liquid crystal (LC) and three different proteins [lysozyme (Lys), myoglobin (Myo), and bovine serum albumin (BSA)], all at a fixed relative concentration. During the drying process, the LC phase separates and becomes optically active detectable through its birefringence. Further, as the protein concentrates, it forms cracks under strain due to the evaporation of water. The mean intensity profile of the drying process is examined using an automated image processing technique that reveals three unique regimes: a steady upsurge, a speedy rise, and an eventual saturation. The high values of standard deviation show the complexity, the roughness, and inhomogeneity of the image surface. A semi-automated image processing technique is proposed to quantify the distance between the consecutive cracks by converting those into high contrast images. The outcome of the image analysis correlates with the initial state of the mixture, the nature of the proteins, and the mechanical response of the final patterns. The paper reveals new insights on the self-assembly of the macromolecules during the drying mechanism of any aqueous solution

Real World Bayesian Optimization Using Robots to Clean Liquid Spills

Developing robots that can contribute to cleaning could have a significant impact on the lives of many. Cleaning wet liquid spills is a particularly challenging task for a robotic system, and has several high impact applications. This is a hard task to physically model due to the complex interactions between cleaning materials and the surface. As such, to the authors' knowledge there has been no prior work in this area. A new method for finding optimal control parameters for the cleaning of liquid spills is required by developing a robotic system which iteratively learns to clean through physical experimentation. The robot creates a liquid spill, cleans and assesses performance and uses Bayesian optimization to find the optimal control parameters for a given size of liquid spill. The automation process enabled the experiment to be repeated more than 400 times over 20 hours to find the optimal wiping control parameters for many different conditions. We then show that these solutions can be extrapolated for different spill conditions. The optimized control parameters showed reliable and accurate performances, which in some cases, outperformed humans at the same task.This work was supported by BEKO PLC and Symphony Kitchens. We are especially thankful for the valuable inputs from Dr Graham Anderson and Dr Natasha Conway

The TgsGP gene is essential for resistance to human serum in Trypanosoma brucei gambiense

Trypanosoma brucei gambiense causes 97% of all cases of African sleeping sickness, a fatal disease of sub-Saharan Africa. Most species of trypanosome, such as T. b. brucei, are unable to infect humans due to the trypanolytic serum protein apolipoprotein-L1 (APOL1) delivered via two trypanosome lytic factors (TLF-1 and TLF-2). Understanding how T. b. gambiense overcomes these factors and infects humans is of major importance in the fight against this disease. Previous work indicated that a failure to take up TLF-1 in T. b. gambiense contributes to resistance to TLF-1, although another mechanism is required to overcome TLF-2. Here, we have examined a T. b. gambiense specific gene, TgsGP, which had previously been suggested, but not shown, to be involved in serum resistance. We show that TgsGP is essential for resistance to lysis as deletion of TgsGP in T. b. gambiense renders the parasites sensitive to human serum and recombinant APOL1. Deletion of TgsGP in T. b. gambiense modified to uptake TLF-1 showed sensitivity to TLF-1, APOL1 and human serum. Reintroducing TgsGP into knockout parasite lines restored resistance. We conclude that TgsGP is essential for human serum resistance in T. b. gambiense

The Unreasonable Effectiveness of Encoder-Decoder Networks for Retinal Vessel Segmentation

We propose an encoder-decoder framework for the segmentation of blood vessels in retinal images that relies on the extraction of large-scale patches at multiple image-scales during training. Experiments on three fundus image datasets demonstrate that this approach achieves state-of-the-art results and can be implemented using a simple and efficient fully-convolutional network with a parameter count of less than 0.8M. Furthermore, we show that this framework - called VLight - avoids overfitting to specific training images and generalizes well across different datasets, which makes it highly suitable for real-world applications where robustness, accuracy as well as low inference time on high-resolution fundus images is required

Rotating tomography Paris-Edinburgh cell:a novel portable press for micro-tomographic 4-D imaging at extreme pressure/temperature/stress conditions

International audienceThis paper presents details of instrumental development to extend synchrotron X-ray microtomography techniques to in situ studies under static compression (high pressure), shear stress or the both conditions at simultaneous high temperatures. To achieve this, a new rotating tomography Paris–Edinburgh cell has been developed. This ultra-compact portable device easily and successfully adapted to various multi-modal synchrotron experimental set-up at ESRF, SOLEIL and DIAMOND is explained in detail. An in-depth description of proof of concept first experiments performed on a high resolution imaging beamline is then given, which illustrate the efficiency of the set-up and the data quality that can be obtained

Cost-Sensitive Regularization for Diabetic Retinopathy Grading from Eye Fundus Images.

Assessing the degree of disease severity in biomedical images is a task similar to standard classification but constrained by an underlying structure in the label space. Such a structure reflects the monotonic relationship between different disease grades. In this paper, we propose a straightforward approach to enforce this constraint for the task of predicting Diabetic Retinopathy (DR) severity from eye fundus images based on the well-known notion of Cost-Sensitive classification. We expand standard classification losses with an extra term that acts as a regularizer, imposing greater penalties on predicted grades when they are farther away from the true grade associated to a particular image. Furthermore, we show how to adapt our method to the modelling of label noise in each of the sub-problems associated to DR grading, an approach we refer to as Atomic Sub-Task modeling. This yields models that can implicitly take into account the inherent noise present in DR grade annotations. Our experimental analysis on several public datasets reveals that, when a standard Convolutional Neural Network is trained using this simple strategy, improvements of 3- 5% of quadratic-weighted kappa scores can be achieved at a negligible computational cost. Code to reproduce our results is released at github.com/agaldran/cost_sensitive_loss_classification