Deep Convolutional Hashing for Low-Dimensional Binary Embedding of Histopathological Images

Abstract

Compact binary representations of histopathology images using hashing methods provide efficient approximate nearest neighbor search for direct visual query in large-scale databases. They can be utilized to measure the probability of the abnormality of the query image based on the retrieved similar cases, thereby providing support for medical diagnosis. They also allow for efficient managing of large-scale image databases because of a low storage requirement. However, the effectiveness of binary representations heavily relies on the visual descriptors that represent the semantic information in the histopathological images. Traditional approaches with hand-crafted visual descriptors might fail due to significant variations in image appearance. Recently, deep learning architectures provide promising solutions to address this problem using effective semantic representations. In this paper, we propose a Deep Convolutional Hashing (DCH) method that can be trained "point-wise" to simultaneously learn both semantic and binary representations of histopathological images. Specifically, we propose a convolutional neural network (CNN) that introduces a latent binary encoding (LBE) layer for low dimensional feature embedding to learn binary codes. We design a joint optimization objective function that encourages the network to learn discriminative representations from the label information, and reduce the gap between the real-valued low dimensional embedded features and desired binary values. The binary encoding for new images can be obtained by forward propagating through the network and quantizing the output of the LBE layer. Experimental results on a large-scale histopathological image dataset demonstrate the effectiveness of the proposed method.

Pengenalan Ekspresi Wajah dengan Metode Viola Jones dan Convolutional Neural Network

“…Backpropagation adalah salah satu algoritma supervised learning yang digunakan dalam artificial neural networks. Backpropagation mencari kombinasi bobot untuk meminimalkan kesalahan output untuk dianggap menjadi solusi yang benar [15]. Adapun tahapan Backpropagation adalah sebagai berikut: a. Turunan gradien error terhadap softmax Tahapan turunan gradien error terhadap softmax berdasarkan rumus yang ditulis oleh Peter Sadowski [15].…”

Toward more efficient locality‐sensitive hashing via constructing novel hash function cluster

“…An instance of the approach uses an efficient graph partitioner KaHIP to partition the dataset. Others including the DCH method 30 26 which reduces the number of hash tables and alleviates the pressure from the complexity of the algorithm space to a certain extent. Zheng et al propose a LSH framework PM-LSH, 33 which adopts PM-tree to index the data points, and uses a tunable confidence interval to achieve accurate distance estimation and guarantee high result quality.…”

Toward More Efficient WMSN Data Search Combined FJLT Dimension Expansion With PCA Dimension Reduction

“…Here, the method of principal component hashing is introduced into the retrieval to enhance the robustness of the algorithm to different data distributions. In the past few years, some new research works have emerged in this field, including the Deep Convolutional Hashing (DCH) method [30] that was proposed by Sapkota, the HashNet method [31] and the Deep Visual-Semantic Quantization (DVSQ) method [32] that was proposed by Cao. Although the index structure based on a deep neural network has certain advantages with respect to the retrieval accuracy, its training time for large-scale data sets is longer, and the training quality has higher sensitivity to network parameters; therefore, there are still obstacles to its practical application.…”