| 1. |
- Nie, Yali
(författare)
-
Automatic Melanoma Diagnosis in Dermoscopic Imaging Base on Deep Learning System
- 2021
-
Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Melanoma is one of the deadliest forms of cancer. Unfortunately, its incidence rates have been increasing all over the world. One of the techniques used by dermatologists to diagnose melanomas is an imaging modality called dermoscopy. The skin lesion is inspected using a magnification device and a light source. This technique makes it possible for the dermatologist to observe subcutaneous structures that would be invisible otherwise. However, the use of dermoscopy is not straightforward, requiring years of practice. Moreover, the diagnosis is many times subjective and challenging to reproduce. Therefore, it is necessary to develop automatic methods that will help dermatologists provide more reliable diagnoses. Since this cancer is visible on the skin, it is potentially detectable at a very early stage when it is curable. Recent developments have converged to make fully automatic early melanoma detection a real possibility. First, the advent of dermoscopy has enabled a dramatic boost in the clinical diagnostic ability to the point that it can detect melanoma in the clinic at the earliest stages. This technology’s global adoption has allowed the accumulation of extensive collections of dermoscopy images. The development of advanced technologies in image processing and machine learning has given us the ability to distinguish malignant melanoma from the many benign mimics that require no biopsy. These new technologies should allow earlier detection of melanoma and reduce a large number of unnecessary and costly biopsy procedures. Although some of the new systems reported for these technologies have shown promise in preliminary trials, a widespread implementation must await further technical progress in accuracy and reproducibility. This thesis provides an overview of our deep learning (DL) based methods used in the diagnosis of melanoma in dermoscopy images. First, we introduce the background. Then, this paper gives a brief overview of the state-of-art article on melanoma interpret. After that, a review is provided on the deep learning models for melanoma image analysis and the main popular techniques to improve the diagnose performance. We also made a summary of our research results. Finally, we discuss the challenges and opportunities for automating melanocytic skin lesions’ diagnostic procedures. We end with an overview of a conclusion and directions for the following research plan.
|
|
| 2. |
- Nie, Yali
(författare)
-
Deep Learning Approaches towards Skin Lesion Classification with Dermoscopic Images
- 2023
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Melanoma is a skin cancer that tends to be deadly. The incidence of melanoma is currently at the highest level ever recorded in Europe, North America and Oceania. The survival rate can be significantly increased if skin lesions are identified in dermoscopic images at an early stage. In the other hand, the classification of skin lesions is incredibly challenging. Skin lesion classification using deep learning approaches has provided better results in classifying skin diseases than those of dermatologist, which is lifesaving in terms of diagnosis.This thesis presents a review of our research articles on classifying skin lesions using deep learning. Regarding the research, I have four goals concerning research frontier work, small datasets, data imbalance, and improving accuracy. In this thesis, I discuss how deep learning can classify skin diseases, summarizing the problems that remain at this stage and the outlook for the future.For the above goals, I first studied and summarized more than 200 highguality articles published over five years. I then used three versions of You only look once (Yolo) to detect skin lesions. Although there were only 200 pictures, the test was very effective for detection. I applied the five-fold algorithm to Vgg_16, trained five models, and fused them so solve the small data problem. To improve the accuracy, I also tried to combine the traditional machine learning method, i.e., the seven-point checklist, with three different backbones. Since the learning rate. Then, I also tried to use the hybrid model, combining convolutional neural networks (CNN) and transformer to train the dataset, and applied focal loss to balance the extremely unbalanced weight of the data.In addition to high-quality data sets and high-performance computers being extremely important in the research and application of deep learning, the optimization of machine learning algorithms for skin lesions can be endless
|
|
| 3. |
- Perion, P., et al.
(författare)
-
Spectral micro-CT for simultaneous gold and iodine detection, and multi-material identification
- 2024
-
Ingår i: Journal of Instrumentation. - : IOP Publishing. - 1748-0221. ; 19:4
-
Tidskriftsartikel (refereegranskat)abstract
- Multiple energy bin spectral micro-CT (SμCT) is an advanced imaging technique that allows multi-material decomposition according to their specific absorption patterns at a sub-100 μm scale. Typically, iodine is the preferred CT contrast agent for cardiovascular imaging, while gold nanoparticles have gained attention in recent years owing to their high absorption properties, biocompatibility and ability to target tumors. In this work, we demonstrate the potential for multi-material decomposition through SμCT imaging of a test sample at the PEPI lab of INFN Trieste. The sample, consisting of gold, iodine, calcium, and water, was imaged using a Pixirad1/PixieIII chromatic detector with multiple energy thresholds and a wide spectrum (100 kV) produced by a micro-focus X-ray tube. The results demonstrate the simultaneous detection and separation of the four materials at a spatial scale of 35 μm, suggesting the potential of this technique in improving material detectability and quantification in a range of pre-clinical applications, including cardiovascular and oncologic imaging.
|
|
| 4. |
- Reza, Salim, 1985-, et al.
(författare)
-
Smart dosimetry by pattern recognition using a single photon counting detector system in time over threshold mode
- 2012
-
Ingår i: Journal of Instrumentation. - 1748-0221. ; 7:1
-
Tidskriftsartikel (refereegranskat)abstract
- The function of a dosimeter is to determine the absorbed dose of radiation, for those cases in which, generally, the particular type of radiation is already known. Lately, a number of applications have emerged in which all kinds of radiation are absorbed and are sorted by pattern recognition, such as the Medipix2 application in [1]. This form of smart dosimetry enables measurements where not only the total dosage is measured, but also the contributions of different types of radiation impacting upon the detector surface. Furthermore, the use of a photon counting system, where the energy deposition can be measured in each individual pixel, ensures measurements with a high degree of accuracy in relation to the pattern recognition. In this article a Timepix [2] detector system has been used in the creation of a smart dosimeter for Alpha, Beta and Gamma radiation. When a radioactive particle hits the detector surface it generates charge clusters and those impacting upon the detector surface are read out and image processing algorithms are then used to classify each charge cluster. The individual clusters are calculated and as a result, the dosage for each type of radiation is given. In some cases, several particles can impact in roughly the same place, forming overlapping clusters. In order to handle this problem, a cluster separation method has been added to the pattern recognition algorithm. When the clusters have been separated, they are classified by shape and sorted into the correct type of radiation. The algorithms and methods used in this dosimeter have been developed so as to be simple and computationally effective, in order to enable implementation on a portable device. © 2012 IOP Publishing Ltd and SISSA.
|
|
| 5. |
- Tourancheau, Sylvain, 1982-, et al.
(författare)
-
Subjective evaluation of user experience in interactive 3D-visualization in a medical context
- 2012
-
Ingår i: Proceedings of the SPIE, vol 8318: Conference on Image Perception, Observer Performance, and Technology Assessment, San Diego, CA, USA, 4 - 9 February 2012. - : SPIE - International Society for Optical Engineering. ; , s. Art. no. 831814-
-
Konferensbidrag (refereegranskat)abstract
- New display technologies enable the usage of 3D-visualization in a medical context. Even though user performance seems to be enhanced with respect to 2D thanks to the addition of recreated depth cues, human factors, and more particularly visual comfort and visual fatigue can still be a bridle to the widespread use of these systems. This study aimed at evaluating and comparing two different 3D visualization systems (a market stereoscopic display, and a state-of-the-art multi-view display) in terms of quality of experience (QoE), in the context of interactive medical visualization. An adapted methodology was designed in order to subjectively evaluate the experience of users. 14 medical doctors and 15 medical students took part in the experiment. After solving different tasks using the 3D reconstruction of a phantom object, they were asked to judge their quality of the experience, according to specific features. They were also asked to give their opinion about the influence of 3D-systems on their work conditions. Results suggest that medical doctors are opened to 3D-visualization techniques and are confident concerning their beneficial influence on their work. However, visual comfort and visual fatigue are still an issue of 3D-displays. Results obtained with the multi-view display suggest that the use of continuous horizontal parallax might be the future response to these current limitations.
|
|
