Early detection of oesophageal cancer through colour contrast enhancement for data augmentation

While white light imaging (WLI) of endoscopy has been set as the gold standard for screening and detecting oesophageal squamous cell cancer (SCC), the early signs of SCC are often missed (1 in 4) due to its subtle change of early onset of SCC. This study firstly enhances colour contrast of each of over 600 WLI images and their accompanying narrow band images (NBI) applying CIE colour appearance model CIECAM02. Then these augmented data together with the original images are employed to train a deep learning based system for classification of low grade dysplasia (LGD), SCC and high grade dysplasia (HGD). As a result, the averaged colour difference (ΔE) measured using CIEL*a*b* increased from 11.60 to 14.46 for WLI and from 17.52 to 32.53 for NBI in appearance between suspected regions and their normal neighbours. When training a deep learning system with added enhanced contrasted WLI images, the sensitivity, specific and accuracy for LGD increases by 10.87%, 4.95% and 6.76% respectively. When training with enhanced both WLI and NBI images, these measures for LGD increases by 14.83%, 4.89% and 7.97% respectively, the biggest increase among three classes of SCC, HGD and LGD. In average, the sensitivity, specificity and accuracy for these three classes are 88.26%, 94.44% and 92.63% respectively for classification of SCC, HGD and LGD, being comparable or exceeding existing published work

Towards real-time detection of squamous pre-cancers from oesophageal endoscopic videos

This study investigates the feasibility of applying state of the art deep learning techniques to detect precancerous stages of squamous cell carcinoma (SCC) cancer in real time to address the challenges while diagnosing SCC with subtle appearance changes as well as video processing speed. Two deep learning models are implemented, which are to determine artefact of video frames and to detect, segment and classify those no-artefact frames respectively. For detection of SCC, both mask-RCNN and YOLOv3 architectures are implemented. In addition, in order to ascertain one bounding box being detected for one region of interest instead of multiple duplicated boxes, a faster non-maxima suppression technique (NMS) is applied on top of predictions. As a result, this developed system can process videos at 16-20 frames per second. Three classes are classified, which are ‘suspicious’, ‘high grade’ and ‘cancer’ of SCC. With the resolution of 1920x1080 pixels of videos, the average processing time while apply YOLOv3 is in the range of 0.064-0.101 seconds per frame, i.e. 10-15 frames per second, while running under Windows 10 operating system with 1 GPU (GeForce GTX 1060). The averaged accuracies for classification and detection are 85% and 74% respectively. Since YOLOv3 only provides bounding boxes, to delineate lesioned regions, mask-RCNN is also evaluated. While better detection result is achieved with 77% accuracy, the classification accuracy is similar to that by YOLOYv3 with 84%. However, the processing speed is more than 10 times slower with an average of 1.2 second per frame due to creation of masks. The accuracy of segmentation by mask-RCNN is 63%. These results are based on the date sets of 350 images. Further improvement is hence in need in the future by collecting, annotating or augmenting more datasets

Transfer learning for endoscopy disease detection and segmentation with mask-RCNN benchmark architecture

We proposed and implemented a disease detection and semantic segmentation pipeline using a modified mask-RCNN infrastructure model on the EDD2020 dataset1. On the images provided for the phase-I test dataset, for ’BE’, we achieved an average precision of 51.14%, for ’HGD’ and ’polyp’ it is 50%. However, the detection score for ’suspicious’ and ’cancer’ were low. For phase-I, we achieved a dice coefficient of 0.4562 and an F2 score of 0.4508. We noticed the missed and mis-classification was due to the imbalance between classes. Hence, we applied a selective and balanced augmentation stage in our architecture to provide more accurate detection and segmentation. We observed an increase in detection score to 0.29 on phase-II images after balancing the dataset from our phase-I detection score of 0.24. We achieved an improved semantic segmentation score of 0.62 from our phase-I score of 0.52

Normal families and fixed points of iterates

Let F be a family of holomorphic functions and let K be a constant less than 4. Suppose that for all f in F the second iterate of f does not have fixed points for which the modulus of the multiplier is greater than K. We show that then F is normal. This is deduced from a result about the multipliers of iterated polynomials.Comment: 5 page

Fusion of colour contrasted images for early detection of oesophageal squamous cell dysplasia from endoscopic videos in real time

Standard white light (WL) endoscopy often misses precancerous oesophageal changes due to their only subtle differences to the surrounding normal mucosa. While deep learning (DL) based decision support systems benefit to a large extent, they face two challenges, which are limited annotated data sets and insufficient generalisation. This paper aims to fuse a DL system with human perception by exploiting computational enhancement of colour contrast. Instead of employing conventional data augmentation techniques by alternating RGB values of an image, this study employs a human colour appearance model, CIECAM, to enhance the colours of an image. When testing on a frame of endoscopic videos, the developed system firstly generates its contrast-enhanced image, then processes both original and enhanced images one after another to create initial segmentation masks. Finally, fusion takes place on the assembled list of masks obtained from both images to determine the finishing bounding boxes, segments and class labels that are rendered on the original video frame, through the application of non-maxima suppression technique (NMS). This deep learning system is built upon real-time instance segmentation network Yolact. In comparison with the same system without fusion, the sensitivity and specificity for detecting early stage of oesophagus cancer, i.e. low-grade dysplasia (LGD) increased from 75% and 88% to 83% and 97%, respectively. The video processing/play back speed is 33.46 frames per second. The main contribution includes alleviation of data source dependency of existing deep learning systems and the fusion of human perception for data augmentation

Study on the application of a new multiepoxy reinforcement agent for sheep leather

Content: Leather is a kind of natural biomass composite material which is made of animal skin as material by a series of chemical and physical processing. Its main structure is Collagen fibers of three-dimensional network structure. As we all know sheep leather always exist a common problem with low strength, while the strength of leather depended on the woven degree of collagen fibers. Through the past decades, many methods have been tried to improve the properties of sheep leather. The most commonly used methods are retanning. However, the strength enhancement of sheep leather is extremely limited by retanning, although the fullness and softness may be improved. In this study, a new type of multi-epoxy reinforcement agent (IGE) and IGE with the synergistic effect of polyamine (IGE-PA) were used to enhance the strength of sheep leather in tanning and fatliquoring process. Comparing with chromium tanned leather, it was found that under the optimized conditions (dosage: 10%, pH: 8, Temperature: 35℃ for penetration and 45℃ for fixation, tanning time: 10 h) with IGE as the main tanning agent, the tearing strength was increased 56.8%. While when the polyamine as the synergetic agent for IGE, the tearing strength was significantly increased 87.9%. While IGE and IGE-PA were used in fatliquoring process, it has significant reinforcement effect for tetrakis hydroxymethyl phosphonium (THP) salt tanned leather. It was found that under the optimized conditions (Dosage: 2.5%, pH: 7-8, Temperature: 50℃, Time: 2h) with IGE in fatliquoring process, the tear strength was increased 50.24%, while the IGE-PA was used, the tear strength was increased 64.3%. Furthermore, TGA results showed that decomposition temperatures of IGE and IGE-PA enhanced leather were all higher than traditional chromium tanned leather. In addition, SEM results showed that IGE and IGE-PA enhanced leather obtained better opened-up fiber structure. Take-Away: 1. A new type of multi-epoxy tanning agent (IGE) has reinforcement effect for sheep leather especially in tear strength. 2. IGE with the synergistic effect of polyamine (IGE-PA) were used in tanning process, which has a significant enhancement for the sheep leather. 3. IGE and IGE-PA can be also used in fatliquoring process to enhance the strength of sheep leather

Novel water-assisting low firing MoO3 microwave dielectric ceramics

MoO3 ceramics can not be well densified via conventional solid state method and a low relative density (ρ) was obtained (˜64.5% at 680 °C) with a permittivity (εr) ˜ 7.58, a quality factor (Qf) ˜ 35,000 GHz and a temperature coefficient of resonant frequency (TCF) ˜ − 39 ppm/°C. However, cold sintering at 150 °C using 4 wt. % H2O at 150 MPa enhanced densification and give a relative ρ ˜76.8% and εr ˜ 8.31 but with a Qf of only ˜ 900 GHz. The addition of (NH4)6Mo7O24·4H2O further improved densification to give a relative ρ ˜ 83.7% after annealing at 700 °C, resulting in a εr ˜ 9.91 with a Qf ˜ 11,800 GHz. We conclude therefore that oxides that are difficult to be sintered via a conventional solid state route may benefit from cold sintering but despite the higher density, lower Qf cannot be avoided due to the impurities and grain boundary phases that are introduced

The Microvasculature of Human Infant Oral Mucosa Using Vascular Corrosion Casts and India Ink Injection II. Palate and Lip

The microvasculature of human hard and soft palate and lip originating from four infant males and six females, aged 6 months to 2 years was studied by scanning electron microscopy of vascular corrosion casts and light microscopy of India ink injected specimens. The capillary loops of the hard palate mucosa and vermilion border of the lips were found to be tall, numerous and consisted of primary, secondary and tertiary loops. Those of the soft palatal and labial mucosa were short, few in number and demonstrated a simple hair-pin shape originating directly from the subpapillary vascular network. It was concluded that the configuration of capillary loops is not only determined by the shape of the connective tissue papillae in the lamina propria but also influenced by the functional demands characteristic of the different areas of the oral mucosa