ADLMA-2022: Advances in Deep Learning for Medical Image Analysis: A Practical Approach |
Submission link | https://easychair.org/conferences/?conf=adlma2022 |
Abstract registration deadline | September 9, 2021 |
Submission deadline | November 19, 2021 |
Advances in Deep Learning for Medical Image Analysis: A Practical Approach
Submission Guidelines
All book chapters must be original and not simultaneously submitted to another journal/conference or book chapter. The following paper categories are welcome:
List of Topics
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Chapter 1: Deep Convolutional Neural Network (Approx 40 pages)
1.1. Introduction
1.2. Activation and Loss functions
1.3. Optimization algorithms in Deep Learning
1.4. Linear and logistic regression
1.5. Building of Artificial Neural Networks
1.6. Mathematical foundations for convolution neural network
1.7. Building of convolutional neural networks
1.8. Conclusion
1.9. References
Chapter 2: Nuclei Segmentation of Multi-organ Histopathology Images (Approx 25 pages)
2.1. Introduction
2.2. Related work
2.3. Benchmark models
2.3. 1. FCN8 Architecture
2.3.2. U-Net Architecture
2.3.3. SegNet Architecture
2.3.4. NucleiSegNet Architecture
2.3.5. Attention U-Net
2.3.6. DIST Architecture
2.4. Implementation and training details
2.5. Results and discussions
2.6. Conclusion
2.7. References
Chapter 3: Instance Nuclei Segmentation of Multi-organ Histopathology Images (Approx 25 pages)
3.1. Introduction
3.2. Related work
3.3. Benchmark models
3.3.1. FCN8 Architecture
3.3.2. U-Net Architecture
3.3.3. Attention U-Net Architecture
3.3.4. Micro-Net Architecture
3.3.5. DIST Architecture
3.3.6 HoVer-Net Architecture
3.4. Implementation and training details
3.5. Results and discussions
3.6. Conclusion
3.7. References
Chapter 4: Teeth Segmentation of Dental X-Ray Images (Approx 25 pages)
4.1. Introduction
4.2. Related work
4.3. Benchmark models
4.3.1. FCN8 Architecture
4.3.2. U-Net Architecture
4.3.3. Attention U-Net Architecture
4.3.4. DeeplabV3+ Architecture
4.3.5. SCPPNet Architecture
4.3.6 HMEDN Architecture
4.4. Implementation and training details
4.5. Results and discussions
4.6. Conclusion
4.7. References
Chapter 5: Segmentation of MRI Images (Approx 25 pages)
5.1. Introduction
5.2. Related work
5.3. Benchmark models
5.3.1. U-Net Architecture
5.3.2. Attention U-Net Architecture
5.3.3. DA-Net Architecture
5.3.4. SCPPNet Architecture
5.3.5. HMEDN Architecture
5.3.6 Multi-scale Dual Guided Architecture
5.4. Implementation and training details
5.5. Results and discussions
5.6. Conclusion
5.7. References
Chapter 6: Classification of Histopathology Images (Approx 35 pages)
6.1. Introduction
6.2. Related work
6.3. Benchmark models
6.3.1. VGG 16 Architecture
6.3.2. ResNet50 Architecture
6.3.3. DenseNet Architecture
6.3.4. InceptionNetV3 Architecture
6.3.5. MobileNetV2 Architecture
6.3.6. BHCNet Architecture
6.4. Implementation and training details
6.5. Results and discussions
6.6. Conclusion
6.7. References
Chapter 7: Automatic Detection of Corona Virus Disease 2019 (COVID-19) from Chest X-Ray Images (Approx 30 pages)
7.1. Introduction
7.2. Related work
7.3. Benchmark models
7.3.1. VGG 16 Architecture
7.3.2. DenseNet Architecture
7.3.3. COVID-Net Architecture
7.3.4. DRE-Net Architecture
7.3.5. Deep CNN ResNet50
7.3.6. DarkCovidNet Architecture
7.4. Implementation and training details
7.5. Results and discussions
7.6. Conclusion
7.7. References
Chapter 8: Advance topics in Deep Learning and projects (Approx 25 pages)
8.1. Introduction to Deep learning Libraries such as Keras, TensorFlow, PyTorch
8.2. Python Implementation of deep learning architectures for segmentation task
8.3. Python Implementation of Colon Cancer Grading from Histopathology Images
8.4. Python Implementation of Cancer Patient Treatment from GAFChromic Radiochromic film
8.5. Advance topics in deep learning
8.7. References
Publication
ADLMA-2022 will be published in by CRC Publishers, Taylor and Francis
Contact
All questions about submissions should be emailed to shyamfec@nitk.edu.in