Key steps in neurodegeneration detection algorithm. by Andrew S. Clark (16510496)

Particle tracking validation of the S8 algorithm. by Jennifer M. Knighten (15453354)

<b>DDS3 - Dataset of mosaic sputum smear microscopy images for evaluation of bacillus detection algorithms</b> by Marly G F Costa (19812192)

“…This dataset was generated to enable the training, validation and testing of bacillus detection algorithms. There are 8,000 mosaic images with corresponding binary masks. 6,000 for training set, 1,000 for validation set and 1,000 for testing set. …”

Comparison of images before and after pre-processing: the original image (left) and after applying top-hat and bottom-hat filters (right). by Rinrada Tanthanathewin (18611171)

An example of a histogram of the normalized intensity of an OCT image. by Rinrada Tanthanathewin (18611171)

Examples of results from two different OCT images shown in the left and right columns. by Rinrada Tanthanathewin (18611171)

ILM (pink line) and RPE (green line) layers in a normal OCT image. by Rinrada Tanthanathewin (18611171)

The illustration depicts our CQ-CNN architecture for binary image classification. by Mominul Islam (3513758)

An example of an OCT image of a DR case (left) and illustration of relevant parts (right): EX-AN blobs (yellow blobs) in the area of interest (a blue region), the ILM (a red line)... by Rinrada Tanthanathewin (18611171)

Elsafty_Code_1; Segment & Localize Using a Pen by Ahmed Elsafty (16943883)

Elsafty_Code_1; segment & localize using a pen by Ahmed Elsafty (16943883)

<b>DDS1 - Dataset of patch images derived from sputum smear microscopy images </b><b>for the development of bacillus detection methods</b> by Marly G F Costa (19812192)

“…This dataset was generated to enable the training, validation, and testing of algorithms for bacilli detection in sputum smear microscopy images.…”

Experimental setup and trajectory estimation methodology. by Nouman Javed (16624785)

Image analysis pipeline steps overview. by Gianluigi Paolillo (12009500)

Image3_DFUCare: deep learning platform for diabetic foot ulcer detection, analysis, and monitoring.jpeg by Varun Sendilraj (19732510)

“…</p>Methods<p>We developed DFUCare, a platform that leverages computer vision and deep learning (DL) algorithms to localize, classify, and analyze DFUs non-invasively. …”

Image4_DFUCare: deep learning platform for diabetic foot ulcer detection, analysis, and monitoring.jpeg by Varun Sendilraj (19732510)

“…</p>Methods<p>We developed DFUCare, a platform that leverages computer vision and deep learning (DL) algorithms to localize, classify, and analyze DFUs non-invasively. …”

Image1_DFUCare: deep learning platform for diabetic foot ulcer detection, analysis, and monitoring.jpeg by Varun Sendilraj (19732510)

“…</p>Methods<p>We developed DFUCare, a platform that leverages computer vision and deep learning (DL) algorithms to localize, classify, and analyze DFUs non-invasively. …”

Image2_DFUCare: deep learning platform for diabetic foot ulcer detection, analysis, and monitoring.jpeg by Varun Sendilraj (19732510)

“…</p>Methods<p>We developed DFUCare, a platform that leverages computer vision and deep learning (DL) algorithms to localize, classify, and analyze DFUs non-invasively. …”

Image5_DFUCare: deep learning platform for diabetic foot ulcer detection, analysis, and monitoring.jpeg by Varun Sendilraj (19732510)

“…</p>Methods<p>We developed DFUCare, a platform that leverages computer vision and deep learning (DL) algorithms to localize, classify, and analyze DFUs non-invasively. …”

Validation of image processing steps and metric results using images of a microfluidic device. by Gavrielle R. Untracht (11820311)