Pith. sign in

REVIEW 3 cited by

A Survey on Deep Learning in Medical Image Analysis

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 1702.05747 v2 pith:HEQ77IKG submitted 2017-02-19 cs.CV

Geert Litjens , Thijs Kooi , Babak Ehteshami Bejnordi , Arnaud Arindra Adiyoso Setio , Francesco Ciompi , Mohsen Ghafoorian , Jeroen A.W.M. van der Laak , Bram van Ginneken
show 1 more author
Clara I. Sánchez
This is my paper
classification cs.CV
keywords deeplearningimagemedicalanalysissurveyalgorithmsanalyzing
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.

Discussion (0). Sign in to comment.

Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Curia: A Multi-Modal Foundation Model for Radiology

    cs.CV 2025-09 conditional novelty 6.0 of 10

    A DINOv2 vision transformer pre-trained on 200M unlabeled CT/MRI slices matches or beats prior foundation models and radiology residents on a new 19-task benchmark.

  2. Harmonizing MR Images Across 100+ Scanners: Multi-site Validation with Traveling Subjects and Real-world Protocols

    eess.IV 2026-04 conditional novelty 5.0 of 10

    HACA3^+ improves upon HACA3 with better artifact encoding, attention mechanisms, and training on 100+ scanners, validated via traveling subjects for better downstream performance.

  3. XAI-Guided Analysis of Residual Networks for Interpretable Pneumonia Detection in Paediatric Chest X-rays

    eess.IV 2025-07 conditional novelty 3.0 of 10

    A fine-tuned ResNet-50 with Grad-CAM and Monte Carlo dropout reports 95.94% accuracy and 98.91% AUC for pediatric pneumonia on the Kermany chest X-ray dataset.

Pith tools