1 code implementation • 26 Feb 2024 • Sushmita Sarker, Prithul Sarker, George Bebis, Alireza Tavakkoli
Traditional deep learning approaches for breast cancer classification has predominantly concentrated on single-view analysis.
no code implementations • 20 Oct 2023 • Aminul Huq, Dimitris Zermas, George Bebis
Using deep learning techniques, we have developed a methodology which can detect different levels of abnormality (i. e., low, medium, high or no abnormality) in maize plants independently of their growth stage.
no code implementations • 16 Nov 2022 • Sharif Amit Kamran, Khondker Fariha Hossain, Alireza Tavakkoli, George Bebis, Sal Baker
We also incorporate a novel embedding loss that calculates similarities between linear feature embeddings of the encoder and decoder blocks.
no code implementations • 25 Oct 2022 • Prithul Sarker, Sushmita Sarker, George Bebis, Alireza Tavakkoli
Deep learning has made a breakthrough in medical image segmentation in recent years due to its ability to extract high-level features without the need for prior knowledge.
1 code implementation • 22 Jul 2022 • Adarsh Sehgal, Muskan Sehgal, Hung Manh La, George Bebis
Accurate breast cancer diagnosis through mammography has the potential to save millions of lives around the world.
1 code implementation • 23 Jul 2021 • Touqeer Ahmad, Ebrahim Emami, Martin Čadík, George Bebis
We present a novel mountainous skyline detection approach where we adapt a shallow learning approach to learn a set of filters to discriminate between edges belonging to sky-mountain boundary and others coming from different regions.
no code implementations • 21 May 2018 • Touqeer Ahmad, Pavel Campr, Martin Čadík, George Bebis
Each of the method is tested on an extensive test set (about 3K images) covering various challenging geographical, weather, illumination and seasonal conditions.