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Brendel et al. propose a decision-based black-box attacks against (deep convolutional) neural networks. Specifically, the so-called Boundary Attack starts with a random adversarial example (i.e. random noise that is not classified as the image to be attacked) and randomly perturbs this initialization to move closer to the target image while remaining misclassified. In pseudo code, the algorithm is described in Algorithm 1. Key component is the proposal distribution $P$ used to guide the adversarial perturbation in each step. In practice, they use a maximum-entropy distribution (e.g. uniform) with a couple of constraints: the perturbed sample is a valid image; the perturbation has a specified relative size, i.e. $\|\eta^k\|_2 = \delta d(o, \tilde{o}^{k-1})$; and the perturbation reduces the distance to the target image $o$: $d(o, \tilde{o}^{k-1}) – d(o,\tilde{o}^{k-1} + \eta^k)=\epsilon d(o, \tilde{o}^{k-1})$. This is approximated by sampling from a standard Gaussian, clipping and rescaling and projecting the perturbation onto the $\epsilon$-sphere around the image. In experiments, they show that this attack is competitive to white-box attacks and can attack real-world systems. https://i.imgur.com/BmzhiFP.png Algorithm 1: Minimal pseudo code version of the boundary attack. Also find this summary at [davidstutz.de](https://davidstutz.de/category/reading/). |
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This paper focuses on the application of deep learning to the docking problem within rational drug design. The overall objective of drug design or discovery is to build predictive models of how well a candidate compound (or "ligand") will bind with a target protein, to help inform the decision of what compounds are promising enough to be worth testing in a wet lab. Protein binding prediction is important because many small-molecule drugs, which are designed to be small enough to get through cell membranes, act by binding to a specific protein within a disease pathway, and thus blocking that protein's mechanism. The formulation of the docking problem, as best I understand it, is: 1. A "docking program," which is generally some model based on physical and chemical interactions, takes in a (ligand, target protein) pair, searches over a space of ways the ligand could orient itself within the binding pocket of the protein (which way is it facing, where is it twisted, where does it interact with the protein, etc), and ranks them according to plausibility 2. A scoring function takes in the binding poses (otherwise known as binding modes) ranked the highest, and tries to predict the affinity strength of the resulting bond, or the binary of whether a bond is "active". The goal of this paper was to interpose modern machine learning into the second step, as alternative scoring functions to be applied after the pose generation . Given the complex data structure that is a highly-ranked binding pose, the hope was that deep learning would facilitate learning from such a complicated raw data structure, rather than requiring hand-summarized features. They also tested a similar model structure on the problem of predicting whether a highly ranked binding pose was actually the empirically correct one, as determined by some epsilon ball around the spatial coordinates of the true binding pose. Both of these were binary tasks, which I understand to be 1. Does this ranked binding pose in this protein have sufficiently high binding affinity to be "active"? This is known as the "virtual screening" task, because it's the relevant task if you want to screen compounds in silico, or virtually, before doing wet lab testing. 2. Is this ranked binding pose the one that would actually be empirically observed? This is known as the "binding mode prediction" task The goal of this second task was to better understand biases the researchers suspected existed in the underlying dataset, which I'll explain later in this post. The researchers used a graph convolution architecture. At a (very) high level, graph convolution works in a way similar to normal convolution - in that it captures hierarchies of local patterns, in ways that gradually expand to have visibility over larger areas of the input data. The distinction is that normal convolution defines kernels over a fixed set of nearby spatial coordinates, in a context where direction (the pixel on top vs the pixel on bottom, etc) is meaningful, because photos have meaningful direction and orientation. By contrast, in a graph, there is no "up" or "down", and a given node doesn't have a fixed number of neighbors (whereas a fixed pixel in 2D space does), so neighbor-summarization kernels have to be defined in ways that allow you to aggregate information from 1) an arbitrary number of neighbors, in 2) a manner that is agnostic to orientation. Graph convolutions are useful in this problem because both the summary of the ligand itself, and the summary of the interaction of the posed ligand with the protein, can be summarized in terms of graphs of chemical bonds or interaction sites. Using this as an architectural foundation, the authors test both solo versions and ensembles of networks: https://i.imgur.com/Oc2LACW.png 1. "L" - A network that uses graph convolution to summarize the ligand itself, with no reference to the protein it's being tested for binding affinity with 2. "LP" - A network that uses graph convolution on the interaction points between the ligand and protein under the binding pose currently being scored or predicted 3. "R" - A simple network that takes into account the rank assigned to the binding pose by the original docking program (generally used in combination with one of the above). The authors came to a few interesting findings by trying different combinations of the above model modules. First, they found evidence supporting an earlier claim that, in the dataset being used for training, there was a bias in the positive and negative samples chosen such that you could predict activity of a ligand/protein binding using *ligand information alone.* This shouldn't be possible if we were sampling in an unbiased way over possible ligand/protein pairs, since even ligands that are quite effective with one protein will fail to bind with another, and it shouldn't be informationally possible to distinguish the two cases without protein information. Furthermore, a random forest on hand-designed features was able to perform comparably to deep learning, suggesting that only simple features are necessary to perform the task on this (bias and thus over-simplified) Specifically, they found that L+LP models did no better than models of L alone on the virtual screening task. However, the binding mode prediction task offered an interesting contrast, in that, on this task, it's impossible to predict the output from ligand information alone, because by construction each ligand will have some set of binding modes that are not the empirically correct one, and one that is, and you can't distinguish between these based on ligand information alone, without looking at the actual protein relationship under consideration. In this case, the LP network did quite well, suggesting that deep learning is able to learn from ligand-protein interactions when it's incentivized to do so. Interestingly, the authors were only able to improve on the baseline model by incorporating the rank output by the original docking program, which you can think of an ensemble of sorts between the docking program and the machine learning model. Overall, the authors' takeaways from this paper were that (1) we need to be more careful about constructing datasets, so as to not leak information through biases, and (2) that graph convolutional models are able to perform well, but (3) seem to be capturing different things than physics-based models, since ensembling the two together provides marginal value. |
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A very simple (but impractical) discrete model of subclonal evolution would include the following events: * Division of a cell to create two cells: * **Mutation** at a location in the genome of the new cells * Cell death at a new timestep * Cell survival at a new timestep Because measurements of mutations are usually taken at one time point, this is taken to be at the end of a time series of these events, where a tiny of subset of cells are observed and a **genotype matrix** $A$ is produced, in which mutations and cells are arbitrarily indexed such that $A_{i,j} = 1$ if mutation $j$ exists in cell $i$. What this matrix allows us to see is the proportion of cells which *both have mutation $j$*. Unfortunately, I don't get to observe $A$, in practice $A$ has been corrupted by IID binary noise to produce $A'$. This paper focuses on difference inference problems given $A'$, including *inferring $A$*, which is referred to as **`noise_elimination`**. The other problems involve inferring only properties of the matrix $A$, which are referred to as: * **`noise_inference`**: predict whether matrix $A$ would satisfy the *three gametes rule*, which asks if a given genotype matrix *does not describe a branching phylogeny* because a cell has inherited mutations from two different cells (which is usually assumed to be impossible under the infinite sites assumption). This can be computed exactly from $A$. * **Branching Inference**: it's possible that all mutations are inherited between the cells observed; in which case there are *no branching events*. The paper states that this can be computed by searching over permutations of the rows and columns of $A$. The problem is to predict from $A'$ if this is the case. In both problems inferring properties of $A$, the authors use fully connected networks with two hidden layers on simulated datasets of matrices. For **`noise_elimination`**, computing $A$ given $A'$, the authors use a network developed for neural machine translation called a [pointer network][pointer]. They also find it necessary to map $A'$ to a matrix $A''$, turning every element in $A'$ to a fixed length row containing the location, mutation status and false positive/false negative rate. Unfortunately, reported results on real datasets are reported only for branching inference and are limited by the restriction on input dimension. The inferred branching probability reportedly matches that reported in the literature. [pointer]: https://arxiv.org/abs/1409.0473 |
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Problem ========= Brain MRI segmentation using adversarial training approach Dataset ====== 55 T1 weighted brain MR images (35 adults and 20 elders) with respective label maps. Contributions ========== 1. The authors suggest an adversarial loss in addition to the traditional loss. 2. The authors compare 2 Generator (Segmentor) models - Fully convolutional and dilated networks. https://i.imgur.com/orhWhoM.png Dilated network ------------------ Using conv layers, allows for larger receptive field with fewer trainable weights (compared to the FCN option). However, the authors claim the adversarial loss contributes more when applying the FCN model |
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1. U-NET learns segmentation in an end to end images. 2. They solved Challenges are * Very few annotated images (approx. 30 per application). * Touching objects of the same class. # How: * Input image is fed in to the network, then the data is propagated through the network along all possible path at the end segmentation maps comes out. * In U-net architecture, each blue box corresponds to a multi-channel feature map. The number of channels is denoted on top of the box. The x-y-size is provided at the lower left edge of the box. White boxes represent copied feature maps. The arrows denote the different operations. https://i.imgur.com/Usxmv6r.png * In two 3x3 convolutions (unpadded convolutions), each followed by a rectified linear unit (ReLU) and a 2x2 max pooling operation with stride 2for down sampling. At each down sampling step they double the number of feature channels. * Contracting path (left side from up to down) is increases the feature channel and reduces the steps and an expansive path (right side from down to up) consists of sequence of up convolution and concatenation with the corresponds high resolution features from contracting path. * The network does not have any fully connected layers and only uses the valid part of each convolution, i.e., the segmentation map only contains the pixels, for which the full context is available in the input image. ## Challenges: 1. Overlap-tile strategy for seamless segmentation of arbitrary large images: * To predict the pixels in the border region of the image, the missing context is extrapolated by mirroring the input image. * In fig, segmentation of the yellow area uses input data of the blue area and the raw data extrapolation by mirroring. https://i.imgur.com/NUbBRUG.png 2. Augment training data using deformation: * They use excessive data augmentation by applying elastic deformations to the available training images. * Then the network to learn invariance to such deformations, without the need to see these transformations in the annotated image corpus. * Deformation used to be the most common variation in tissue and realistic deformations can be simulated efficiently. https://i.imgur.com/CyC8Hmd.png 3. Segmentation of touching object of the same class: * They propose the use of a weighted loss, where the separating background labels between touching cells obtain a large weight in the loss function. * Ensure separation of touching objects, in that segmentation mask for training (inserted background between touching objects) get the loss weights for each pixel. https://i.imgur.com/ds7psDB.png 4. Segmentation of neural structure in electro-microscopy(EM): * Ongoing challenge since ISBI 2012 in this dataset structures with low contrast, fuzzy membranes and other cell components. * The training data is a set of 30 images (512x512 pixels) from serial section transmission electron microscopy of the Drosophila first instar larva ventral nerve cord (VNC). Each image comes with corresponding fully annotated ground truth segmentation map for cells(white) and membranes (black). * An evaluation can be obtained by sending the predicted membrane probability map to the organizers. The evaluation is done by thresholding the map at 10 different levels and computation of the warping error, the Rand error and the pixel error. ### Results: * The u-net (averaged over 7 rotated versions of the input data) achieves with-out any further pre or post-processing a warping error of 0.0003529, a rand-error of 0.0382 and a pixel error of 0.0611. https://i.imgur.com/6BDrByI.png * ISBI cell tracking challenge 2015, one of the dataset contains cell phase contrast microscopy has strong shape variations,weak outer borders, strong irrelevant inner borders and cytoplasm has same structure like background. https://i.imgur.com/vDflYEH.png * The first data set PHC-U373 contains Glioblastoma-astrocytoma U373 cells on a polyacrylimide substrate recorded by phase contrast microscopy- It contains 35 partially annotated training images. Here we achieve an average IOU ("intersection over union") of 92%,which is significantly better than the second best algorithm with 83%. https://i.imgur.com/of4rAYP.png * The second data set DIC-HeLa are HeLa cells on a flat glass recorded by differential interference contrast (DIC) microscopy - It contains 20 partially annotated training images. Here we achieve an average IOU of 77.5% which is significantly better than the second best algorithm with 46%. https://i.imgur.com/Y9wY6Lc.png |