Medical image analysis is critical yet challenged by the need of jointly segmenting organs or tissues, and numerous instances for anatomical structures and tumor microenvironment analysis. Existing studies typically formulated different segmentation tasks in isolation, which overlooks the fundamental interdependencies between these tasks, leading to suboptimal segmentation performance and insufficient medical image understanding. To address this issue, we propose a Co-Seg++ framework for versatile medical segmentation. Specifically, we introduce a novel co-segmentation paradigm, allowing semantic and instance segmentation tasks to mutually enhance each other. We first devise a spatio-sequential prompt encoder (SSP-Encoder) to capture long-range spatial and sequential relationships between segmentation regions and image embeddings as prior spatial constraints. Moreover, we devise a multi-task collaborative decoder (MTC-Decoder) that leverages cross-guidance to strengthen the contextual consistency of both tasks, jointly computing semantic and instance segmentation masks. Extensive experiments on diverse CT and histopathology datasets demonstrate that the proposed Co-Seg++ outperforms state-of-the-arts in the semantic, instance, and panoptic segmentation of dental anatomical structures, histopathology tissues, and nuclei instances. The source code is available at https://github.com/xq141839/Co-Seg-Plus.

Deep matching models aim to facilitate search engines retrieving more relevant documents by mapping queries and documents into semantic vectors in the first-stage retrieval. When leveraging BERT as the deep matching model, the attention score across two words are solely built upon local contextualized word embeddings. It lacks prior global knowledge to distinguish the importance of different words, which has been proved to play a critical role in information retrieval tasks. In addition to this, BERT only performs attention across sub-words tokens which weakens whole word attention representation. We propose a novel Global Weighted Self-Attention (GLOW) network for web document search. GLOW fuses global corpus statistics into the deep matching model. By adding prior weights into attention generation from global information, like BM25, GLOW successfully learns weighted attention scores jointly with query matrix Q and key matrix K. We also present an efficient whole word weight sharing solution to bring prior whole word knowledge into sub-words level attention. It aids Transformer to learn whole word level attention. To make our models applicable to complicated web search scenarios, we introduce combined fields representation to accommodate documents with multiple fields even with variable number of instances. We demonstrate GLOW is more efficient to capture the topical and semantic representation both in queries and documents. Intrinsic evaluation and experiments conducted on public data sets reveal GLOW to be a general framework for document retrieve task. It significantly outperforms BERT and other competitive baselines by a large margin while retaining the same model complexity with BERT. The source code is available at https://github.com/GLOW-deep/GLOW.

We study the problem of deep recall model in industrial web search, which is, given a user query, retrieve hundreds of most relevant documents from billions of candidates. The common framework is to encoding queries and documents separately into distributed representations and match them in latent semantic space. However, all the exiting deep encoding models only leverage the information of the document itself, which is often not sufficient in practice when matching with query terms, especially for the hard tail queries. In this work we aim to leverage the additional information for documents from their co-click neighbours to help document retrieval. The challenges include how to effectively extract information and eliminate noise when involving co-click information while meet the demands of industrial scalability for real time online serving.

Recovering traceability links between source code and software document can be very helpful for Software Maintenance and Software Reuse. Existing work has already achieved good results in extracting code elements (classes, methods, etc.) from software documents. However, it will lead to a lot of noise links if we link a document to all the code elements existing in it. In this paper, we propose an approach to identify the contextual code elements and the salient code elements in a software document, then we can weight the traceability links between source code and software document so that those noise traceability links can be filtered effectively. We measure the saliency of each code element in a document with four kinds of document-related features and three kinds of code-related features, and we adopt TransR-based code embedding technology to evaluate the distance between code elements. In the experiments, we get a precision of 70.7% in recognizing salient code elements of StackOverflow answer documents, which is more than 12% improvement compared with Rigby's work. At the same time, we can filter about 56.5%~69.3% noise traceability links compared with the RecoDoc approach. It will improve the quality of traceability links between source code and related software documents.