Automatic Segmentation of Hemorrhages in the Ultra-wide Field Retina: Multi-scale Attention Subtraction Networks and An Ultra-wide Field Retinal Hemorrhage Dataset.

Ultra-wide field (UWF) retinal imaging can improve the detection rate of retinal hemorrhage as compared with conventional fundus images. However, hemorrhages in UWF retinal images can also become smaller and more widely distributed, which can be time consuming and labor intensive. With the development of computer technology, automatic segmentation techniques can assist physicians in diagnosis. However, the lack of publicly available UWF retinal hemorrhage segmentation datasets has limited the development of automatic hemorrhage segmentation techniques in UWF retinal images. We present a large-scale high-quality UWF retinal hemorrhage segmentation dataset, named UWF-RHS Dataset, for public use. To the best of our knowledge, we are the first team to make the UWF retinal hemorrhage segmentation dataset publicly available. In addition, we propose a multi-scale attention subtraction network (MASNet) for UWF retinal hemorrhage segmentation. Specifically, highly focused lesion features are extracted by using the proposed multi-scale attention subtraction (MAS) module at the progress of the skip-connection. Several comparative experiments and ablation experiments were performed at the UWF-RHS Dataset, and all experiments state that our proposed method is effective in diagnosing retinal hemorrhages with state-of-the-art results. The proposed UWF-RHS dataset and MASNet will greatly facilitate the development of automated segmentation techniques for UWF retinal hemorrhages. Links to the UWF-RHS dataset and the MASNet model code are available from https://github.com/wurenkai/UWF-RHS-Dataset-and-MASNet.

A high-order focus interaction model and oral ulcer dataset for oral ulcer segmentation.

Computer-aided diagnosis has been slow to develop in the field of oral ulcers. One of the major reasons for this is the lack of publicly available datasets. However, oral ulcers have cancerous lesions and their mortality rate is high. The ability to recognize oral ulcers at an early stage in a timely and effective manner is a very critical issue. In recent years, although there exists a small group of researchers working on these, the datasets are private. Therefore to address this challenge, in this paper a multi-tasking oral ulcer dataset (Autooral) containing two major tasks of lesion segmentation and classification is proposed and made publicly available. To the best of our knowledge, we are the first team to make publicly available an oral ulcer dataset with multi-tasking. In addition, we propose a novel modeling framework, HF-UNet, for segmenting oral ulcer lesion regions. Specifically, the proposed high-order focus interaction module (HFblock) performs acquisition of global properties and focus for acquisition of local properties through high-order attention. The proposed lesion localization module (LL-M) employs a novel hybrid sobel filter, which improves the recognition of ulcer edges. Experimental results on the proposed Autooral dataset show that our proposed HF-UNet segmentation of oral ulcers achieves a DSC value of about 0.80 and the inference memory occupies only 2029 MB. The proposed method guarantees a low running load while maintaining a high-performance segmentation capability. The proposed Autooral dataset and code are available from  https://github.com/wurenkai/HF-UNet-and-Autooral-dataset .

MCF-SMSIS: Multi-tasking with complementary functions for stereo matching and surgical instrument segmentation.

Stereo matching and instrument segmentation of laparoscopic surgical scenarios are key tasks in robotic surgical automation. Many researchers have been studying the two tasks separately for stereo matching and instrument segmentation. However, the relationship between these two tasks is often neglected. In this paper, we propose a model framework for multi-tasking with complementary functions for stereo matching and surgical instrument segmentation (MCF-SMSIS). We aim to complement the features of instrument prediction segmentation to the parallax matching block of stereo matching. We also propose two new evaluation metrics (MINPD and MAXPD) for assessing how well the parallax range matches the migrated domain when the model used for the stereo matching task undergoes domain migration. We performed stereo matching experiments on the SCARED , SERV-CT dataset as well as instrumentation segmentation experiments on the AutoLaparo dataset. The results demonstrate the effectiveness of the proposed method. In particular, stereo matching supplemented with instrument features reduced EPE, >3px and RMSE Depth in the surgical instrument section by 9.5%, 12.7% and 6.51%, respectively. The instrumentation segmentation performance also achieves a DSC value of 0.9233. Moreover, MCF-SMSIS takes only 0.14 s to infer a set of images. The model code and model weights for each stage are available from https://github.com/wurenkai/MCF-SMSIS.

HSH-UNet: Hybrid selective high order interactive U-shaped model for automated skin lesion segmentation.

The use of computer-assisted clinical dermatologists to diagnose skin diseases is an important aid. And computer-assisted techniques mainly use deep neural networks. Recently, the proposal of higher-order spatial interaction operations in deep neural networks has attracted a lot of attention. It has the advantages of both convolution and transformers, and additionally has the advantages of efficient, extensible and translation-equivariant. However, the selection of the interaction order in higher-order interaction operations requires tedious manual selection of a suitable interaction order. In this paper, a hybrid selective higher-order interaction U-shaped model HSH-UNet is proposed to solve the problem that requires manual selection of the order. Specifically, we design a hybrid selective high-order interaction module HSHB embedded in the U-shaped model. The HSHB adaptively selects the appropriate order for the interaction operation channel-by-channel under the computationally obtained guiding features. The hybrid order interaction also solves the problem of fixed order of interaction at each level. We performed extensive experiments on three public skin lesion datasets and our own dataset to validate the effectiveness of our proposed method. The ablation experiments demonstrate the effectiveness of our hybrid selective higher order interaction module. The comparison with state-of-the-art methods also demonstrates the superiority of our proposed HSH-UNet performance. The code is available at https://github.com/wurenkai/HSH-UNet.