Md. Zahangir Alom

TL;DR: A Recurrent Convolutional Neural Network (RCNN) based on U-Net as well as a Recurrent Residual convolutional neural Network (RRCNN), which are named RU-Net and R2U-Net respectively are proposed, which show superior performance on segmentation tasks compared to equivalent models including U-nets and residual U- net.

TL;DR: This report presents a brief survey on development of DL approaches, including Deep Neural Network (DNN), Convolutional neural network (CNN), Recurrent Neural network (RNN) including Long Short Term Memory (LSTM) and Gated Recurrent Units (GRU), Auto-Encoder (AE), Deep Belief Network (DBN), Generative Adversarial Network (GAN), and Deep Reinforcement Learning (DRL).

TL;DR: These DCNN techniques are applied for solving different DPIA problems and evaluated on different publicly available benchmark datasets for seven different tasks in digital pathology to demonstrate superior performance for classification, segmentation, and detection tasks compared to existing machine learning and DCNN based approaches.

TL;DR: This paper proposes a new DL architecture, the NABLA-N network, with better feature fusion techniques in decoding units for dermoscopic image segmentation tasks, and shows better quantitative and qualitative results with the same or fewer network parameters compared to other methods.

TL;DR: The experimental results show that the proposed DCNN models provide superior performance compared to the existing approaches for nuclei classification, segmentation, and detection tasks and will help for better understanding of different types of cancer in clinical workflow.