YOLOv5s-MC: Lightweight road target detection network

For the problem of large number of target detection algorithm parameters, a lightweight real-time detection algorithm YOLOv5s-MC based on improved YOLOv5s road scenes is proposed. firstly, CA attention is added to the model to improve the sensitivity of the network to detect targets; secondly, in the feature fusion network, add adaptive weight parameters using AS-Concat structure are added to better fuse the feature information of different layers and improve the detection accuracy of the algorithm ; adding a small target detection layer to improve the detection accuracy of tiny targets; finally introducing Mobilnetv2, a lightweight network, as the overall backbone layer to realize the lightweight requirement of the network; to verify the advantages of the proposed algorithm, experiments were conducted on the kitti dataset. The experimental results show that the proposed algorithm, compared with the original network, improves the average accuracy by 0.2% with 55.8% less parameters and 33.7% less computation, and the detection speed reaches 35 FPS, which meets the requirements of real-time detection and improves the ability of algorithm deployment in weak hardware computing power scenarios to a certain extent

Cross-Modality Image Registration using a Training-Time Privileged Third Modality

— In this work, we consider the task of pairwise cross-modality image registration, which may benefit from exploiting additional images available only at training time from an additional modality that is different to those being registered. As an example, we focus on aligning intra-subject multiparametric Magnetic Resonance (mpMR) images, between T2-weighted (T2w) scans and diffusionweighted scans with high b-value (DWI_{high−b}). For the application of localising tumours in mpMR images, diffusion scans with zero b-value (DWI_{b=0}) are considered easier to register to T2w due to the availability of corresponding features. We propose a learning from privileged modality algorithm, using a training-only imaging modality DWIb=0, to support the challenging multi-modality registration problems. We present experimental results based on 369 sets of 3D multiparametric MRI images from 356 prostate cancer patients and report, with statistical significance, a lowered median target registration error of 4.34 mm, when registering the holdout DWI_{high−b} and T2w image pairs, compared with that of 7.96 mm before registration. Results also show that the proposed learning-based registration networks enabled efficient registration with comparable or better accuracy, compared with a classical iterative algorithm and other tested learning-based methods with/without the additional modality. These compared algorithms also failed to produce any significantly improved alignment between DWI_{high−b} and T2w in this challenging application

FEW-SHOT image segmentation for cross-institution male pelvic organs using registration-assisted prototypical learning

The ability to adapt medical image segmentation networks for a novel class such as an unseen anatomical or pathological structure, when only a few labelled examples of this class are available from local healthcare providers, is sought-after. This potentially addresses two widely recognised limitations in deploying modern deep learning models to clinical practice, expertise-and-labour-intensive labelling and cross-institution generalisation. This work presents the first 3D few-shot interclass segmentation network for medical images, using a labelled multi-institution dataset from prostate cancer patients with eight regions of interest. We propose an image alignment module registering the predicted segmentation of both query and support data, in a standard prototypical learning algorithm, to a reference atlas space. The built-in registration mechanism can effectively utilise the prior knowledge of consistent anatomy between subjects, regardless whether they are from the same institution or not. Experimental results demonstrated that the proposed registration-assisted prototypical learning significantly improved segmentation accuracy (p-values<0.01) on query data from a holdout institution, with varying availability of support data from multiple institutions. We also report the additional benefits of the proposed 3D networks with 75% fewer parameters and an arguably simpler implementation, compared with existing 2D few-shot approaches that segment 2D slices of volumetric medical images

Few-shot image segmentation for cross-institution male pelvic organs using registration-assisted prototypical learning

The ability to adapt medical image segmentation networks for a novel class such as an unseen anatomical or pathological structure, when only a few labelled examples of this class are available from local healthcare providers, is sought-after. This potentially addresses two widely recognised limitations in deploying modern deep learning models to clinical practice, expertise-and-labour-intensive labelling and cross-institution generalisation. This work presents the first 3D few-shot interclass segmentation network for medical images, using a labelled multi-institution dataset from prostate cancer patients with eight regions of interest. We propose an image alignment module registering the predicted segmentation of both query and support data, in a standard prototypical learning algorithm, to a reference atlas space. The built-in registration mechanism can effectively utilise the prior knowledge of consistent anatomy between subjects, regardless whether they are from the same institution or not. Experimental results demonstrated that the proposed registration-assisted prototypical learning significantly improved segmentation accuracy (p-values<0.01) on query data from a holdout institution, with varying availability of support data from multiple institutions. We also report the additional benefits of the proposed 3D networks with 75% fewer parameters and an arguably simpler implementation, compared with existing 2D few-shot approaches that segment 2D slices of volumetric medical images

The north–south shift of the ridge location of the western Pacific subtropical high and its influence on the July precipitation in the Jianghuai region from 1978 to 2021

The Jianghuai region is the area between the Yangtze River and the Huai River in China and is a densely populated agriculture region therefore, the economics and human activity there are significantly affected by the precipitation changes, particularly during the summer when extreme storms and droughts normally occur. It will be helpful if the summer precipitation changes can be predicted. The monthly ERA5 atmospheric reanalysis data from 1978 to 2021 are used in this study to investigate the relationship between the ridge latitudinal location of the western Pacific subtropical high (WPSH) and the precipitation in July over the Jianghuai region. The results show that the WPSH ridge location has an important impact on the amount and spatial distribution of the precipitation in this region. When the ridge was northward, an anomalous anticyclonic circulation will appear over the western Pacific, leading to the weakening of the summer monsoon and the reduction of moisture transport from the Indian Ocean, therefore decreasing precipitation in the Jianghuai region, while the situation is opposite when the ridge was southward. The Niño 3.4 index in March and the India–Burma trough intensity index in June have significant correlations with the July WPSH ridge location, and both can be used as precursors to predict the WPSH ridge location and, therefore, the precipitation in this region

Long-range prediction of the tropical cyclone frequency landfalling in China using thermocline temperature anomalies at different longitudes

The landfalls of the tropical cyclone (TC) along the coast of China have caused huge economic damages. There are approximately nine TC landfalls in China every year. It will be beneficial if the landfall frequency can be predicted in advance. Inspired by the study of Sparks and Toumi (Commun Earth Environ, 30-1-2020), six datasets, including four ocean reanalyses and two object analyses from 1993 to 2019, are employed to study the consistency in the relationship between the thermocline temperature anomalies at different longitudes and the frequency of TC landfalls along the coastal areas of China (South China, East China, and the whole of China). The thermocline temperature anomalies at different longitudes are tested in order to confirm our hypothesis that the eastward and westward transports of ocean heat from the warm pool are the causes of the significant correlations. The results show some significant correlations at various longitudes, and the temperature anomalies can predict the TC landfall frequency for several months or longer. Further study also shows the close relationship between the ocean heat transport and the sea surface temperature anomalies at the genesis locations of TC landfalls. The locations of the western Pacific subtropical high (WPSH) during high-frequency TC landfall years also show favorable spatial patterns to the TC landfall in South China and East China, respectively. In years with a high TC frequency in South China, the westward displacement of the WPSH ridge steers TC toward South China, while during high-frequency TC landfall years in East China, WPSH is located further north, and the westward extension of the ridge is in close proximity to the East China Sea

Boundary-RL: Reinforcement Learning for Weakly-Supervised Prostate Segmentation in TRUS Images

We propose Boundary-RL, a novel weakly supervised segmentation method that utilises only patch-level labels for training. We envision the segmentation as a boundary detection problem, rather than a pixel-level classification as in previous works. This outlook on segmentation may allow for boundary delineation under challenging scenarios such as where noise artefacts may be present within the region-of-interest (ROI) boundaries, where traditional pixel-level classification-based weakly supervised methods may not be able to effectively segment the ROI. Particularly of interest, ultrasound images, where intensity values represent acoustic impedance differences between boundaries, may also benefit from the boundary delineation approach. Our method uses reinforcement learning to train a controller function to localise boundaries of ROIs using a reward derived from a pre-trained boundary-presence classifier. The classifier indicates when an object boundary is encountered within a patch, as the controller modifies the patch location in a sequential Markov decision process. The classifier itself is trained using only binary patch-level labels of object presence, which are the only labels used during training of the entire boundary delineation framework, and serves as a weak signal to inform the boundary delineation. The use of a controller function ensures that a sliding window over the entire image is not necessary. It also prevents possible false-positive or -negative cases by minimising number of patches passed to the boundary-presence classifier. We evaluate our proposed approach for a clinically relevant task of prostate gland segmentation on trans-rectal ultrasound images. We show improved performance compared to other tested weakly supervised methods, using the same labels e.g., multiple instance learning.Comment: Accepted to MICCAI Workshop MLMI 2023 (14th International Conference on Machine Learning in Medical Imaging

Combiner and HyperCombiner Networks: Rules to Combine Multimodality MR Images for Prostate Cancer Localisation

One of the distinct characteristics in radiologists' reading of multiparametric prostate MR scans, using reporting systems such as PI-RADS v2.1, is to score individual types of MR modalities, T2-weighted, diffusion-weighted, and dynamic contrast-enhanced, and then combine these image-modality-specific scores using standardised decision rules to predict the likelihood of clinically significant cancer. This work aims to demonstrate that it is feasible for low-dimensional parametric models to model such decision rules in the proposed Combiner networks, without compromising the accuracy of predicting radiologic labels: First, it is shown that either a linear mixture model or a nonlinear stacking model is sufficient to model PI-RADS decision rules for localising prostate cancer. Second, parameters of these (generalised) linear models are proposed as hyperparameters, to weigh multiple networks that independently represent individual image modalities in the Combiner network training, as opposed to end-to-end modality ensemble. A HyperCombiner network is developed to train a single image segmentation network that can be conditioned on these hyperparameters during inference, for much improved efficiency. Experimental results based on data from 850 patients, for the application of automating radiologist labelling multi-parametric MR, compare the proposed combiner networks with other commonly-adopted end-to-end networks. Using the added advantages of obtaining and interpreting the modality combining rules, in terms of the linear weights or odds-ratios on individual image modalities, three clinical applications are presented for prostate cancer segmentation, including modality availability assessment, importance quantification and rule discovery.Comment: 30 pages, 6 figure

Image quality assessment for machine learning tasks using meta-reinforcement learning

In this paper, we consider image quality assessment (IQA) as a measure of how images are amenable with respect to a given downstream task, or task amenability. When the task is performed using machine learning algorithms, such as a neural-network-based task predictor for image classification or segmentation, the performance of the task predictor provides an objective estimate of task amenability. In this work, we use an IQA controller to predict the task amenability which, itself being parameterised by neural networks, can be trained simultaneously with the task predictor. We further develop a meta-reinforcement learning framework to improve the adaptability for both IQA controllers and task predictors, such that they can be fine-tuned efficiently on new datasets or meta-tasks. We demonstrate the efficacy of the proposed task-specific, adaptable IQA approach, using two clinical applications for ultrasound-guided prostate intervention and pneumonia detection on X-ray images