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Projects Proposed Projects

Please see propose projects for Spring and Winter upcoming semesters. This is a 4 academic point course. For further details please contact the laboratory engineer Yaron Honen (04-8295535, room 441).

Project Title:

Developing an Artificial Intelligence System to Detect Mild Cognitive Impairment and Alzheimer Disease Dementia through Self-Figure Drawing;

Picture of Developing an Artificial Intelligence System to Detect Mild Cognitive Impairment and Alzheimer  Disease Dementia through Self-Figure Drawing;
Abstract:

Alzheimer's disease dementia (AD) is a debilitating and prevalent neurodegenerative disease in older adults worldwide. Cognitive impairment, a hallmark of AD, is assessed through verbal tests that require high specialization, and while accepted as screening tools for AD, general practitioners seldom use them. Recent evidence indicates that early detection of mild cognitive impairment (MCI) can enable interventions that slow the rapid decline in functioning.
In this project, the students will utilize CNN-based methods to develop and test an application tailored to differentiate between drawings of individuals with MCI, AD, and healthy controls (HC).

Supervisors:
Requirements:
Deep learning experience/course, python (pytorch), computer vision/image processing.
Project Title:

Emotion recognition system for children

Picture of Emotion recognition system for children
Abstract:

The Geometric Image Processing Lab (GIP) at the Computer Science Department, in collaboration with the Educational Neuroimaging Center (ENIC) at the Technion, invites you to participate in a project focused on developing an emotion recognition system for children. In this project, the students will develop an emotion recognition system for children based on the paper: "EmoCLIP: A Vision-Language Method for Zero-Shot Video Facial Expression Recognition." The method in the paper is for adult Facial Expression Recognition (FER), and the students will refine and adjust it to children's faces. Some of the children's emotions, unlike those of adults, have similar representations and are, therefore, difficult to distinguish. Another challenge is that datasets of children’s faces are limited. This project continues the previous project at the lab (See "Children facial expressions detection with EEG from video" and the BMVC’17 paper “A Deep Learning Perspective on the Origin of Facial Expressions”). 

Supervisors:
Requirements:
Deep learning experience/course, python (pytorch), computer vision/image processing.
Project Title:

Personalized Gan Based Editing

Picture of Personalized Gan Based Editing
Abstract:

Developing an image editing pipeline that allows the user to edit personal photos using a user-friendly interface (dragging and text prompts). Photo Editing is a universally used tool, but few can master complex tools like Photoshop. We aim to develop an intuitive user-friendly method of editing. Where users can drag points around the picture and add text prompts to generate high-quality edited pictures. While this is possible using previously developed tools, we propose using a personal generative prior to constraining the images to the space of the person's images, thus providing a more desirable output.

Supervisors:
Requirements:
Python Programming skills , numerical algebra. Bonus : numerical optimization , deep learning , computer vision or image proccessing
Project Title:

Coronary angiography video segmentation method for assisting cardiovascular disease interventional treatment

Picture of Coronary angiography video segmentation method for assisting cardiovascular disease interventional treatment
Abstract:

Coronary heart disease is one of the diseases with the highest mortality rate. Due to the important position of cardiovascular disease prevention and diagnosis in the medical field, the segmentation of cardiovascular images has gradually become a research hotspot. How to segment accurate blood vessels from coronary angiography videos to assist doctors in making accurate analysis has become the goal of our research. In this project, the student will be required to implement a new method based on the U-net architecture. The proposed method includes three modules: the sequence encoder module, the sequence decoder module, and the sequence filter module. The high-level information of the feature is extracted in the encoder module. Multi-kernel pooling layers suitable for the extraction of blood vessels are added before the decoder module. In the filter block, we add a simple temporal filter to reduce inter-frame flickers.

Supervisors:
Requirements:
Deep learning Computer vision Design and train UNet or other networks with python (pytorch)
Project Title:

Agriculture-Vision Semantic Segmentation Challenge

Picture of Agriculture-Vision Semantic Segmentation Challenge
Abstract:

In recent years, unmanned aerial vehicles (UAV) are exploited to capture images of large agricultural areas. For agricultural purposes, near infrared (NIR) or other spectral channels are acquired in addition to the RGB colors. These are then used for various tasks, especially semantic segmentation – the ability to distinguish between healthy plants, unwanted weeds, running or standing water, and other classes. We will use an existing semantic segmentation framework, test several network architectures, and employ pre-processing and post-processing algorithms for improved results. Our goal is to participate in an international agriculture segmentation challenge, and attempt to achieve high scores. 

Supervisors:
Requirements:
Basic understanding of deep learning for computer vision.
Python programming + pytorch and/or tensorflow.
Project Title:

High Accuracy Leaf Segmentation

Picture of High Accuracy Leaf Segmentation
Abstract:

Instance segmentation is the task of detecting and masking objects in an image and distinguishing between instances of the same class. Accurate segmentation of leaves in plant images is important in many agricultural applications. These include early detection of water and heat stress, identification of biological infection, monitoring of plant growth, and prediction of harvest yields. Our purpose is to use an existing instance segmentation deep neural network, and integrate it with image processing tools for better performance. In this project we will improve upon previous results, participate in an international leaf segmentation challenge, and attempt to achieve best scores

Supervisors:
Requirements:
Basic deep learning understanding and programming experience.
Project Title:

Nice or Spice? Cannabis Detection by Microscope Inspection

Picture of Nice or Spice? Cannabis Detection by Microscope Inspection
Abstract:

Synthetic cannabinoids are harmful products manufactured and distributed to circumvent legal regulations. Real cannabis can be distinguished from synthetic cannabinoids by inspecting their leaf cystoliths. Cystoliths are hairy appearing outgrowths on leaves and flowers of some plant species. The unaided eye cannot distinguish the real from the fake, but looking through a microscope, it is possible to do so. In this project we will build an artificial intelligence application for classifying real vs. fake cannabis. 

Supervisors:
Requirements:
Basic deep learning understanding and programming experience
Project Title:

Computational oncology by deep learning-based analysis of histopathology slide images

Picture of Computational oncology by deep learning-based analysis of histopathology slide images
Abstract:

A few years ago, we showed for the first time that the molecular profile of cancer can be predicted by analysis of biopsy images, without using molecular assays. In other words, the shape of the tumor cells and the tissue architecture hold information that allows to accurately predict molecular expression, even though such molecules cannot be seen by humans by visual examination of biopsy images.  Based on these findings, in the past two years we established collaborations with several medical data hospitals in Israel and abroad and extended the scope of our research to different prediction tasks in breast cancer, lung cancer, and Leukemia. We collected and scanned high quality well annotated tens of thousands of histology slides, and extended our research team, consisting of data scientists, graduate and postdoctoral students and clinical collaborators and advisors. We have recently shown how to steer our technology into assisting personalized medicine procedures. Take a part in developing an AI-based framework for analysis of histology images for improving personalized oncology  -  Link to the article 

Supervisors:
Requirements:
Python must. Experience/courses in deep learning, image processing, and computer vision a big advantage
Project Title:

Cardiothoracic surgery

Picture of Cardiothoracic surgery
Abstract:

The main task is to adjust a 3D (pre-prepared) CT model on a patient's abdomen and chest, while finding the location and angle at which the model should be allocated, so that it fits exactly to the patient's body position. The project extensively uses voice commands, spatial mapping construction, and receiving and processing data from the sensors on the HoloLens glasses, as well as using an ICP algorithm for the purpose of fitting the model onto the patient. As the major problem the system still misses is the accurate registration of the images delivered by the augmented device (Microsoft Hololens) on the patient’s body. Here is where your knowledge will transform digital work into daily medicine. The student will be part of the development team here in Sheba, coming to the operating room and working with the patients and the surgeons.

Supervisors:
Requirements:
לפחות אחד מהקורסים לעיבוד תמונה או ראיה חישובית 
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