De-speckling of Optical Coherence Tomography Images Using Anscombe Transform and a Noisier2noise Model

Optical Coherence Tomography (OCT) image denoising is a fundamental problem as OCT images suffer from multiplicative speckle noise, resulting in poor visibility of retinal layers. The traditional denoising methods consider specific statistical properties of the noise, which are not always known. Furthermore, recent deep learning-based denoising methods require paired noisy and clean images, which are often difficult to obtain, especially medical images. Noise2Noise family architectures are generally proposed to overcome this issue by learning without noisy-clean image pairs. However, for that, multiple noisy observations from a single image are typically needed. Also, sometimes the experiments are demonstrated by simulating noises on clean synthetic images, which is not a realistic scenario. This work shows how a single real-world noisy observation of each image can be used to train a denoising network. Along with a theoretical understanding, our algorithm is experimentally validated using a publicly available OCT image dataset. Our approach incorporates Anscombe transform to convert the multiplicative noise model to additive Gaussian noise to make it suitable for OCT images. The quantitative results show that this method can outperform several other methods where a single noisy observation of an image is needed for denoising. The code and implementation of this paper will be available publicly upon acceptance of this paper.Comment: Accepted to MICCAI OMIA workshop 202

Revisiting model self-interpretability in a decision-theoretic way for binary medical image classification

Interpretability is highly desired for deep neural network-based classifiers, especially when addressing high-stake decisions in medical imaging. Commonly used post-hoc interpretability methods have the limitation that they can produce plausible but different interpretations of a given model, leading to ambiguity about which one to choose. To address this problem, a novel decision-theory-motivated approach is investigated to establish a self-interpretable model, given a pretrained deep binary black-box medical image classifier. This approach involves utilizing a self-interpretable encoder-decoder model in conjunction with a single-layer fully connected network with unity weights. The model is trained to estimate the test statistic of the given trained black-box deep binary classifier to maintain a similar accuracy. The decoder output image, referred to as an equivalency map, is an image that represents a transformed version of the to-be-classified image that, when processed by the fixed fully connected layer, produces the same test statistic value as the original classifier. The equivalency map provides a visualization of the transformed image features that directly contribute to the test statistic value and, moreover, permits quantification of their relative contributions. Unlike the traditional post-hoc interpretability methods, the proposed method is self-interpretable, quantitative, and fundamentally based on decision theory. Detailed quantitative and qualitative analysis have been performed with three different medical image binary classification tasks

Deep Generative Modeling Based Retinal Image Analysis

In the recent past, deep learning algorithms have been widely used in retinal image analysis (fundus and OCT) to perform tasks like segmentation and classification. But to build robust and highly efficient deep learning models amount of the training images, the quality of the training images is extremely necessary. The quality of an image is also an extremely important factor for the clinical diagnosis of different diseases. The main aim of this thesis is to explore two relatively under-explored area of retinal image analysis, namely, the retinal image quality enhancement and artificial image synthesis. In this thesis, we proposed a series of deep generative modeling based algorithms to perform these above-mentioned tasks. From a mathematical perspective, the generative model is a statistical model of the joint probability distribution between an observable variable and a target variable. The generative adversarial network (GAN), variational auto-encoder(VAE) are some popular generative models. Generative models can be used to generate new samples from a given distribution. The OCT images have inherent speckle noise in it, fundus images do not suffer from noises in general, but the newly developed tele-ophthalmoscope devices produce images with relatively low spatial resolution and blur. Different GAN based algorithms were developed to generate corresponding high-quality images fro its low-quality counterpart. A combination of residual VAE and GAN was implemented to generate artificial retinal fundus images with their corresponding artificial blood vessel segmentation maps. This will not only help to generate new training images as many as needed but also will help to reduce the privacy issue of releasing personal medical data

Can Musical Emotion Be Quantified With Neural Jitter Or Shimmer? A Novel EEG Based Study With Hindustani Classical Music

The term jitter and shimmer has long been used in the domain of speech and acoustic signal analysis as a parameter for speaker identification and other prosodic features. In this study, we look forward to use the same parameters in neural domain to identify and categorize emotional cues in different musical clips. For this, we chose two ragas of Hindustani music which are conventionally known to portray contrast emotions and EEG study was conducted on 5 participants who were made to listen to 3 min clip of these two ragas with sufficient resting period in between. The neural jitter and shimmer components were evaluated for each experimental condition. The results reveal interesting information regarding domain specific arousal of human brain in response to musical stimuli and also regarding trait characteristics of an individual. This novel study can have far reaching conclusions when it comes to modeling of emotional appraisal. The results and implications are discussed in detail.Comment: 6 pages, 12 figures, Presented in 4th International Conference on Signal Processing and Integrated Networks (SPIN) 201

Generative Modeling for Retinal Fundus Image Synthesis

Medical imaging datasets typically do not contain many training images, usually being deficient for training deep learning networks.We propose a deep residual variational auto-encoder and a generative adversarial network that can generate a synthetic retinal fundus image dataset with corresponding blood vessel annotation. Ourinitial experiments produce results with higher scores than the stateof the art for verifying that the structural statistics of our generatedimages are compatible with real fundus images. The successful application of generative models to generate synthetic medical datawill not only help to mitigate the small dataset problem but will alsoaddress the privacy concerns associated with medical datasets

Explainable deep learning models in medical image analysis

Deep learning methods have been very effective for a variety of medical diagnostic tasks and has even beaten human experts on some of those. However, the black-box nature of the algorithms has restricted clinical use. Recent explainability studies aim to show the features that influence the decision of a model the most. The majority of literature reviews of this area have focused on taxonomy, ethics, and the need for explanations. A review of the current applications of explainable deep learning for different medical imaging tasks is presented here. The various approaches, challenges for clinical deployment, and the areas requiring further research are discussed here from a practical standpoint of a deep learning researcher designing a system for the clinical end-users.Comment: Preprint submitted to J.Imaging, MDP