Sai Saketh Chennamsetty

Bio: Sai Saketh Chennamsetty is an academic researcher from Indian Institute of Technology Madras. The author has contributed to research in topics: Convolutional neural network & Probability distribution. The author has an hindex of 5, co-authored 6 publications receiving 372 citations.

Generative adversarial networks for brain lesion detection

Abstract: Manual segmentation of brain lesions from Magnetic Resonance Images (MRI) is cumbersome and introduces errors due to inter-rater variability. This paper introduces a semi-supervised technique for detection of brain lesion from MRI using Generative Adversarial Networks (GANs). GANs comprises of a Generator network and a Discriminator network which are trained simultaneously with the objective of one bettering the other. The networks were trained using non lesion patches (n=13,000) from 4 different MR sequences. The network was trained on BraTS dataset and patches were extracted from regions excluding tumor region. The Generator network generates data by modeling the underlying probability distribution of the training data, (PData). The Discriminator learns the posterior probability P (Label Data) by classifying training data and generated data as “Real” or “Fake” respectively. The Generator upon learning the joint distribution, produces images/patches such that the performance of the Discriminator on them are random, i.e. P (Label Data = GeneratedData) = 0.5. During testing, the Discriminator assigns posterior probability values close to 0.5 for patches from non lesion regions, while patches centered on lesion arise from a different distribution (PLesion) and hence are assigned lower posterior probability value by the Discriminator. On the test set (n=14), the proposed technique achieves whole tumor dice score of 0.69, sensitivity of 91% and specificity of 59%. Additionally the generator network was capable of generating non lesion patches from various MR sequences.

Classification of Breast Cancer Histology Image using Ensemble of Pre-trained Neural Networks

Abstract: Breast cancer is one of the most commonly occurring types of cancer and the treatment administered to a subject is dependent on the grade or type of the lesion. In this manuscript, we make use of an ensemble of convolutional neural networks (CNN) to classify histology images as Normal, In-situ, Benign or Invasive. The performance of CNN is dependent on the network architecture, number of training instances and also on the data normalization scheme. However, there exists neither a single architecture nor a pre-processing regime that promises best performance. For the reason stated above, we use 3 CNNs trained on different pre-processing regimes to form an ensemble. On the held out test data (n = 40), the proposed scheme achieved an accuracy of 97.5%. On the challenge data (n = 100) provided by the organizers, the proposed technique achieved an accuracy of 87% and was jointly adjudged as the top performing algorithm for the task of classification of breast cancer from histology images.

Ensemble of Convolutional Neural Networks for Automatic Grading of Diabetic Retinopathy and Macular Edema.

Abstract: In this manuscript, we automate the procedure of grading of diabetic retinopathy and macular edema from fundus images using an ensemble of convolutional neural networks The availability of limited amount of labeled data to perform supervised learning was circumvented by using transfer learning approach The models in the ensemble were pre-trained on a large dataset comprising natural images and were later fine-tuned with the limited data for the task of choice For an image, the ensemble of classifiers generate multiple predictions, and a max-voting based approach was utilized to attain the final grade of the anomaly in the image For the task of grading DR, on the test data (n=56), the ensemble achieved an accuracy of 839\%, while for the task for grading macular edema the network achieved an accuracy of 9545% (n=44)

Deep Learning Applications in Medical Image Analysis

Abstract: The tremendous success of machine learning algorithms at image recognition tasks in recent years intersects with a time of dramatically increased use of electronic medical records and diagnostic imaging. This review introduces the machine learning algorithms as applied to medical image analysis, focusing on convolutional neural networks, and emphasizing clinical aspects of the field. The advantage of machine learning in an era of medical big data is that significant hierarchal relationships within the data can be discovered algorithmically without laborious hand-crafting of features. We cover key research areas and applications of medical image classification, localization, detection, segmentation, and registration. We conclude by discussing research obstacles, emerging trends, and possible future directions.

Indian Diabetic Retinopathy Image Dataset (IDRiD): A Database for Diabetic Retinopathy Screening Research

Abstract: Diabetic Retinopathy is the most prevalent cause of avoidable vision impairment, mainly affecting the working-age population in the world Recent research has given a better understanding of the requirement in clinical eye care practice to identify better and cheaper ways of identification, management, diagnosis and treatment of retinal disease The importance of diabetic retinopathy screening programs and difficulty in achieving reliable early diagnosis of diabetic retinopathy at a reasonable cost needs attention to develop computer-aided diagnosis tool Computer-aided disease diagnosis in retinal image analysis could ease mass screening of populations with diabetes mellitus and help clinicians in utilizing their time more efficiently The recent technological advances in computing power, communication systems, and machine learning techniques provide opportunities to the biomedical engineers and computer scientists to meet the requirements of clinical practice Diverse and representative retinal image sets are essential for developing and testing digital screening programs and the automated algorithms at their core To the best of our knowledge, IDRiD (Indian Diabetic Retinopathy Image Dataset), is the first database representative of an Indian population It constitutes typical diabetic retinopathy lesions and normal retinal structures annotated at a pixel level The dataset provides information on the disease severity of diabetic retinopathy, and diabetic macular edema for each image This makes it perfect for development and evaluation of image analysis algorithms for early detection of diabetic retinopathy