A5051638665- Natural Language Processing Techniques
- Radiomics and Machine Learning in Medical Imaging
- Anomaly Detection Techniques and Applications
- Text Readability and Simplification
- AI in cancer detection
- Topic Modeling
- Face and Expression Recognition
- Gene expression and cancer classification
- Video Surveillance and Tracking Methods
- Imbalanced Data Classification Techniques
- Medical Imaging Techniques and Applications
- Advanced Neural Network Applications
- Handwritten Text Recognition Techniques
- HER2/EGFR in Cancer Research
- Cancer Genomics and Diagnostics
- Music and Audio Processing
- Advanced Radiotherapy Techniques
- Human Pose and Action Recognition
- Electricity Theft Detection Techniques
- Cell Image Analysis Techniques
- Monoclonal and Polyclonal Antibodies Research
Sharif University of Technology
2019-2024
Detecting various types of cells in and around the tumor matrix holds a special significance characterizing micro-environment for cancer prognostication research. Automating tasks detecting, segmenting, classifying nuclei can free up pathologists' time higher value reduce errors due to fatigue subjectivity. To encourage computer vision research community develop test algorithms these tasks, we prepared large diverse dataset nucleus boundary annotations class labels. The has over 46,000 from...
Breast cancer is the most common malignancy in women, being responsible for more than half a million deaths every year. As such, early and accurate diagnosis of paramount importance. Human expertise required to diagnose correctly classify breast define appropriate therapy, which depends on evaluation expression different biomarkers such as transmembrane protein receptor HER2. This requires several steps, including special techniques immunohistochemistry or situ hybridization assess HER2...
Text generation is an important Natural Language Processing task with various applications. Although several metrics have already been introduced to evaluate the text methods, each of them has its own shortcomings. The most widely used such as BLEU only consider quality generated sentences and neglecting their diversity. For example, repeatedly one high sentence would result in a score. On other hand, more recent metric diversity texts known Self-BLEU ignores texts. In this paper, we propose...
Adversarial approach has been widely used for data generation in the last few years. However, this not extensively utilized classifier training. In paper, we propose an adversarial framework training that can also handle imbalanced data. Indeed, a network is trained via to give weights samples of majority class such obtained classification problem becomes more challenging discriminator and thus boosts its capability. addition general problems, proposed method be problems as graph...
Background and Objectives: Breast cancer is the most common malignancy in women responsible for more than half a million deaths each year. Early accurate diagnosis therefore of utmost importance. Human expertise required to diagnose correctly classify breast determine appropriate therapy, which depends on evaluating expression various biomarkers such as transmembrane protein receptor HER2. This evaluation requires several steps, including specialized techniques immunohistochemistry or situ...
Text generation is an important Natural Language Processing task with various applications. Although several metrics have already been introduced to evaluate the text methods, each of them has its own shortcomings. The most widely used such as BLEU only consider quality generated sentences and neglect their diversity. For example, repeatedly one high sentence would result in a score. On other hand, more recent metric diversity texts known Self-BLEU ignores texts. In this paper, we propose...
Although GAN-based methods have received many achievements in the last few years, they not been entirelysuccessful generating discrete data. The most crucial challenge of these is difficulty passing gradientfrom discriminator to generator when outputs are discrete. Despite fact that several attemptshave made alleviate this problem, none existing improved performance oftext generation compared with maximum likelihood approach terms both quality and diversity. In thispaper, we proposed a new...