IEEE-TMI
@IEEE_TMI
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TMI publishes manuscripts on imaging of body structure, morphology and function, including cell and molecular imaging and all forms of microscopy.
Joined February 2016
🚨 New paper alert! ⚠️ Irregular polyp morphologies make segmentation in endoscopy a real challenge! 💡⬇️The Endoscopic Adaptive Transformer (EAT) uses an adaptive perception module to dynamically capture fine anatomical details and global context! https://t.co/FGPXpvp5iy
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💡Is your work ready for IEEE TMI? A new IEEE TMI editorial by EiC @GeWang92343256 et al. outlines what makes a study truly impactful: ✅Significance ✅Innovation ✅Evaluation ✅Reproducibility We hope it helps you shape your next paper for TMI! 🚀 🔗 https://t.co/kkJKPAnn6B
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🚨New paper alert! 🔬 Traditional HE staining of FFPE slides is slow & costly! 🚀Discover a novel Multiple Cell Semantics-guided supervised generative adversarial model, MCS-Stain, for FFPE→HE virtual staining 🔥It also extends to HE→IHC tasks! https://t.co/LHeQ6eVYUF
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🚨New paper alert! ℹ️Annotating volumetric medical images is slow and labor-intensive ⚠️SAM 2 speeds this up but suffers from error propagation 💡SLM-SAM 2 adds short- & long-term memories to fix this, boosting accuracy and cutting annotation time! 🔗 https://t.co/zmYtFubEhM
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🚨 New paper alert! 🚀Authors developed a sub-0.5 mm resolution PET scanner with optimized 3-layer depth-of-interaction detectors! 🚀Visualization of small mouse brain structures, eg hypothalamus, amygdala, cerebellar nuclei now possible! 🔗 https://t.co/BFWSfirE2f
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🚨New paper alert! 💡Bringing causal reasoning to unsupervised domain adaptation in medical image segmentation! 🚀CiSeg disentangles causal vs. bias features via a Structural Causal Model, boosting cross-domain generalization from CT→MRI! https://t.co/5Mmr2B2U8m
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🚨 New paper alert! 🧠Discover a DL framework that jointly reconstructs white matter, pial and midthickness cortical surfaces via coupled diffeomorphic deformat° 🚀Result: fast, topology-preserving reconstruct° and cortical thickness estimat° from MRI! https://t.co/oqyWyBm16s
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🚨New paper alert! ⚠️Fairness in healthcare AI remains a challenge, esp in Federated Learning (FL)! 🚀 FairFedMed is the 1st benchmark for fairness in medical FL 🚀FairLoRA: a fairness-aware FL method that boosts ACC and equity across demographic grp https://t.co/NKxBzyOI3N
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🚨New paper alert! 🚀Discover EviVLM, that bridges the modality gap in medical image segmentation by combining Evidential Learning with Vision-Language Models! 💡It quantifies cross-modal uncertainty using subjective logic + Dempster-Shafer theory 🔗 https://t.co/5PY1BjXkBU
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🚨 New paper alert! ⚠️Sharing medical data threatens patient privacy 🚀Discover a latent diffusion approach to generate synthetic CT, MRI & PET data that’s privacy-safe yet realistic! Networks trained on the synthetic data match performance on real data https://t.co/0E9wEIOM3e
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🚨New Paper Alert! 🚀Discover Ceb, a novel boundary-centric framework for cell instance segmentation! Boundary modeling allows Ceb to capture geometric properties such as shape, curvature, and convexity, which leads to more accurate segmentation results https://t.co/XqGIZwxShH
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🚨 New paper alert! 🧠 Head motion during brain PET scans causes artifacts & quantitative errors but hardware tracking isn’t always feasible! 🔥 Discover DL-HMC++: a deep learning model that predicts motion directly from PET raw data! 🔗 https://t.co/GS5v4z80NQ
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🚨 New paper alert! 🚀A new framework for few-shot vascular image segmentation! 🚀It uses limited labeled data to generate vascular structures via a joint training strategy where the generator and segmentation model learn collaboratively! 🔗 https://t.co/6wBMqpCLxE
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🚨 New paper alert that tackles data scarcity in medical imaging! 🚀Instead of relying on synthetic data with poor generalization, this few-shot framework learns to generate realistic vascular images + segment them jointly via Dual-Consistency Learning. https://t.co/6wBMqpDjnc
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🚨 New paper alert! ⚠️Cell tracking in PET needs ground-truth data 🚀 New framework CeFloPS delivers physiologically realistic simulations of cell flow & tissue uptake, generating realistic PET data to validate next-gen cell-tracking algorithms! 🔗 https://t.co/cB7tlNvMj8
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🚨 New paper alert! Discover MetaSSL, a plug-and-play framework for semi-supervised learning in med image segmentat°! Main idea= to mine rich information from prediction uncertainty and consistency, applying a loss that adapts to pixel confidence levels https://t.co/RqtcbJ7WQV
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🚨New paper alert! In pathology, multiplex IHC (mIHC) allows simultaneous labeling of multiple protein biomarkers on the same tissue section 🚀Discover a novel annotation-free, self-supervised deep learning method for multiplexed color deconvolution! https://t.co/ufihrfxZ91
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🚨New paper alert! 🚀A new AI model, MultiASNet, boosts aortic stenosis screening using POCUS (portable ultrasound) + structured data - making early detection faster, easier, and more accurate for all clinicians! #MultimodalLearning #ContrastiveLearning
https://t.co/K9Dwepeb5x
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✨TMI statistics are out!💫 🔥Discover TMI’s latest impact factor, average time from submission to first decision, and more! https://t.co/3z644LPrx4 While we are discussing numbers, we’re also grateful to our 2,395 followers 😊 Don't forget you can also follow us on Linkedin!
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🚨 New paper alert! ⚠️Traditional video summarizat° struggles with minimally invasive surgery videos due to complex scenes! 🔥Explore a new dynamic framework using multitask learning, multi-objective optimization→informative & diverse video summaries! https://t.co/BvuoketCwy
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