Marzieh Haghighi

Marzieh Haghighi, Mario C. Cruz, Erin Weisbart et al.

Various strategies for label-scarce object detection have been explored by the computer vision research community. These strategies mainly rely on assumptions that are specific to natural images and not directly applicable to the biological and biomedical vision domains. For example, most semi-supervised learning strategies rely on a small set of labeled data as a confident source of ground truth. In many biological vision applications, however, the ground truth is unknown and indirect information might be available in the form of noisy estimations or orthogonal evidence. In this work, we frame a crucial problem in spatial transcriptomics - decoding barcodes from In-Situ-Sequencing (ISS) images - as a semi-supervised object detection (SSOD) problem. Our proposed framework incorporates additional available sources of information into a semi-supervised learning framework in the form of privileged information. The privileged information is incorporated into the teacher's pseudo-labeling in a teacher-student self-training iteration. Although the available privileged information could be data domain specific, we have introduced a general strategy of pseudo-labeling enhanced by privileged information (PLePI) and exemplified the concept using ISS images, as well on the COCO benchmark using extra evidence provided by CLIP.

Marzieh Haghighi, Simon K. Warfield, Sila Kurugol

Kidney function evaluation using dynamic contrast-enhanced MRI (DCE-MRI) images could help in diagnosis and treatment of kidney diseases of children. Automatic segmentation of renal parenchyma is an important step in this process. In this paper, we propose a time and memory efficient fully automated segmentation method which achieves high segmentation accuracy with running time in the order of seconds in both normal kidneys and kidneys with hydronephrosis. The proposed method is based on a cascaded application of two 3D convolutional neural networks that employs spatial and temporal information at the same time in order to learn the tasks of localization and segmentation of kidneys, respectively. Segmentation performance is evaluated on both normal and abnormal kidneys with varying levels of hydronephrosis. We achieved a mean dice coefficient of 91.4 and 83.6 for normal and abnormal kidneys of pediatric patients, respectively.

Seyed Sadegh Mohseni Salehi, Mohammad Moghadamfalahi, Hooman Nezamfar et al.

Noninvasive brain computer interfaces (BCI), and more specifically Electroencephalography (EEG) based systems for intent detection need to compensate for the low signal to noise ratio of EEG signals. In many applications, the temporal dependency information from consecutive decisions and contextual data can be used to provide a prior probability for the upcoming decision. In this study we proposed two probabilistic graphical models (PGMs), using context information and previously observed EEG evidences to estimate a probability distribution over the decision space in graph based decision-making mechanism. In this approach, user moves a pointer to the desired vertex in the graph in which each vertex represents an action. To select a vertex, a Select command, or a proposed probabilistic Selection criterion (PSC) can be used to automatically detect the user intended vertex. Performance of different PGMs and Selection criteria combinations are compared over a keyboard based on a graph layout. Based on the simulation results, probabilistic Selection criterion along with the probabilistic graphical model provides the highest performance boost for individuals with pour calibration performance and achieving the same performance for individuals with high calibration performance.