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Questions related from Sivaramakrishnan Rajaraman
Let me assume that the organizations would afford to have high performance super computing to work on high resolution images directly without the need for image down scaling for machine vision...
12 December 2019 3,801 14 View
Please find attached the picture. I have a bigger image (in orange) with two sub-sections (in blue). I have to translate (superimpose) different regions within these sub-sections with another...
11 November 2019 3,345 7 View
I have observed that the performance of a machine learning model trained on a larger dataset is not as convincing as the one trained on a smaller (different) dataset. What quantitative measures I...
02 February 2019 5,555 5 View
I have been experimenting with greyscale CXRs and also with different colormaps to observe a possible difference in the performance of deep learning models. Has anyone identified the best colormap...
12 December 2018 4,208 4 View
I could come across a few studies that perform AP/PA Chest X-ray view classification using machine learning. However I'm puzzled if this is a truly noteworthy/real problem in the clinical setting....
11 November 2018 8,491 7 View
I am studying the performance of deep learning (DL) models toward abnormality detection in chest X-rays. Due to sparsity of data, I augmented the data using different augmentation strategies...
11 November 2018 417 7 View
With Python and Keras, I have been using three different deep learning models and extracting features from different layers for the given images. I'm getting feature vectors of dimensions...
04 April 2018 6,196 2 View
I have been using three different deep learning models and extracting features from the given images. I'm getting feature vectors of dimensions (1,4096), (1,2048) and (1,21024) from three...
04 April 2018 2,910 1 View
in layman terms, can you explain the architectural disparity between Inception, Residual networks and DenseNets and how they benefi classification?
03 March 2018 3,934 2 View
I’ve been using Bayesian optimation for finding optimal hyper parameters that varies for each cross-validated fold? How do you settle with the optimum for the entire dataset?
03 March 2018 2,360 3 View
I’m working with segmented cells from thin blood smear images and using deep learning models to classify parasitic and uninfected cells. I obtained the following values: Accuracy: 0.986, AUC:...
01 January 2018 4,459 5 View
I've been statistically validating the performance of different Deep Learning models. In the process, I could find that there is no statistically significant difference in performance between the...
12 December 2017 1,383 2 View
I've been using GIST, HOG and SURF descriptors for extracting features from different collections of Chest-X-rays. I could repeatedly see that one descriptor performs better than the other and the...
11 November 2017 9,460 0 View
Commonly we extract features using: net = googlenet() %Extract features featureLayer = 'pool5-drop_7x7_s1'; #the last layer before softmax How to extract features from a different layer earlier in...
11 November 2017 7,929 5 View
I've been using the pre-trained Deep Learning models as feature extractors on a project involving chest x-ray images. Conventionally, we extract the features from the layer just before the...
11 November 2017 5,301 2 View
Hello everyone, I have a query. I could find some papers that randomly divide the data set into train/test (70/30 or 90/10) at different seeds points (number of seeds: 10-15). It means for a given...
10 October 2017 6,620 4 View
Do we have a standardized open access Ziehl Neelsen Sputum smear microscopy image database? Kindly suggest the available options?
07 July 2016 8,901 0 View
