Digital Pathology Projects for M.E, M.Tech, Masters, MS abroad, and PhD students. These Digital Pathology ieee projects are implemented with future work and extension for final year project submission with research paper publishing. These research projects guide final year students to learn, practice, and complete their academic submissions successfully. Each project includes complete source code, project report, PPT, a tutorial, documentation, and a research paper.
Latest Digital Pathology Projects
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Lymphocyte-Infiltrated Periportal Region Detection With StructurallyRefined Deep Portal Segmentation and Heterogeneous Infiltration Features
This project focuses on helping doctors diagnose hepatitis more accurately. It uses deep learning to automatically find regions in the liver affected by immune cells called lymphocytes. These regions are hard to see because their boundaries are irregular. The system can detect them reliably and provide information that matches liver disease severity and liver function tests. -
The Latent Doctor Model for Modeling Inter-Observer Variability
This project focuses on improving medical image analysis. It teaches a computer model to understand not just the most common expert opinion, but also the differences between experts. The model can predict both the likely correct label and how uncertain experts might be. It works better than traditional methods in grading prostate tumors and other tasks. -
Center-Focused Affinity Loss for Class Imbalance Histology Image Classification
This project focuses on helping doctors detect cancer early by analyzing medical images of tissues. The researchers created a new computer method that can better identify cancer cells, even when the data is uneven or hard to read. Their approach improves accuracy compared to existing techniques and works well on breast and colon cancer images. It aims to make cancer diagnosis faster and more reliable. -
LYSTO The Lymphocyte Assessment Hackathon and Benchmark Dataset
This project, called LYSTO, organized a fast-paced competition to count immune cells called lymphocytes in cancer tissue images. Participants had only a few hours to develop methods to analyze colon, breast, and prostate cancer samples. The results showed some methods matched expert pathologists in accuracy. The dataset and evaluation tools are now available online for further research. -
PseudoCell Hard Negative Mining as Pseudo Labeling for Deep LearningBased Centroblast Cell Detection
This project introduces PseudoCell, a system that automatically detects important cells called centroblasts in large tissue images. It reduces the need for pathologists to manually mark every cell. The system uses a mix of real labels and computer-generated hints to find relevant areas. This makes diagnosis faster and less labor-intensive for doctors. -
Multimodal Non-Small Cell Lung Cancer Classification Using Convolutional Neural Networks
This project focuses on detecting and classifying lung cancer at an early stage. It uses multiple types of biological data together, rather than just one. Advanced deep learning models are applied to these data to improve accuracy. The results show high success in identifying different lung cancer subtypes, which can help doctors choose better treatments.
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