Chao Li

Bio: Chao Li is an academic researcher from Huazhong University of Science and Technology. The author has contributed to research in topics: Object detection & AdaBoost. The author has an hindex of 3, co-authored 5 publications receiving 194 citations. Previous affiliations of Chao Li include Temple University.

Object detection model training method and target object detection method

Abstract: The invention relates to a target object detection method and device, a computer readable storage medium and computer equipment. The method comprises the steps that acquiring a to-be-detected image; inputting the to-be-detected image into the target object detection model, wherein the target object detection model is obtained by carrying out parameter adjustment on an initial object detection model through training a loss value; wherein the training loss value is obtained through calculation according to target pixel points determined by a first area and a second area, and the first area and the second area are obtained through determination according to the position of the sample mass center of a target type object in the training sample image; generating a prediction graph correspondingto the to-be-detected image by the target object detection model, wherein the prediction graph describes the relation degree of each pixel point of the to-be-detected image belonging to the target detection object; and carrying out region segmentation on the prediction image to obtain a target detection object region. In addition, the invention further provides an object detection model training method and device, a computer readable storage medium and computer equipment.

Image Retrieval with Similar Object Detection and Local Similarity to Detected Objects

Abstract: Commercial image search applications like eBay and Pinterest allow users to select the focused area as bounding box over the query images, which improves the retrieval accuracy. The focused area image retrieval strategy motivated our research, but our system has three main advantages over the existing works. (1) Given a query focus area, our approach localizes the most similar region in the database image and only this region is used for computing image similarity. This is done in a unified network whose weights are adjusted both for localization and similarity learning in an end-to-end manner. (2) This is achieved using fewer than five proposals extracted from a saliency map, which speedups the pairwise similarity computation. Usually hundreds or even thousands of proposals are used for localization. (3) For users, our system explains the relevance of the retrieved results by locating the regions in the database images that are the most similar to the query object. Our method achieves significantly better retrieval performance than the off-the-shelf object localization-based retrieval methods and end-to-end trained triplet method with a region proposal network. Our experimental results demonstrate 86% retrieval rate as compared to 73% achieved by the existing methods on PASCAL VOC07 and VOC12 datasets. Extensive experiments are also conducted on the instance retrieval databases Oxford5k and INSTRE, where we exhibit competitive performance. Finally, we provide both quantitative and qualitative results of our retrieval method demonstrating its superiority over commercial image search systems.

Digital pathology and artificial intelligence

Abstract: In modern clinical practice, digital pathology has a crucial role and is increasingly a technological requirement in the scientific laboratory environment. The advent of whole-slide imaging, availability of faster networks, and cheaper storage solutions has made it easier for pathologists to manage digital slide images and share them for clinical use. In parallel, unprecedented advances in machine learning have enabled the synergy of artificial intelligence and digital pathology, which offers image-based diagnosis possibilities that were once limited only to radiology and cardiology. Integration of digital slides into the pathology workflow, advanced algorithms, and computer-aided diagnostic techniques extend the frontiers of the pathologist's view beyond a microscopic slide and enable true utilisation and integration of knowledge that is beyond human limits and boundaries, and we believe there is clear potential for artificial intelligence breakthroughs in the pathology setting. In this Review, we discuss advancements in digital slide-based image diagnosis for cancer along with some challenges and opportunities for artificial intelligence in digital pathology.

Computational pathology definitions, best practices, and recommendations for regulatory guidance: a white paper from the Digital Pathology Association.

Abstract: In this white paper, experts from the Digital Pathology Association (DPA) define terminology and concepts in the emerging field of computational pathology, with a focus on its application to histology images analyzed together with their associated patient data to extract information. This review offers a historical perspective and describes the potential clinical benefits from research and applications in this field, as well as significant obstacles to adoption. Best practices for implementing computational pathology workflows are presented. These include infrastructure considerations, acquisition of training data, quality assessments, as well as regulatory, ethical, and cyber-security concerns. Recommendations are provided for regulators, vendors, and computational pathology practitioners in order to facilitate progress in the field. © 2019 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.