Medical Image Analysis (MedIA), Boundary and Entropy-driven Adversarial Learning for Fundus Image Segmentation Shujun Wang*, Lequan Yu*, Kang Li, Xin Yang, Chi-Wing Fu, and Pheng-Ann Heng. Medical Image Computing and Computer Assisted Intervention (MICCAI), Dec 19, · PHD THESIS REPOSITORY. PhD Thesis Repository of MAHE, Manipal. List for the year No. ANALYSIS OF GENES INVOLVED IN NOTCH SIGNALING PATHWAY IN MULTIPLE VERTEBRAL SEGMENTATION DEFECTS: KMC, Manipal: Dr Girisha K M: Click here: Fast Image Retrieval Techniques for Medical Images. SOIS, Manipal. Dr. Niranjan U.C. 07 Mathieu Hatt: Mathieu Hatt is a computer scientist. He received his PhD in and his habilitation to supervise research in His main skills and expertise lie in radiomics, from automated image segmentation to features extraction, as well as machine (deep)
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The semantic image segmentation task consists of classifying each pixel of an image into an instance, where each instance corresponds to a class. This task is a part of the concept of scene understanding or better explaining the global context of an image, phd thesis on medical image segmentation. In the medical image analysis domain, image segmentation can be used for image-guided interventions, radiotherapy, or improved radiological diagnostics.
In this review, we categorize the leading deep learning-based medical and non-medical image segmentation solutions into six main groups of deep architectural, data synthesis-based, loss function-based, phd thesis on medical image segmentation, sequenced models, weakly supervised, and multi-task methods and provide a comprehensive review of the contributions in each of these groups.
Further, for each group, we analyze each variant of these groups and discuss the limitations of the current approaches and present potential future research directions phd thesis on medical image segmentation semantic image segmentation.
This is a preview of subscription content, access via your institution. Rent this article via DeepDyve. Abdulla W Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow. Abhishek K, Hamarneh G Mask2Lesion: mask-constrained adversarial skin lesion image synthesis. In: Medical image computing and computer-assisted intervention workshop on simulation and synthesis in medical imaging, pp 71— Abhishek K, Hamarneh G, Drew MS Illumination-based transformations improve skin lesion segmentation in dermoscopic images.
Adams RA, Fournier JJ Sobolev spaces. Elsevier, Amsterdam. MATH Google Scholar. Afshari S, BenTaieb A, Mirikharaji Z, Hamarneh G Weakly supervised fully convolutional network for PET lesion segmentation.
In: Medical imaging image processing, international society for optics and photonics, volp K. Alom MZ, Yakopcic C, Hasan M, Taha TM, Asari VK Recurrent phd thesis on medical image segmentation U-Net for medical image segmentation. J Med Imag 6 1 Article Google Scholar. Amirul Islam M, Rochan M, Bruce ND, Wang Y Gated feedback refinement network for dense image labeling. In: Proceedings of the IEEE conference on computer vision and pattern recognition, phd thesis on medical image segmentation, pp — Amit Y Deep learning with asymmetric connections and hebbian updates.
Front Comput Neurosci. Anantharaman R, Velazquez M, phd thesis on medical image segmentation, Lee Y Utilizing Mask R-CNN for detection and segmentation of oral diseases. In: IEEE international conference on bioinformatics and biomedicine, pp — Badrinarayanan V, Handa A, Cipolla R Segnet: a deep convolutional encoder-decoder architecture for image segmentation.
Bai W, Suzuki Phd thesis on medical image segmentation, Qin C, Tarroni G, Oktay O, Matthews PM, Rueckert D Recurrent neural networks for aortic image sequence segmentation with sparse annotations, phd thesis on medical image segmentation. In: International conference on medical image computing and computer-assisted intervention. Springer, pp — Bellec G, Scherr F, Hajek E, Salaj D, Legenstein R, Maass W Biologically inspired alternatives to backpropagation through time for learning in recurrent neural nets.
Bengio Y, Frasconi P Credit assignment through time: alternatives to backpropagation. In: Advances in neural information processing systems, pp 75— Benoit-Cattin H, Collewet G, Belaroussi B, Saint-Jalmes H, Odet C The SIMRI project: a versatile and interactive MRI simulator. J Magn Reson 1 — BenTaieb A, Hamarneh G Topology aware fully convolutional networks for histology gland segmentation. In: International conference on medical image computing and computer assisted intervention.
Berman M, Blaschko MB, Triki AR, Yu J a Yes, IoU loss is submodular-as a function of the mispredictions. Berman M, Rannen Triki A, Blaschko MB b The Lovász-Softmax loss: a tractable surrogate for the optimization of the intersection-over-union measure in neural networks. Bischke B, Helber P, Folz J, Borth D, Dengel A Multi-task learning for segmentation of building footprints with deep neural networks.
In: IEEE international conference on image processing. IEEE, pp — Bonta LR, Kiran NU Efficient segmentation of medical images using dilated residual networks. In: Computer aided intervention and diagnostics in clinical and medical images. Springer, pp 39— Borji A, Cheng MM, Hou Q, Jiang H, Li J Salient object detection: a survey. Comput Vis Media 5 2 — Brostow GJ, Shotton J, Fauqueur J, Cipolla R Segmentation and recognition using structure from motion point clouds.
In: Lecture notes in computer science. Springer, Berlin, pp 44— Brostow GJ, Fauqueur J, Cipolla R Semantic object classes in video: a high-definition ground truth database. Pattern Recognit Lett 30 2 — Brügger R, Baumgartner CF, Konukoglu E A partially reversible U-Net for memory-efficient volumetric image segmentation. Caliva F, Iriondo C, Martinez AM, Majumdar S, Pedoia V Distance map loss penalty term for semantic segmentation.
In: International conference on medical imaging with deep learning. Caruana R Multitask learning. Mach Learn 28 1 — MathSciNet Article Google Scholar. Chaichulee S, Villarroel M, Jorge J, Arteta C, Green G, McCormick K, Zisserman A, Tarassenko L Multi-task convolutional neural network for patient detection and skin segmentation in continuous non-contact vital sign monitoring.
Chakravarty A, Sivaswamy J RACE-Net: a recurrent neural network for biomedical image segmentation. IEEE J Biomed Health Inform 23 3 — Challenge G Grand challenges in biomedical image analysis. Chartsias A, Joyce T, Dharmakumar R, Tsaftaris SA Adversarial image synthesis for unpaired multi-modal cardiac data.
In: International workshop on simulation and synthesis in medical imaging. Springer, pp 3— Chen LC, Yang Y, Wang J, Xu W, Yuille AL Attention to scale: scale-aware semantic image segmentation. Chen LC, Papandreou G, Kokkinos I, Murphy K, Yuille AL a Deeplab: semantic image segmentation with deep convolutional nets, phd thesis on medical image segmentation, atrous convolution, and fully connected crfs.
IEEE Trans Pattern Anal Mach Intell 40 4 — Chen LC, Papandreou G, Schroff F, Adam H b Rethinking atrous convolution for semantic image segmentation. Chen LC, Collins M, Zhu Y, Papandreou G, Zoph B, Schroff F, Adam H, Shlens J a Searching for efficient multi-scale architectures for dense image prediction. In: Advances in neural information processing systems, pp — Chen LC, Zhu Y, Papandreou G, Schroff F, Adam H b Encoder-decoder with atrous separable convolution for semantic image segmentation.
In: Proceedings of the European conference on computer vision, pp — Chen X, Williams BM, Vallabhaneni SR, Czanner G, Williams R, Zheng Y Learning active contour models for medical image segmentation. Cherian A, Sullivan A Sem-GAN: semantically-consistent image-to-image translation. In: IEEE winter conference on applications of computer vision WACV.
Choi J, Kim T, phd thesis on medical image segmentation, Kim C Self-ensembling with gan-based data augmentation for domain adaptation in semantic segmentation. In: Proceedings of the IEEE international conference on computer vision, pp — Chollet F Xception: deep learning with depthwise separable convolutions. Cireşan D, Meier U, Schmidhuber J Multi-column deep neural networks for image classification.
In: IEEE conference on computer vision and pattern recognition. Cireşan DC, Meier U, Masci J, Gambardella LM, Schmidhuber J High-performance neural networks for visual object classification. Cohen JP, Luck M, Honari S Distribution matching losses can hallucinate features in medical image translation.
In: Medical image computing and computer assisted intervention — MICCAI Cordts M, Omran M, Ramos S, Rehfeld T, Enzweiler M, Benenson R, Franke U, Roth S, Schiele B The cityscapes dataset for semantic urban scene understanding. Costa P, Galdran A, Meyer MI, Abràmoff MD, Niemeijer M, Mendonça AM, Campilho A Towards adversarial retinal image synthesis.
Couprie C, Farabet C, Najman L, LeCun Y Indoor semantic segmentation using depth information. Czarnecki WM, Osindero S, Jaderberg M, Swirszcz G, Pascanu R Sobolev training for neural networks. Dai W, Dong N, Wang Z, Liang X, Zhang H, Xing EP SCAN: structure correcting adversarial network for organ segmentation in chest X-rays. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Drobnjak I, Gavaghan D, Süli E, Pitt-Francis J, Jenkinson M Development of a functional magnetic resonance imaging simulator for modeling realistic rigid-body motion artifacts.
Magn Reson Med 56 2 — Drobnjak I, Pell GS, Jenkinson M Simulating the effects of time-varying magnetic fields with a realistic simulated scanner. Magn Reson Imaging 28 7 — Drozdzal M, Chartrand G, Vorontsov E, Shakeri M, Di Jorio L, Tang A, Romero A, Bengio Y, Pal C, Kadoury S Learning normalized inputs for iterative estimation in medical image segmentation.
Med Image Anal — Everingham M, Gool LV, Williams CKI, Winn J, Zisserman A The pascal visual object classes VOC challenge.
Int J Comput Vis 88 2 — Everingham M, Van Gool L, Williams CKI, Winn J, Zisserman A The PASCAL visual object classes challenge VOC results. Everingham M, Eslami SA, Van Gool L, Williams CK, Winn J, Zisserman A The PASCAL visual phd thesis on medical image segmentation classes challenge: a retrospective.
PhD Defense: Deep Learning for Medical Image and Signal Understanding
, time: 55:54Lequan Yu Homepage
Journal of medical imaging and radiation sciences (): (Song et al. ) Song, Qi, et al. “Optimal co-segmentation of tumor in PET-CT images with context information.” IEEE transactions on medical imaging (): Since age 15 or so, the main goal of professor Jürgen Schmidhuber has been to build a self-improving Artificial Intelligence (AI) smarter than himself, then retire. His lab's Deep Learning Neural Networks based on ideas published in the "Annus Mirabilis" have revolutionised machine learning and AI. By the mid s, they were on 3 billion devices, and used billions of times per day Dec 19, · PHD THESIS REPOSITORY. PhD Thesis Repository of MAHE, Manipal. List for the year No. ANALYSIS OF GENES INVOLVED IN NOTCH SIGNALING PATHWAY IN MULTIPLE VERTEBRAL SEGMENTATION DEFECTS: KMC, Manipal: Dr Girisha K M: Click here: Fast Image Retrieval Techniques for Medical Images. SOIS, Manipal. Dr. Niranjan U.C. 07
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