A novel mutation type S420I in Myosin-5 confers resistance to Phenamacril in Fusarium fujikuroi.

Rice bakanae disease is a soil-borne disease mainly caused by Fusarium fujikuroi, which seriously damages the yield and quality of rice. Phenamacril targets Myosin-5, thereby inhibiting its ATPase activity to exert an antifungal effect, demonstrating significant bioactivity against Fusarium species. However, the resistance of Fusarium fujikuroi field populations to phenamacril in Jiangsu Province in recent years remains unclear.

A new point mutation (E198T) in β-tubulin confers resistance to carbendazim in Fusarium incarnatum.

Carbendazim is commonly used to control maize stalk rot caused by Fusarium species, and its resistance mainly arises from point mutations in the amino acids of β-tubulin. In this study, we identified two field isolates of Fusarium incarnatum resistant to carbendazim. One moderately resistant isolate, HA16R (100 μg/mL > MIC>50 μg/mL) carried a β-tubulin F167Y point mutation, while the other highly resistant isolate, HA18R (MIC >100 μg/mL) harbored a novel β-tubulin substitution at position 198-E198T.

Instrument-Tissue-Guided Surgical Action Triplet Detection via Textual-Temporal Trail Exploration.

Surgical action triplet detection offers intuitive intraoperative scene analysis for dynamically perceiving laparoscopic surgical workflows and analyzing the interaction between instruments and tissues. The current challenge of this task lies in simultaneously localizing surgical instruments while performing more accurate surgical triplet recognition to enhance a comprehensive understanding of intraoperative surgical scenes. To fully leverage the spatial localization of surgical instruments for associating with triplet detection, we propose an Instrument-Tissue-Guided Triplet detector, termed ITG-Trip, which navigates the confluence of surgical action cues through instrument and tissue pseudo-localization labeling to optimize action triplet detection.

Pro-NeXt: An All-in-One Unified Model for General Fine-Grained Visual Recognition.

Unlike general visual classification (CLS) tasks, certain CLS problems are significantly more challenging as they involve recognizing professionally categorized or highly specialized images. Fine-Grained Visual Classification (FGVC) has emerged as a broad solution to address this complexity. However, most existing methods have been predominantly evaluated on a limited set of homogeneous benchmarks, such as bird species or vehicle brands.

LMT++: Adaptively Collaborating LLMs with Multi-specialized Teachers for Continual VQA in Robotic Surgical Videos.

Visual question answering (VQA) plays a vital role in advancing surgical education. However, due to the privacy concern of patient data, training VQA model with previously used data becomes restricted, making it necessary to use the exemplar-free continual learning (CL) approach. Previous CL studies in the surgical field neglected two critical issues: i) significant domain shifts caused by the wide range of surgical procedures collected from various sources, and ii) the data imbalance problem caused by the unequal occurrence of medical instruments or surgical procedures.

Fitness and Resistance Mechanism of from Fields to Demethylation Inhibitor (DMI) Fungicides in China.

Tebuconazole and prothioconazole are triazole sterol demethylation inhibitor (DMI) fungicides widely used for controlling head blight (FHB) in the world. In 2023, 3714 isolates were collected from fields of 8 provinces in China, among which 116 (3.12%) isolates and 82 (2.

Medical SAM adapter: Adapting segment anything model for medical image segmentation.

The Segment Anything Model (SAM) has recently gained popularity in the field of image segmentation due to its impressive capabilities in various segmentation tasks and its prompt-based interface. However, recent studies and individual experiments have shown that SAM underperforms in medical image segmentation due to the lack of medical-specific knowledge. This raises the question of how to enhance SAM's segmentation capability for medical images.

Personalizing Federated Instrument Segmentation With Visual Trait Priors in Robotic Surgery.

Personalized federated learning (PFL) for surgical instrument segmentation (SIS) is a promising approach. It enables multiple clinical sites to collaboratively train a series of models in privacy, with each model tailored to the individual distribution of each site. Existing PFL methods rarely consider the personalization of multi-headed self-attention, and do not account for appearance diversity and instrument shape similarity, both inherent in surgical scenes.

DC²T: Disentanglement-Guided Consolidation and Consistency Training for Semi-Supervised Cross-Site Continual Segmentation.

Continual Learning (CL) is recognized to be a storage-efficient and privacy-protecting approach for learning from sequentially-arriving medical sites. However, most existing CL methods assume that each site is fully labeled, which is impractical due to budget and expertise constraint. This paper studies the Semi-Supervised Continual Learning (SSCL) that adopts partially-labeled sites arriving over time, with each site delivering only limited labeled data while the majority remains unlabeled.

Multi-rater Prism: Learning self-calibrated medical image segmentation from multiple raters.

In medical image segmentation, it is often necessary to collect opinions from multiple experts to make the final decision. This clinical routine helps to mitigate individual bias. However, when data is annotated by multiple experts, standard deep learning models are often not applicable.

S²Former-OR: Single-Stage Bi-Modal Transformer for Scene Graph Generation in OR.

Scene graph generation (SGG) of surgical procedures is crucial in enhancing holistically cognitive intelligence in the operating room (OR). However, previous works have primarily relied on multi-stage learning, where the generated semantic scene graphs depend on intermediate processes with pose estimation and object detection. This pipeline may potentially compromise the flexibility of learning multimodal representations, consequently constraining the overall effectiveness.

CalibNet: Dual-Branch Cross-Modal Calibration for RGB-D Salient Instance Segmentation.

In this study, we propose a novel approach for RGB-D salient instance segmentation using a dual-branch cross-modal feature calibration architecture called CalibNet. Our method simultaneously calibrates depth and RGB features in the kernel and mask branches to generate instance-aware kernels and mask features. CalibNet consists of three simple modules, a dynamic interactive kernel (DIK) and a weight-sharing fusion (WSF), which work together to generate effective instance-aware kernels and integrate cross-modal features.

Video-Instrument Synergistic Network for Referring Video Instrument Segmentation in Robotic Surgery.

Surgical instrument segmentation is fundamentally important for facilitating cognitive intelligence in robot-assisted surgery. Although existing methods have achieved accurate instrument segmentation results, they simultaneously generate segmentation masks of all instruments, which lack the capability to specify a target object and allow an interactive experience. This paper focuses on a novel and essential task in robotic surgery, i.

SimCol3D - 3D reconstruction during colonoscopy challenge.

Colorectal cancer is one of the most common cancers in the world. While colonoscopy is an effective screening technique, navigating an endoscope through the colon to detect polyps is challenging. A 3D map of the observed surfaces could enhance the identification of unscreened colon tissue and serve as a training platform.

Intelligent surgical workflow recognition for endoscopic submucosal dissection with real-time animal study.

Recent advancements in artificial intelligence have witnessed human-level performance; however, AI-enabled cognitive assistance for therapeutic procedures has not been fully explored nor pre-clinically validated. Here we propose AI-Endo, an intelligent surgical workflow recognition suit, for endoscopic submucosal dissection (ESD). Our AI-Endo is trained on high-quality ESD cases from an expert endoscopist, covering a decade time expansion and consisting of 201,026 labeled frames.

SurgT challenge: Benchmark of soft-tissue trackers for robotic surgery.

This paper introduces the "SurgT: Surgical Tracking" challenge which was organized in conjunction with the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2022). There were two purposes for the creation of this challenge: (1) the establishment of the first standardized benchmark for the research community to assess soft-tissue trackers; and (2) to encourage the development of unsupervised deep learning methods, given the lack of annotated data in surgery. A dataset of 157 stereo endoscopic videos from 20 clinical cases, along with stereo camera calibration parameters, have been provided.

FedDP: Dual Personalization in Federated Medical Image Segmentation.

Personalized federated learning (PFL) addresses the data heterogeneity challenge faced by general federated learning (GFL). Rather than learning a single global model, with PFL a collection of models are adapted to the unique feature distribution of each site. However, current PFL methods rarely consider self-attention networks which can handle data heterogeneity by long-range dependency modeling and they do not utilize prediction inconsistencies in local models as an indicator of site uniqueness.

Comparative validation of machine learning algorithms for surgical workflow and skill analysis with the HeiChole benchmark.

Purpose: Surgical workflow and skill analysis are key technologies for the next generation of cognitive surgical assistance systems. These systems could increase the safety of the operation through context-sensitive warnings and semi-autonomous robotic assistance or improve training of surgeons via data-driven feedback. In surgical workflow analysis up to 91% average precision has been reported for phase recognition on an open data single-center video dataset.

Trans-SVNet: hybrid embedding aggregation Transformer for surgical workflow analysis.

Purpose: Real-time surgical workflow analysis has been a key component for computer-assisted intervention system to improve cognitive assistance. Most existing methods solely rely on conventional temporal models and encode features with a successive spatial-temporal arrangement. Supportive benefits of intermediate features are partially lost from both visual and temporal aspects.

Exploring Intra- and Inter-Video Relation for Surgical Semantic Scene Segmentation.

Automatic surgical scene segmentation is fundamental for facilitating cognitive intelligence in the modern operating theatre. Previous works rely on conventional aggregation modules (e.g.

Unsupervised feature disentanglement for video retrieval in minimally invasive surgery.

In this paper, we propose a novel method of Unsupervised Disentanglement of Scene and Motion (UDSM) representations for minimally invasive surgery video retrieval within large databases, which has the potential to advance intelligent and efficient surgical teaching systems. To extract more discriminative video representations, two designed encoders with a triplet ranking loss and an adversarial learning mechanism are established to respectively capture the spatial and temporal information for achieving disentangled features from each frame with promising interpretability. In addition, the long-range temporal dependencies are improved in an integrated video level using a temporal aggregation module and then a set of compact binary codes that carries representative features is yielded to realize fast retrieval.

Real-time landmark detection for precise endoscopic submucosal dissection via shape-aware relation network.

We propose a novel shape-aware relation network for accurate and real-time landmark detection in endoscopic submucosal dissection (ESD) surgery. This task is of great clinical significance but extremely challenging due to bleeding, lighting reflection, and motion blur in the complicated surgical environment. Compared with existing solutions, which either neglect geometric relationships among targeting objects or capture the relationships by using complicated aggregation schemes, the proposed network is capable of achieving satisfactory accuracy while maintaining real-time performance by taking full advantage of the spatial relations among landmarks.

Learning With Privileged Multimodal Knowledge for Unimodal Segmentation.

Multimodal learning usually requires a complete set of modalities during inference to maintain performance. Although training data can be well-prepared with high-quality multiple modalities, in many cases of clinical practice, only one modality can be acquired and important clinical evaluations have to be made based on the limited single modality information. In this work, we propose a privileged knowledge learning framework with the 'Teacher-Student' architecture, in which the complete multimodal knowledge that is only available in the training data (called privileged information) is transferred from a multimodal teacher network to a unimodal student network, via both a pixel-level and an image-level distillation scheme.