Simple Summary Deep learning has succeeded greatly in medical image-based cancer
diagnosis. To help readers better understand the current research status and ideas, this …

Deep learning has recently become very popular on account of its incredible success in
many complex datadriven applications, including image classification and speech …

Modern practice for training classification deepnets involves a terminal phase of training
(TPT), which begins at the epoch where training error first vanishes. During TPT, the training …

Automatic segmentation of abdominal anatomy on computed tomography (CT) images can
support diagnosis, treatment planning, and treatment delivery workflows. Segmentation …

In this work, we present a simple, highly efficient and modularized Dual Path Network (DPN)
for image classification which presents a new topology of connection paths internally. By …

An increasing need of running Convolutional Neural Network (CNN) models on mobile
devices with limited computing power and memory resource encourages studies on efficient …

Recent work has shown that convolutional networks can be substantially deeper, more
accurate, and efficient to train if they contain shorter connections between layers close to the …

Background and objective Convolutional neural networks (CNNs) play an important role in
the field of medical image segmentation. Among many kinds of CNNs, the U-net architecture …

Weakly supervised learning has emerged as a compelling tool for object detection by
reducing the need for strong supervision during training. However, major challenges …

In recent years, larger and deeper models are springing up and continuously pushing state-
of-the-art (SOTA) results across various fields like natural language processing (NLP) and …