DEEP LEARNING IN BIOMEDICAL IMAGING, KPR Institute Engineering and Technology, Autonomous Engineering Institution, Coimbatore, India

1. Image Classification

• Detecting diseases (e.g., pneumonia from chest X-rays, tumors from MRI).

• Tools: CNNs (Convolutional Neural Networks), ResNet, EfficientNet.

2. Image Segmentation

• Separating anatomical structures or pathological regions (e.g., tumor boundaries).

• Tools: U-Net, Mask R-CNN, DeepLab.

3. Image Registration

• Aligning images taken at different times or from different angles/modalities.

• Tools: VoxelMorph, spatial transformer networks.

4. Object Detection

• Identifying regions of interest (e.g., polyps, nodules).

• Tools: YOLO, Faster R-CNN.

5. Image Reconstruction

• Enhancing image quality, reducing noise, or reconstructing from raw data.

• Tools: Deep convolutional autoencoders, GANs.

6. Disease Prediction/Diagnosis

• End-to-end models for diagnosis support.

• Tools: Ensemble networks, hybrid DL and radiomics approaches.