Zobrazeno 1 - 10
of 58
pro vyhledávání: '"Hussein, Sarfaraz"'
Early diagnosis of breast cancer (BC) significantly contributes to reducing the mortality rate worldwide. The detection of different factors and biomarkers such as Estrogen receptor (ER), Progesterone receptor (PR), Human epidermal growth factor rece
Externí odkaz:
http://arxiv.org/abs/2406.18212
Lung cancer is the leading cause of cancer related mortality by a significant margin. While new technologies, such as image segmentation, have been paramount to improved detection and earlier diagnoses, there are still significant challenges in treat
Externí odkaz:
http://arxiv.org/abs/2209.08423
Deep neural networks have shown promising results in disease detection and classification using medical image data. However, they still suffer from the challenges of handling real-world scenarios especially reliably detecting out-of-distribution (OoD
Externí odkaz:
http://arxiv.org/abs/2111.01505
Publikováno v:
Computers in Biology and Medicine Volume 133, June 2021, Pages 104392
Body-Mass-Index (BMI) conveys important information about one's life such as health and socio-economic conditions. Large-scale automatic estimation of BMIs can help predict several societal behaviors such as health, job opportunities, friendships, an
Externí odkaz:
http://arxiv.org/abs/2104.04733
Autor:
Echauz, Javier, Kenemer, Keith, Hussein, Sarfaraz, Dhaliwal, Jay, Shintre, Saurabh, Grzonkowski, Slawomir, Gardner, Andrew
Machine learning models are vulnerable to adversarial inputs that induce seemingly unjustifiable errors. As automated classifiers are increasingly used in industrial control systems and machinery, these adversarial errors could grow to be a serious p
Externí odkaz:
http://arxiv.org/abs/1911.08090
We introduce a new technique for narrow-band (NB) signal classification in sparsely populated wide-band (WB) spectrum using supervised learning approach. For WB spectrum acquisition, Nyquist rate sampling is required at the receiver's analog-to-digit
Externí odkaz:
http://arxiv.org/abs/1904.06322
Autor:
Irmakci, Ismail, Hussein, Sarfaraz, Savran, Aydogan, Kalyani, Rita R., Reiter, David, Chia, Chee W., Fishbein, Kenneth W., Spencer, Richard G., Ferrucci, Luigi, Bagci, Ulas
Magnetic resonance imaging (MRI) is the non-invasive modality of choice for body tissue composition analysis due to its excellent soft tissue contrast and lack of ionizing radiation. However, quantification of body composition requires an accurate se
Externí odkaz:
http://arxiv.org/abs/1810.06071
Risk stratification (characterization) of tumors from radiology images can be more accurate and faster with computer-aided diagnosis (CAD) tools. Tumor characterization through such tools can also enable non-invasive cancer staging, prognosis, and fo
Externí odkaz:
http://arxiv.org/abs/1801.03230
Publikováno v:
In Biomedical Signal Processing and Control September 2022 78
Discriminating lung nodules as malignant or benign is still an underlying challenge. To address this challenge, radiologists need computer aided diagnosis (CAD) systems which can assist in learning discriminative imaging features corresponding to mal
Externí odkaz:
http://arxiv.org/abs/1710.09762