Logo Lanfrica
  • Home
  • Atlas
  • Insights
  • Docs
  • Sign in

© 2026 Lanfrica. All rights reserved. All copyrights of the resources shown on the Lanfrica website belong to the original copyright holders, unless explicitly stated otherwise.

NCI-UNC Lymphoma models trained on samples from a clinic in Malawi

Domain:

healthcare

Record type:

model
Creator:
Bro
Publisher:
Zenodo
Host:avatar

This is file for the model weights to use for inference on lymphoma WSI that were tained on samples from Malawi. There is no training script or code, because this model was generated in the commerical platform HALO by Indica Labs. A custom Densenet was trained and then exported out using ONNX. You can find code regarding using foundation models on the github page for this project: github.com

Visit

doi.org

Tasks

computer visionimage classification

Licenses

info:eu-repo/semantics/openAccessCreative Commons Attribution 4.0 Internationalhttps://creativecommons.org/licenses/by/4.0/legalcode

Similar

NWR_YOLO_v1: a YOLOv4 multiclass detector model trained on camera trap images from Nkhotakota Wildlife Reserve, MalawiPytorch Language Models trained on South African LanguagesPerformance of trained models.LAWDR: Language-Agnostic Weighted Document Representations from Pre-trained ModelsAnalyzing Acoustic Word Embeddings from Pre-trained Self-supervised ModelsAnalyzing Acoustic Word Embeddings from Pre-trained Self-supervised Speech Models

NWR_YOLO_v1: a YOLOv4 multiclass detector model trained on camera trap images from Nkhotakota Wildlife Reserve, Malawi

For implementation of the model and additional citation information, please see: https://github.com/

Pytorch Language Models trained on South African Languages

Pytorch Language Models trained on the South African isiZulu and Sepedi languages using the NCHLT an

Performance of trained models.

Ensuring complete utilization of maternal continuum of care is essential for reducing matern

LAWDR: Language-Agnostic Weighted Document Representations from Pre-trained Models

Cross-lingual document representations enable language understanding in multilingual contexts and al

Analyzing Acoustic Word Embeddings from Pre-trained Self-supervised Models

IEEE ICASSP 2023 Conference, Hybrid Event, 4-10 June 2023, Rhodes Island, Greece Given the strong re

Analyzing Acoustic Word Embeddings from Pre-trained Self-supervised Speech Models

Given the strong results of self-supervised models on various tasks, there have been surprisingly fe