Reviewerly/exHarmony huggingface dataset:
Mind the Gap: Scanner-Induced Domain Shifts Pose Challenges for Representation Learning in Histopathology

Computer-aided systems in histopathology are often challenged by various sources of domain shift that impact the performance of these algorithms considerably. We investigated the potential of using self-supervised pre-training to overcome scanner-induced domain shifts for the downstream task of tumor segmentation. For this, we present the Barlow Triplets to learn scanner-invariant representations from a multi-scanner dataset with local image correspondences. We show that self-supervised pre-training successfully aligned different scanner representations, which, interestingly only results in a limited benefit for our downstream task. We thereby provide insights into the influence of scanner characteristics for downstream applications and contribute to a better understanding of why established self-supervised methods have not yet shown the same success on histopathology data as they have for natural images.
Removing Scattered Light in Biomedical Images

Scattered light is a common issue in various biomedical images such as fluorescence and X-ray images, leading to a hazy view of the imaging objects and thus degrading the image quality. To tackle this issue, this paper proposes a novel mathematical model to remove such scattered light. Our model is guaranteed to produce an image with better image contrast than the input. We also present a Local Guided Total Variation filter to solve this model. The proposed filter has linear computation complexity with respect to the total number of pixels, resulting in a high computational performance. Extensive experiments have been conducted to confirm the effectiveness of the proposed model and efficiency of the presented filter. Our method can be applied to a wide range of images to remove their scattered light. The open-source code is publicly available at http://gyh8.github.io.
Text Detection And Recognition Algorithm For Arbitrary Shapes In Natural Scenes

Natural scenes are rich and varied in text presentation and complex in the background. These characteristics are likely to cause problems such as missing text and difficulties in coordinate regression during detection. At the same time, the gradient disappears due to the long text sequence in the recognition stage, thus causing poor correlation before and after the text. In order to address the issue, this research suggests an end-to-end scene text detection and identification model that emphasizes spatial image data and long-range text relevance. The model uses the ABCNet network as the underlying structure. In the detection stage, this paper uses ResNet to extract image features, feature fusion with iterative FPN, convolution using convolutional blocks with coordinate information, and finally, focus the coordinate information by coordinated attention mechanism. This paper uses a combination of Transformer and LSTM coding blocks in the recognition phase to encode the detection region. It uses Transformer to obtain textual long-sequence relations, LSTM to enhance short-range information, and a lightweight attention mechanism to decode. The experimental findings demonstrate that the suggested model outperforms more widely used approaches in terms of text localisation and recognition.
TransEM-Net: Transformer Based Efficient Multi-Magnification Network for Histopathology

Analyzing digital Whole Slide Images at multiple magnifications simulates the workflow of a pathologist and gives much-needed context from global tissue structures. Transformer models are especially adept at capturing long-range dependencies and naturally lend themselves well to multi-magnification tissue analysis. In this paper, we present TransEM-Net, an efficient transformer-based architecture that collates information from two encoders operating on concentric images with varying fields of view. We evaluate TransEM-Net on lung and prostate cancer tissue datasets to demonstrate that the model outperforms the baseline as well as contemporary architectures. At the same time, TransEM-Net also provides a better balance between performance and model efficiency.
Spatial Distribution-Based Pseudo Labeling for Pathological Image Segmentation

This paper proposes a semi-supervised pathological image segmentation method that can improve the segmentation using a large amount of unlabeled data. Typical pseudo labeling methods select pseudo labels from unlabeled data to be used for re-training based on the confidence of each patch. However, the initial estimation is not accurate, so the pseudo labels contain many inaccurate labels. This may affect the segmentation performance. The highlight of this study is that we avoid selecting incorrect pseudo labels by introducing a spatial distribution model in a whole slide image. This is based on the assumption that a tumor region forms a cluster in tissue. This improves pseudo label selection and segmentation performance. Experimental results demonstrate the effectiveness of our method, where our method achieved the best segmentation performance in comparison.
An Unequal Error Protection-based Coded Transmission for Federated Learning

Effective data transmission plays a crucial role in federated learning (FL), which enables collaborative model training without centralizing data. This paper proposes a new coded transmission to enhance the communication quality for FL. The proposed coded transmission incorporates weight quantization, multilevel coding, set partitioning, and multi-stage decoding which are optimized to improve the FL performance. Furthermore, the unequal error protection (UEP) strategy is adopted in the proposed coded transmission, which allows the code rates to be optimized according to the significance of the quantized data. Simulation results demonstrate that the proposed UEP-based coded transmission outperforms conventional bit-interleaved coded modulation (BICM) scheme in terms of NMSE performance for FL, which, in return, improves the FL performance.
Deep Adaptive Discriminate Siamese Network with Multi-Level Response for Visual Object Tracking

Visual object tracking has been intensively studied for its role in traffic surveillance, human action recognition, and autonomous driving. Siamese network-based methods have demonstrated a satisfactory trade-off between precision and efficiency for visual tracking. Nevertheless, the accuracy of Siamese trackers is limited when it comes to predicting the target’s location in scenarios involving background clutter, changes in illumination, variations in scale, deformation, fast motion, among others. We suggest a novel approach in our manuscript, which involves a deep adaptive discriminative Siamese network equipped with an advanced fusion scheme for multiple level responses. To enhance the feature discriminability of the Siamese network, we introduce a novel residual channel attention clipping unit. This unit seamlessly integrates residual connections and channel attention, leading to significant optimization and improved representation in the network. Then, we introduce a multi-response adaptive fusion structure that takes the advantages of the low-level, mediate-level, and high-level features, yielding a comprehensive score map that reveals multiple levels of semantics. Our experiments demonstrate that our tracker performs exceptionally well compared to current leading trackers on widely-used public tracking datasets such as OTB-2015 and GOT10k. The method attains an AUC score of 0.655 on OTB2015, while maintaining a processing speed of 63 FPS.
A Densely Connected Attention Spatiotemporal U-Net for Kidney and Renal Artery Segmentation in CT Images

U-Net is widely used for medical image segmentation and challenges accurate structure segmentation without spatial and contextual information. This paper proposes an improved U-shape architecture called densely connected attention spatial-temporal U-Net. Specifically, such an architecture is created in accordance with several modules of dense connections, convolutional long short-term memory, and attention gates, fusing the advantages of these modules to precisely extract both spatial-temporal structural intra-slice and inter-slice contextual information. We applied our proposed method to segment the kidneys and main (external) renal arteries in 35 cases of patient kidney CT volumes, with the experimental results showing that our proposed method certainly outperforms current 2D and 3D fully convolutional networks. The average dice similarity coefficients of the kidneys and main renal arteries were improved from (95.48%, 81.11%) to (96.25%, 82.16%), respectively. Particularly, the amount of parameters were reduced from 34.88M to 13.49M.
A New Heuristic Algorithm Based on Nearby Measures for Shortest Path Calculation

Path planning is one of the key performance indexes to realize intelligent and efficient robot work. For improving the mobile efficiency of the robot, the Nearby Measures & Simulated Annealing (N-SA) algorithm model is proposed in this paper to solve the TSP. By combining the nearby measures with the traditional simulated annealing (SA) algorithm, the convergence rate of the original SA algorithm can be accelerated. At the same time, the solution generated by SA can be optimized by using the nearby measures to solve the path crossing problem. In the experiment, TSPLIB data set is used for verification, which can make the mass of the solution converge more than 95% quickly. The computational model has the characteristics of fast speed, small amount of computation and strong universality, which can provide certain research value for the efficient movement of robots.
High Throughput Deep Model of 3D Nucleus Instance Segmentation by Stereo Stitching Contextual Gaps

Whole-brain three dimensional (3D) nucleus instance segmentation is vital to quantify regional variations in many neuroscience studies using tissue clearing and microscopy imaging technologies. Due to the large size of whole-brain microscopy images (μm vs. mm resolution in MRI leads voxel volume increasing in 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">9</sup> level), however, it is computationally challenging to train an end-to-end deep model that can recognize nuclei instances in the full 3D volume. Instead, it is common practice to first segment 2D instances in each slice and then assemble them into 3D instances. Moreover, the whole-brain segmentation often comprises a collection of nucleus instance segmentation results in pre-partitioned image stacks, each with a manageable size for applying deep models. Complex arrangements of nuclei in close proximity makes stitching together 2D nucleus segmentations across slices non-trivial, leading to inconsistent segmentation along inter-slice and cross-stack gaps, which undermines the nucleus instance segmentation accuracy of current state-of-the-art deep models. To address this challenge we present a flexible learning-based stitching component which can be either integrated into existing deep models or used as a post-processing step. The backbone is a contextual graph model which is trained to predict the one-to-one correspondence between 2D segmentations along the gap. We have evaluated the performance of our stitching model for 3D nucleus instance segmentation from light-sheet microscopy images of mouse brains. After integrating our stitching model into existing methods, a significant improvement in instance segmentation accuracy is achieved by alleviating the inconsistency issue across discontinuous slices and stacks.
Graph Proposal Neural Networks for Cell Detection Immunostained Hodgkin Lymphoma Histology Images

The popularity of graph convolution networks has increased significantly in recent years due to their generality on non-Euclidean data. One of the areas in this realm that needs investigation is object detection. Due to certain limitation in clinical procedures, such as noisy imaging and ambiguity in the data, pathologists have difficulties in early diagnosis of illness. In this instance, the automated clinical work of the medical examiners is made possible by the identification of various things in the cell image. Conventional convolutional neural networks (CNN) include various ways for processing Euclidean data for object detection tasks, but non-Euclidean data needs special consideration in this respect. In this research, we offer a region proposal technique for non-Euclidean data object recognition based on graph convolution. The extraction of a subgraph as a candidate for the potential object region, the categorization of object candidates into associated class labels, and the detection technique are our key areas of interest. For the immunostained images of lymphoma cancer patients’ blood cell dataset, we tested our technique against the euclidean domain Region Based Convolutional Neural Networks (R-CNN) method and discovered enhanced average precision and recall score by 12.8% and 34.72%.
ST-MKSC: The FF-FFPE Stain Transfer Based on Multiple Key Structure Constraint

The common computational pathology tissue slides mainly include Fresh Frozen (FF) slides and Formalin-Fixed Paraffin-Embedded (FFPE) slides. The FF slide has low quality but is easy to prepare, while the FFPE one is the opposite, which is widely used in pathological slide preservation and high-precision diagnosis. Our goal is to generate FFPE images based on FF patches in order to quickly obtain high-quality slides in a short time and to provide doctors with convenient and accurate diagnostic evidence. We propose ST-MKSC, an FF2FFPE image translation network, which contains a Multi-frequency Domain Hierarchical Constraint (MDHC) network based on key consistent information constraints to keep the content information from the FF domain being preserved in the FFPE domain and Released Constraint loss (RC loss) to weaken the existing constraints, so as to reduce the impact of source domain (FF) style information on the appearance of the target domain (FFPE). We conduct FF2FFPE translation experiments on the TCGA-KIRC Dataset and our method is the best among the existing methods. Our model can effectively eliminate or weaken the cavity, artifacts, and other unreasonable structures in FF slides, and generate high-quality FFPE images.
Automated Virtual Reduction of Displaced Distal Radius Fractures

A distal radius fracture is one of the most common fractures treated in emergency departments. Pre-operative 3D virtual reduction of distal radius fractures is key as it improves the recovery process and reduces complications. Unfortunately, most state-of-the-art virtual reduction methods are either interactive or semi-automatic, which are labour intensive and suffer from high inter-observer variability.In this work, we propose a fully automated algorithm for the reduction of displaced fractures that is robust to both fracture segmentation errors and large fragment displacements. A strategy is proposed to adaptively select landmarks for fragment replacement and iteratively moving the fragments towards a template extracted from the contralateral radius. Real datasets of distal radius fractures with fragment displacements were used to validate our method. Results show that, in terms of distance to the mirrored contralateral radius, the automated reductions closely resemble the manual reductions by the surgeon.
Leveraging Inter-Annotator Disagreement for Semi-Supervised Segmentation

The low signal-to-noise ratio typically found in biomedical images often leads experts to disagree about the underlying ground-truth segmentation. While existing approaches for multiple annotations try to resolve conflicting annotations, we instead focus on efficiently using pixels of disagreement to estimate areas of high uncertainty in the data and exploit this information for semi-supervised segmentation.Pseudo-labelling approaches, which utilise unlabelled data by trying to match their own predictions, need to distinguish reliable from unreliable predictions. We propose to identify unreliable pseudo-labels from the output of a separate network that is trained to predict the uncertainty in the data based on conflicting annotations from different annotators.Compared to other uncertainty estimation techniques like MC-Dropout or ensembling approaches, our approach has the two key advantages that its estimates stem directly from the data and that it is computationally more efficient. Using two public datasets, we show the effectiveness of our approach.
Normal Reconstruction from Specularity in the Endoscopic Setting

We show that for a plane imaged by an endoscope the specular isophotes are concentric circles on the scene plane, which appear as nested ellipses in the image. We show that these ellipses can be detected and used to estimate the plane’s normal direction, forming a normal reconstruction method, which we validate on simulated data. In practice, the anatomical surfaces visible in endoscopic images are locally planar. We use our method to show that the surface normal can thus be reconstructed for each of the numerous specularities typically visible on moist tissues. We show results on laparoscopic and colonoscopic images.
A Global-Local Features Exchange and Fusion Network for Multi-Organ Segmentation

Convolutional neural network(CNN) based methods for multi-organ segmentation have achieved impressive results. However, the global feature extraction capability of CNNs is limited due to their localisation problem. In this paper, we propose a more efficient CNN and Transformer hybrid network for abdominal multi-organ segmentation. A parallel encoder is formed by the CNN and the Transformer encoder, making full use of the local and global feature extraction capabilities of both. Based on this, feature exchange modules are inserted at each scale of the encoder to enhance the features flow and alleviate the variability between different encoder features. In addition, a feature fusion module and a feature consistency loss function are proposed to couple the output features of the two encoders to ensure the consistency of the decoder input features. Experiments based on the Synapse dataset show that our approach achieves superior results compared with both CNN-based and Transformer-based state-of-the-art methods.
SFB-net for Cardiac Segmentation: Bridging the Semantic Gap with Attention

In the past few years, deep learning algorithms have been widely used for cardiac image segmentation. However, most of these architectures rely on convolutions that hardly model long-range dependencies, limiting their ability to extract contextual information. In order to tackle this issue, this article introduces the Swin Filtering Block network (SFB-net) which takes advantage of both conventional and swin transformer layers. The former are used to introduce spatial attention at the bottom of the network, while the latter are applied to focus on high level semantically rich features between the encoder and decoder. An average Dice score of 92.4 was achieved on the ACDC dataset. To the best of our knowledge, this result outperforms any other work on this dataset. The average Dice score of 87.99 obtained on the M&M’s dataset demonstrates that the proposed method generalizes well to data from different vendors and centres.
A Multi-dimensional Sparsity Nested Winograd Convolutional Neural Networks Accelerator

Convolutional neural networks (CNNs) have succeeded tremendously in various fields, including speech recognition, image classification, video processing, and pattern recognition. However, the computational complexity and the number of parameters of CNNs are very high. This limits the deployment and application of CNNs on hardware. To address this issue, we combined sparsity pruning with a multi-dimensional nested Winograd algorithm to design a multi-dimensional sparsity nested Winograd CNN accelerator. Finally, we implemented this accelerator on the Xilinx ZCU102 FPGA platform. The DSP efficiency of this accelerator can reach up to 3.6 times that of similar works, and the DSP performance density can reach up to 3.0 times that of similar works.
Open-RIMC: Open-Source RRAM-based IMC Co-Processor with Reconfigurable Logic Mapping

In this study we present Open-RIMC, an open-source RRAM-based IMC (In-memory computing) co-processor that enables energy-efficient mapping of MAC (multiply and accumulate) operations while providing reconfigurable logic-mapping. The proposed RRAM-IMC IP is implemented using the open-source Skywater-130 nm PDK and a fully open-source EDA tool-chain. Our IMC block acts as a co-processor to the RISC-V based Caravel co-processor. We demonstrate BNN (Binary Neural Network) as a potential application along with analysis on impact of variability on inference performance of the IMC-macro. We further analyze trade-offs due to logic-mapping. The design is open-sourced and available online at https://gitlab.com/NVM_IITD_Research/RRAM_IMC_IITD. The proposed design can be used for realizing efficient edge AI (artificial intelligence) quantized neural networks (QNN).
Contrast Uncertainty Domain Alignment for Cross-Domain Pancreatic Image Segmentation

Multiple phase/modality images can provide more morphological and functional information about the pancreas for diagnosing pancreatic cancer. Cross-domain pancreatic image segmentation meets the demand for time-consuming manual annotation for multiple phase/modality images. However, the large domain discrepancy, individual difference and the large deformation make traditional methods lead to the instability of style transfer and shape deformation during domain transfer. To address the above issues, a novel domain adaptation network is proposed to improve the segmentation of the pancreas in the target phase/modality image. To ensure the stability of style transfer, features of the transformed images and target images are aligned by using an Attentional Feature Fusion Module (AFFM) based adversarial learning in feature space. To maintain the shape invariance, the uncertainty-constrained consistency loss is presented to constrain training of the proposed framework. The proposed framework is evaluated with two abdominal image datasets, and the experimental results show that it outperforms the state-of-the-art approaches.
Efficient 3D Transformer with cluster-based Domain-Adversarial Learning for 3D Medical Image Segmentation

Real-world application of volumetric medical image segmentation is still challenging due to the domain shift problem and the disability to process volumetric information efficiently by existing algorithms. To address these problems, we propose a 3D Swin Transformer with a pyramidal downsampling strategy to process volumetric information efficiently, dubbed as PDSwin. Specifically, the improved 3D Swin Transformer includes a spatial downsampling strategy that downsamples 2D slices pyramidally according to the spatial relationship, reducing the computation complexity while providing a wider downsampled receptive field. Furthermore, we propose a cluster-based domain-adversarial learning algorithm to attenuate the domain shift problem. The algorithm generates fine-grained cluster-based domains instead of employing center-based domains, ameliorating the domain-adversarial learning performance. We evaluated our model against other competitive models on brain stroke lesion segmentation and prostate segmentation tasks. Extensive experimental results indicated that our proposed model outperforms other models, demonstrating the efficacy of our proposed method.