NECSTFridayTalk
NECSTFridayTalk
Lorenzo Veronese, Luca Drole
DEIB - NECSTLab Meeting Room (Bld. 20)
On Line via Facebook
June 16th, 2023
12.30 pm
Contacts:
Marco Santambrogio
Research Line:
System architectures
Lorenzo Veronese, Luca Drole
DEIB - NECSTLab Meeting Room (Bld. 20)
On Line via Facebook
June 16th, 2023
12.30 pm
Contacts:
Marco Santambrogio
Research Line:
System architectures
Sommario
On June 16th, 2023 at 12.30 pm a new appointment of NECSTFridayTalk will take place in DEIB NECSTLab Meeting Room (Building 20).
The Talk will be held by Lorenzo Veronese (Master's degree student in Computer Science Engineering, Artificial Intelligence, Politecnico di Milano) and Luca Drole (Undergraduate Student in Biomedical Engineering, Politecnico di Milano) on the following subjects:
"Stain Transfer using CycleGANs for Histopathological Images" - Lorenzo Veronese
Histopathology refers to the observation of tissues to identify the manifestation of diseases, e.g., cancer. Tiny tissue samples are taken from the patient and studied through a microscope; the analysis of the different cells, particularly their nuclei and other structures, allows for disease detection. The biological specimens need some preparation, namely Hematoxylin and Eosin (H&E) staining is often used to highlight nuclei and cytoplasm. Although staining is fundamental, given that cells are transparent when imaged, it is still highly affected by casual errors: colors change when a small preparation step is slightly different and even when a different microscope is used. This factor leads to Computer Aided Detection (CAD) systems losing performance. Therefore, to solve this problem and allow for the integration of multiple low-dimensional datasets, we propose a CycleGAN-based architecture exploiting PatchGAN and U-Net backbones as discriminators and generators, respectively, demonstrating an improvement in mean Structural Similarity Index Measure (SSIM) over the one computed on the original datasets of around 1.8%.
"Towards a Lightweight 2D U-Net for Accurate Semantic Segmentation of Kidney Tumors in Abdominal CT Images" - Luca Drole
Accurate segmentation of the kidney anatomy is crucial in the diagnosis and treatment of various kidney diseases. However, 3D U-Net-based Neural Networks entail significant computational requirements, complex architectures, and a fully annotated volumetric dataset. To address these challenges, our study designs and implement a custom image preprocessing workflow that suppresses fat and uninformative structures and compares the performances of 2D U-Net-based Neural Networks for semantic segmentation of kidneys and tumors from abdominal CT images. We found the ResU-Net model to achieve an accuracy of 89.17% for kidney segmentation, outperforming other models, while the Vanilla U- Net during the renal tumor segmentation task, with up to 11.7% higher DSC scores. Moreover, all the investigated methods do not require 3D CNNs, thus reducing computational costs. This comparison could be potentially useful to make a step forward in identifying the most accurate and lightweight technology to aid physicians in diagnosing kidney diseases while improving patient outcomes.
The Talk will be held by Lorenzo Veronese (Master's degree student in Computer Science Engineering, Artificial Intelligence, Politecnico di Milano) and Luca Drole (Undergraduate Student in Biomedical Engineering, Politecnico di Milano) on the following subjects:
"Stain Transfer using CycleGANs for Histopathological Images" - Lorenzo Veronese
Histopathology refers to the observation of tissues to identify the manifestation of diseases, e.g., cancer. Tiny tissue samples are taken from the patient and studied through a microscope; the analysis of the different cells, particularly their nuclei and other structures, allows for disease detection. The biological specimens need some preparation, namely Hematoxylin and Eosin (H&E) staining is often used to highlight nuclei and cytoplasm. Although staining is fundamental, given that cells are transparent when imaged, it is still highly affected by casual errors: colors change when a small preparation step is slightly different and even when a different microscope is used. This factor leads to Computer Aided Detection (CAD) systems losing performance. Therefore, to solve this problem and allow for the integration of multiple low-dimensional datasets, we propose a CycleGAN-based architecture exploiting PatchGAN and U-Net backbones as discriminators and generators, respectively, demonstrating an improvement in mean Structural Similarity Index Measure (SSIM) over the one computed on the original datasets of around 1.8%.
"Towards a Lightweight 2D U-Net for Accurate Semantic Segmentation of Kidney Tumors in Abdominal CT Images" - Luca Drole
Accurate segmentation of the kidney anatomy is crucial in the diagnosis and treatment of various kidney diseases. However, 3D U-Net-based Neural Networks entail significant computational requirements, complex architectures, and a fully annotated volumetric dataset. To address these challenges, our study designs and implement a custom image preprocessing workflow that suppresses fat and uninformative structures and compares the performances of 2D U-Net-based Neural Networks for semantic segmentation of kidneys and tumors from abdominal CT images. We found the ResU-Net model to achieve an accuracy of 89.17% for kidney segmentation, outperforming other models, while the Vanilla U- Net during the renal tumor segmentation task, with up to 11.7% higher DSC scores. Moreover, all the investigated methods do not require 3D CNNs, thus reducing computational costs. This comparison could be potentially useful to make a step forward in identifying the most accurate and lightweight technology to aid physicians in diagnosing kidney diseases while improving patient outcomes.
The NECSTLab is a DEIB laboratory, with different research lines on advanced topics in computing systems: from architectural characteristics, to hardware-software codesign methodologies, to security and dependability issues of complex system architectures.
Every week, the “NECSTFridayTalk” invites researchers, professionals or entrepreneurs to share their work experiences and projects they are implementing in the “Computing Systems”.
Event will hold on line by Facebook.
