Cancer diagnosis using deep learning: A bibliographic review

In this paper, we first describe the basics of the field of cancer diagnosis, which includes steps of cancer diagnosis followed by the typical classification methods used by doctors, providing a historical idea of cancer classification techniques to the readers. These methods include Asymmetry, Border, Color and Diameter (ABCD) method, seven-point detection method, Menzies method, and pattern analysis. They are used regularly by doctors for cancer diagnosis, although they are not considered very efficient for obtaining better performance. Moreover, considering all types of audience, the basic evaluation criteria are also discussed. The criteria include the receiver operating characteristic curve (ROC curve), Area under the ROC curve (AUC), F1 score, accuracy, specificity, sensitivity, precision, dice-coefficient, average accuracy, and Jaccard index. Previously used methods are considered inefficient, asking for better and smarter methods for cancer diagnosis. Artificial intelligence and cancer diagnosis are gaining attention as a way to define better diagnostic tools. In particular, deep neural networks can be successfully used for intelligent image analysis. The basic framework of how this machine learning works on medical imaging is provided in this study, i.e., pre-processing, image segmentation and post-processing. The second part of this manuscript describes the different deep learning techniques, such as convolutional neural networks (CNNs), generative adversarial models (GANs), deep autoencoders (DANs), restricted Boltzmann’s machine (RBM), stacked autoencoders (SAE), convolutional autoencoders (CAE), recurrent neural networks (RNNs), long short-term memory (LTSM), multi-scale convolutional neural network (M-CNN), multi-instance learning convolutional neural network (MIL-CNN). For each technique, we provide Python codes, to allow interested readers to experiment with the cited algorithms on their own diagnostic problems. The third part of this manuscript compiles the successfully applied deep learning models for different types of cancers. Considering the length of the manuscript, we restrict ourselves to the discussion of breast cancer, lung cancer, brain cancer, and skin cancer. The purpose of this bibliographic review is to provide researchers opting to work in implementing deep learning and artificial neural networks for cancer diagnosis a knowledge from scratch of the state-of-the-art achievements

Run-time optimization of adaptive irregular applications

Compared to traditional compile-time optimization, run-time optimization could offer significant performance improvements when parallelizing and optimizing adaptive irregular applications, because it performs program analysis and adaptive optimizations during program execution. Run-time techniques can succeed where static techniques fail because they exploit the characteristics of input data, programs' dynamic behaviors, and the underneath execution environment. When optimizing adaptive irregular applications for parallel execution, a common observation is that the effectiveness of the optimizing transformations depends on programs' input data and their dynamic phases. This dissertation presents a set of run-time optimization techniques that match the characteristics of programs' dynamic memory access patterns and the appropriate optimization (parallelization) transformations. First, we present a general adaptive algorithm selection framework to automatically and adaptively select at run-time the best performing, functionally equivalent algorithm for each of its execution instances. The selection process is based on off-line automatically generated prediction models and characteristics (collected and analyzed dynamically) of the algorithm's input data, In this dissertation, we specialize this framework for automatic selection of reduction algorithms. In this research, we have identified a small set of machine independent high-level characterization parameters and then we deployed an off-line, systematic experiment process to generate prediction models. These models, in turn, match the parameters to the best optimization transformations for a given machine. The technique has been evaluated thoroughly in terms of applications, platforms, and programs' dynamic behaviors. Specifically, for the reduction algorithm selection, the selected performance is within 2% of optimal performance and on average is 60% better than "Replicated Buffer," the default parallel reduction algorithm specified by OpenMP standard. To reduce the overhead of speculative run-time parallelization, we have developed an adaptive run-time parallelization technique that dynamically chooses effcient shadow structures to record a program's dynamic memory access patterns for parallelization. This technique complements the original speculative run-time parallelization technique, the LRPD test, in parallelizing loops with sparse memory accesses. The techniques presented in this dissertation have been implemented in an optimizing research compiler and can be viewed as effective building blocks for comprehensive run-time optimization systems, e.g., feedback-directed optimization systems and dynamic compilation systems

Algorithmic and Coding-theoretic Methods for Group Testing and Private Information Retrieval

In the first part of this dissertation, we consider the Group Testing (GT) problem and its two variants, the Quantitative GT (QGT) problem and the Coin Weighing (CW) problem. An instance of the GT problem includes a ground set of items that includes a small subset of defective items. The GT procedure consists of a number of tests, such that each test indicates whether or not a given subset of items includes one or more defective items. The goal of the GT procedure is to identify the subset of defective items with the minimum number of tests. Motivated by practical scenarios where the outcome of the tests can be affected by noise, we focus on the noisy GT setting, in which the outcome of a test can be flipped with some probability. In the noisy GT setting, the goal is to identify the set of defective items with high probability. We investigate the performance of two variants of the Belief Propagation (BP) algorithm for decoding of noisy non-adaptive GT under the combinatorial model for defective items. Through extensive simulations, we show that the proposed algorithms achieve higher success probability and lower false-negative and false-positive rates when compared to the traditional BP algorithm. We also consider a variation of the probabilistic GT model in which the prior probability of each item to be defective is not uniform and in which there is a certain amount of side information on the distribution of the defective items available to the GT algorithm. This dissertation focuses on leveraging the side information for improving the performance of decoding algorithms for noisy GT. First, we propose a probabilistic model, referred to as an interaction model, that captures the side information about the probability distribution of the defective items. Next, we present a decoding scheme, based on BP, that leverages the interaction model to improve the decoding accuracy. Our results indicate that the proposed algorithm achieves higher success probability and lower false-negative and false-positive rates when compared to the traditional BP, especially in the high noise regime. In the QGT problem, the result of a test reveals the number of defective items in the tested group. This is in contrast to the standard GT where the result of each test is either 1 or 0 depending on whether the tested group contains any defective items or not. In this dissertation, we study the QGT problem for the combinatorial and probabilistic models of defective items. We propose non-adaptive QGT algorithms using sparse graph codes over bi-regular and irregular bipartite graphs, and binary t-error-correcting BCH codes. The proposed schemes provide exact recovery with a probabilistic guarantee, i.e. recover all the defective items with high probability. The proposed schemes outperform existing non-adaptive QGT schemes for the sub-linear regime in terms of the number of tests required to identify all defective items with high probability. The CW problem lies at the intersection of GT and compressed sensing problems. Given a collection of coins and the total weight of the coins, where the weight of each coin is an unknown integer, the problem is to determine the weight of each coin by weighing subsets of coins on a spring scale. The goal is to minimize the average number of weighings over all possible weight configurations. Toward this goal, we propose and analyze a simple and effective adaptive weighing strategy. This is the first non-trivial achievable upper bound on the minimum expected required number of weighings. In the second part of this dissertation, we focus on the private information retrieval problem. In many practical settings, the user needs to retrieve information messages from a server in a periodic manner, over multiple rounds of communication. The messages are retrieved one at a time and the identity of future requests is not known to the server. We study the private information retrieval protocols that ensure that the identities of all the messages retrieved from the server are protected. This scenario can occur in practical settings such as periodic content download from text and multimedia repositories. We refer to this problem of minimizing the rate of data download as online private information retrieval problem. Following the previous line of work by Kadhe et al., we assume that the user knows a subset of messages in the database as side information. The identities of these messages are initially unknown to the server. Focusing on scalar-linear settings, we characterize the per-round capacity, i.e., the maximum achievable download rate at each round. The key idea of our achievability scheme is to combine the data downloaded during the current round and the previous rounds with the original side information messages and use the resulting data as side information for the subsequent rounds

CARNet:Compression Artifact Reduction for Point Cloud Attribute

A learning-based adaptive loop filter is developed for the Geometry-based Point Cloud Compression (G-PCC) standard to reduce attribute compression artifacts. The proposed method first generates multiple Most-Probable Sample Offsets (MPSOs) as potential compression distortion approximations, and then linearly weights them for artifact mitigation. As such, we drive the filtered reconstruction as close to the uncompressed PCA as possible. To this end, we devise a Compression Artifact Reduction Network (CARNet) which consists of two consecutive processing phases: MPSOs derivation and MPSOs combination. The MPSOs derivation uses a two-stream network to model local neighborhood variations from direct spatial embedding and frequency-dependent embedding, where sparse convolutions are utilized to best aggregate information from sparsely and irregularly distributed points. The MPSOs combination is guided by the least square error metric to derive weighting coefficients on the fly to further capture content dynamics of input PCAs. The CARNet is implemented as an in-loop filtering tool of the GPCC, where those linear weighting coefficients are encapsulated into the bitstream with negligible bit rate overhead. Experimental results demonstrate significant improvement over the latest GPCC both subjectively and objectively.Comment: 13pages, 8figure