Biomedical Image Analysis Scientist
Aganitha Cognitive Solutions
We combine deep science and advanced AI to accelerate discovery in biopharma and material sciences.
See the full job description on jobRxiv: https://jobrxiv.org/job/aganitha-cognitive-solutions-27778-biomedical-image-analysis-scientist-2/
#AI #atomicforcemicroscopy #Biomedicalimaging #deeplearning #histopathology #r...
https://jobrxiv.org/job/aganitha-cognitive-solutions-27778-biomedical-image-analysis-scientist-2/?fsp_sid=4638
🌌 Could we see cancer differently—without staining, just by its optical fingerprint?
🔗 Phasor-FLIM and SHG imaging for quantitative analysis of lung cancer autofluorescence. DOI: https://doi.org/10.1016/j.csbj.2025.08.010
📚 CSBJ Quantum Biology and Biophotonics: https://www.csbj.org/qbio
#CancerResearch #LungCancer #Biophotonics #Autofluorescence #Histopathology #PrecisionDiagnostics #BiomedicalOptics #MedicalImaging #QuantitativePathology #ComputationalBiology #CancerDiagnostics #HealthTech @csbj
Atlas of Pathology with Whole Slide Images. Histopathology Atlas and Notes for Medical Students. Atlas of Histopathology. Notes for Pathology. Virtual Microscope. Virtual Microscopy. Online Pathology Atlas. Pathology Lecture Notes and Histopathology Atlas is being prepared from Memorial Pathology Archive and collaborators from other institutions.
Atlas of Pathology with Whole Slide Images. Histopathology Atlas and Notes for Medical Students. Atlas of Histopathology. Notes for Pathology. Virtual Microscope. Virtual Microscopy. Online Pathology Atlas. Pathology Lecture Notes and Histopathology Atlas is being prepared from Memorial Pathology Archive and collaborators from other institutions.
We have a little #EyePath case report out today: Recurrence of a non-AIDS-related #eyelid Kaposi sarcoma.
FREE copies available from https://authors.elsevier.com/c/1j1dqfHwmDnG6 until mid-June.
If you're interested, pick it up while you can.
Feel free to share with any colleagues who might be interested 😉
#Ophthalmology #Histopathology #MedMastodon #DermPath @pathology
Time for some more #EyePath! We'll look at some old and odd cases from my personal "interest" collection. Dates and sign-up links are:
Monday 12 February 1900h (GMT/UTC)
https://us06web.zoom.us/meeting/register/tZMlceiorj0rEtXNNzMSehjig_t7DogXgD83
Tuesday 13 February 1000h (GMT/UTC)
https://us06web.zoom.us/meeting/register/tZIlduuopj0pGtXlsh6RfeMNO6-nXYCS-hSj
#MedMastodon #MedEd #Histopathology #Ophthalmology @pathology
Welcome! You are invited to join a meeting: Eye pathology Monday evening. After registering, you will receive a confirmation email about joining the meeting.
"AI pioneer Daphne Koller sees generative AI leading to cancer breakthroughs"
AI, or rather machine learning, has great potential in the analysis of all kinds of science data. We are now producing such huge volumes of raw information that human researchers need more help in filtering and pattern recognition.
#science #AI #MachineLearning #research #cancer #histopathology
Ophthalmic manifestations and tissue tropism of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have been reported in association with coronavirus disease 2019 (COVID-19), but the pathology and cellular localization of SARS-CoV-2 are not well characterized. The objective of this study was to evaluate macroscopic and microscopic changes and investigate cellular localization of SARS-CoV-2 across ocular tissues at autopsy. Ocular tissues were obtained from 25 patients with COVID-19 at autopsy.
Iris Lähdeniemi and Jennifer Devlin develop a reproducible and rapid tumour-lung colonisation model to study an aggressive subset of non-small cell lung #cancer (NSCLC):
#NSCLC #Carcinoma #Histopathology #Metastasis #PreclinicalModel #Science #BiologyOpen #Biology #AcademicMastodon
#SelfSupervision with ~10k parameters & < 10 min training?
Check out our latest work "#Efficient Self-Supervision using Patch-based Contrastive Learning for #Histopathology #Image #Segmentation", to be presented at the #NorthernLights #DeepLearning Conference this week.
The first author Nicklas Boserup, who is currently a 1st year's MSc student from UCPH
, will give an oral presentation at #NLDL this week.
Paper: https://arxiv.org/abs/2208.10779
Code: https://github.com/nickeopti/bach-contrastive-segmentation
Learning discriminative representations of unlabelled data is a challenging task. Contrastive self-supervised learning provides a framework to learn meaningful representations using learned notions of similarity measures from simple pretext tasks. In this work, we propose a simple and efficient framework for self-supervised image segmentation using contrastive learning on image patches, without using explicit pretext tasks or any further labeled fine-tuning. A fully convolutional neural network (FCNN) is trained in a self-supervised manner to discern features in the input images and obtain confidence maps which capture the network's belief about the objects belonging to the same class. Positive- and negative- patches are sampled based on the average entropy in the confidence maps for contrastive learning. Convergence is assumed when the information separation between the positive patches is small, and the positive-negative pairs is large. The proposed model only consists of a simple FCNN with 10.8k parameters and requires about 5 minutes to converge on the high resolution microscopy datasets, which is orders of magnitude smaller than the relevant self-supervised methods to attain similar performance. We evaluate the proposed method for the task of segmenting nuclei from two histopathology datasets, and show comparable performance with relevant self-supervised and supervised methods.
jobRxiv
CSBJ
Serdar Balci
Serdar Balcı
MH Thaung
hesgen
Giuseppe Michieli
Biology Open
Raghav{endra}| ರಾಘವೇಂದ್ರ